Italy

In this paper, we present the results of an empirical evaluation investigating how recommendation algorithms are affected by popularity bias. Popularity bias makes more popular items to be recommended more frequently than less popular ones, thus it is one of the most relevant issues that limits the fairness of recommender systems. In particular, we define an experimental protocol based on two state-of-theart datasets containing users’ preferences on movies and books and three different recommendation paradigms, i.e., collaborative filtering, content-based filtering and graph-based algorithms. In order to evaluate the overall fairness of the recommendations we use well-known metrics such as Catalogue Coverage, Gini Index and Group Average Popularity (ΔGAP). The goal of this paper is: (i) to provide a clear picture of how recommendation techniques are affected by popularity bias; (ii) to trigger further research in the area aimed to introduce methods to mitigate or reduce biases in order to provide fairer recommendations.

AIxIA 2021

Fairness and Popularity Bias in Recommender Systems: an Empirical Evaluation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

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The certificate will be provided by December 17. 
In case you need it urgently please contact: francesca.gasparini@unimib.it. 

 

The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

On the Impact of Social Media Recommendations on Opinion Consensus

Misogynous MEME Recognition: A Preliminary Study

Symbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of - not necessarily easily obtained - data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of trainable artificial neural networks. In this talk we will present and discuss recent advances made on this topic, and concrete pointers to open research questions.

Neuro-Symbolic Deep Deductive Reasoning

The language system in the human brain

An automated and data-driven rules extraction is crucial for the construction of Fuzzy Inference Systems (FIS). This work presents a method for extracting fuzzy rules based on clustering and association mining through the FP-growth algorithm. First, Self Organizing Maps are used to identify subsets of elements with similar characteristics, separately for each class. Then, the FP-Growth algorithm is applied to each cluster. Elements matching each rule are subdivided in the corresponding classes and only rules showing a predominance of elements belonging to one class are used as fuzzy rules. The method was applied to the construction of a Decision Support System based on FIS for the target prostate biopsy outcome prediction based on six pre-bioptic variables. A dataset containing 1447 patients (824 with positive outcome, 623 with negative outcome) was used. Four and six clusters were identified for the positive and the negative class, respectively. A total of 151 rules were extracted with FP-Growth algorithm and 29 were included in the FIS. The system was able to classify 927 patients out of 1447. On the classi-fied subjects, it reached a sensitivity of 87.5% and a specificity of 58.8%.

Decision Support System for target prostate biopsy outcome prediction: clustering and FP-growth algorithm for fuzzy rules extraction

Cancer patients may struggle with mental wellbeing issues such as distress and depression. As a part of the CAPABLE project, we aim to develop a digital behaviour-change intervention that helps them build positive health habits and improve their wellbeing. The main challenge to the evaluation of the system is the lack of access to real data prior to intervention start. Therefore, first, we created a simulator that mimics patient responses to activity suggestions based on Fogg’s behaviour model. Later we used supervised and reinforcement learning methods to learn the best time of sending the patient prompts. We found that the reinforcement learning methods learn quickly not to over-notify patients and find prompt policies that are more effective in facilitating users in performing target activity than a random notification strategy, but are less effective than adaptive supervised learning method trained to predict patient responsiveness.

From Personalized Timely Notification to Healthy Habit Formation: A Feasibility Study of Reinforcement Learning Approaches on Synthetic Data

We aim to develop a virtual coaching (VC) system which facilitates cancer patients with the formation of positive health habits. The VC should adjust content and delivery of recommendations based on user’s preferences. We conducted a survey that elicited potential VC users’ perceptions about the appropriate context for notification, aspects driving their motivation, and their ability to perform a selected activity in different circumstances. We analyzed 173 survey responses, identified different types of users and formulated suggestions how to adjust application content and notification timing to increase their susceptibility to non-medication health interventions.

Personality and habit formation: Is there a link?

AI applications to health related processes include the adoption of robotic platforms for rehabilitation, aiming at the delivery of highly intensive, repeatable and accurate motion therapies and able to constantly monitor the patient and provide the suitable assistance levels. However, a comprehensive approach to robot-aided rehabilitation requires also a social level of interaction with the patient that implies cognitive modeling and linguistic communication. It is worth noticing that robotic platforms providing both physical and cognitive support to patients have not been proposed so far. In the HeAL9000 project an intelligent robot for rehabilitation of patients affected by musculoskeletal disorders is proposed with cognitive and linguistic abilities. It relies strongly on machine learning technologies whose aim is to support cost effective engineering of the platform as well as evolving capabilities across time. In the paper early experimental evidence is acquired through quantitative evaluation.

HeAL9000: an Intelligent Rehabilitation Robot

In this paper we propose an early stage decision support system for personalized arteriovenous fistula (AVF) management. The goal of the model is to identify an optimal strategy to recognize the onset of a stenosis and intervene to prevent the failure of the AVF. We used an Influence Diagram (ID) that combines a risk model, clinical tests, angioplasty and searches a series of policies that optimizes the cost of treatment.

Personalized arterovenous fistula management through utility maximization with Influence Diagram

Scientific progress is offering increasingly better ways to tailor a patient’s treatment to the patient’s needs, i.e., better support for optimal clinical decision-making can be offered. Choosing the appropriate treatment for a patient depends on numerous factors, including pathology results, tumor stage, genetic, and molecular characteristics. Bayesian networks are a type of probabilistic artificial intelligence, which in principle would be suitable to support complex clinical decision-making. However, most clinicians do not have experience with these networks. This paper describes an approach of developing a clinical decision support system based on Bayesian networks, that does not require insight knowledge about the underlying computational model for its use. It is developed as a therapy-oriented approach with a focus on usability and explainability. The approach features the computation and presentation of individualized treatment recommendations, comparison of treatments and patient cases, as well as explanations and visualizations providing additional information on the current patient case.

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