Italy

AIxIA 2021

Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

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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.

 
 



**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.

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper

Sparse-reward environments are famously challenging for deep reinforcement learning (DRL) algorithms. Yet, the prospect of solving intrinsically sparse tasks in an end-to-end fashion without any extra reward engineering is highly appealing. Such aspiration has recently led to the development of numerous DRL algorithms able to handle sparse-reward environments to some extent. Some methods have even gone one step further and have tackled sparse tasks involving different kinds of distractors (e.g. broken TV, self-moving phantom objects and many more). In this work, we put forward two motivating new sparse-reward environments containing the so-far largely overlooked class of exploration-intensive distractors. Furthermore, we conduct a benchmarking which reveals that state-of-the-art algorithms are not yet all-around suitable for solving the proposed environments.

Exploration-Intensive Distractors: Two Environment Proposals and a Benchmarking

Virtual Power Plants (VPP) are one of the main components of future smart electrical grids, connecting and integrating several types of energy sources, loads and storage devices. A typical VPP is a large industrial plant with high (partially shiftable) electric and thermal loads, renewable energy generators and electric and thermal storages. Optimizing the use and the cost of energy could lead to a significant economic impact. This work proposes a VPP Energy Management System (EMS), based on a two-step optimization model that decides the minimum-cost energy balance at each point in time considering the following data: electrical load, photovoltaic production, electricity costs, upper and lower limits for generating units and storage units. The first (day-ahead) step models the prediction uncertainty using a robust approach defining scenarios to optimize the load demand shift and to estimate the cost. The second step is an online optimization algorithm, implemented within a simulator, that uses the optimal shifts produced by the previous step to minimize, for each timestamp, the real cost while fully covering the optimally shifted energy demand. The system is implemented and tested using real data and we provide analysis of results and comparison between real and estimated optimal costs.

Robust optimization models for local flexibility characterization of Virtual Power Plants

Knowledge-based neural pre-training for Intelligent Document Management

Artificial Intelligence solutions are empowering many fields of knowledge, including art. Indeed, the growing availability of large collections of digitized artworks, coupled with recent advances in Pattern Recognition and Computer Vision, offer new opportunities for researchers in these fields to help the art community with automatic and intelligent support tools. In this discussion paper, we outline some research directions that we are exploring to contribute to the challenge of understanding art with AI. Specifically, our current research is primarily concerned with visual link retrieval, artwork clustering, integrating new features based on contextual information encoded in a knowledge graph, and implementing these methods on social robots to provide new engaging user interfaces. The application of Information Technology to fine arts has countless applications, the most important of which concerns the preservation and fruition of our cultural heritage, which has been severely penalized, along with other sectors, by the ongoing COVID pandemic. On the other hand, the artistic domain poses entirely new challenges to the traditional ones, which, if addressed, can push the limits of current methods to achieve better semantic scene understanding.

Understanding Art with AI: Our Research Experience

Respecting and caring for the environment is an aspect that is becoming more important with each passing year. The damage caused by human beings to the planet has triggered a global climate change, favoring the creation of major weather phenomena, increasing the temperatures, and changing the rainfall cycles, which in turn make access to water more complicated. The effects mentioned above are only a part of the true impact of Climate Change, and taking this into account, the European Union has established the European Directive 20-20-20 in an attempt to try to slow it down and mitigate its impact on the planet. In this regard, Italy suffers greatly from water wastage due to leaks in the supply system and a practically non-existent culture of saving water. To address this problem, in this paper, we propose a system based on the Internet of Things (IoT) and Artificial Intelligence (AI) to monitor water consumption in real-time, as well as to detect and prevent possible failures in the water supply system, allowing to save water and economic resources due to maintenance. We evaluate the potential of our system by testing it in a real environment in the province of Catanzaro.

Hydrocontrol IT: an attempt to mitigate water losses in Italy towns

Scientific research implies the production of data describing phenomena still not studied and well understood. Sometimes the amount and rate of generation of produced data can be overwhelming, and anyway tools supporting a computer assisted analysis of scientific data can support systematic forms of data driven analysis. Machine learning can be an instrument in an overall flow including domain experts and computer scientists. Adopted machine learning approaches need to be unsupervised, employing just the input data as a teacher. We propose a two-step workflow: (i) achieving a compact representation of elements of the dataset by means of representation learning techniques, shifting the analysis from cumbersome representations to compact vectors in a latent space, and (ii) clustering points associated to instances to suggest patterns to the domain experts that will evaluate their potential meaning within the domain. The paper presents the rationale of the approach within a cloud based setting, and first experiments on an image dataset from the literature.

Unsupervised Data Pattern Discovery on the Cloud

Convolutional neural networks (CNNs) have become commonplace in addressing major challenges in computer vision. Researchers are not only coming up with new CNN architectures but are also researching different techniques to improve the performance of existing architectures. However, there is a tendency to over-emphasize performance improvement while neglecting certain important variables such as simplicity, versatility, the fairness of comparisons, and energy efficiency. Overlooking these variables in architectural design and evaluation has led to research bias and a significantly negative environmental impact. Furthermore, this can undermine the positive impact of research in using deep learning models to tackle climate change. Here, we perform an extensive and fair empirical study of a number of proposed techniques to gauge the utility of each technique for segmentation and classification. Our findings restate the importance of favoring simplicity over complexity in model design (Occam's Razor). Furthermore, our results indicate that simple standardized practices can lead to a significant reduction in environmental impact with little drop in performance. We highlight that there is a need to rethink the design and evaluation of CNNs to alleviate the issue of research bias and carbon emissions.

Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs

Generating textual explanations for CNNs using knowledge graphs

The recent success of deep learning models in solving complex problems and in different domains has increased interest in understanding what they learn. Therefore, different approaches have been employed to explain these models, one of which uses human-understandable concepts as explanations. Two examples of methods that use this approach are Network Dissection and Compositional explanations. The former explains units using atomic concepts, while the latter makes explanations more expressive, replacing atomic concepts with logical forms. While intuitively, logical forms are more informative than atomic concepts, it is not clear how to quantify this improvement, and their evaluation is often based on the same metric that is optimized during the search-process and on the usage of hyper-parameters to be tuned. In this paper, we propose to use as evaluation metric the Detection Accuracy, which measures units' consistency of detection of their assigned explanations. We show that this metric (1) evaluates explanations of different lengths effectively, (2) can be used as a stopping criterion for the compositional explanation search, eliminating the explanation length hyper-parameter, and (3) exposes new specialized units whose length 1 explanations are the perceptual abstractions of their longer explanations.

Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

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Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

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Next from AIxIA 2021

Exploration-Intensive Distractors: Two Environment Proposals and a Benchmarking

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T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis

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PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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