The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 12th International Workshop on Theorem Proving Components for Educational Software(ThEdu'23), was a satellite event of the 29th international Conference on Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy. ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions. An open call for papers was then issued, to which eight contributions were submitted. Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving based software, and that it will allow to improve the mutual understanding between computer scientists, mathematicians and stakeholders in education. PC Chairs:Julien Narboux (University of Strasbourg, France); Walther Neuper (JKU, Johannes Kepler University, Linz, Austria); Pedro Quaresma (University of Coimbra, Portugal)

"When in Rome, do as the Romans do," the proverb says. But what if you're only in the Italian capital for just one day and you're keen to fit in as much of its history and culture as possible? Sure, you could take a few hours out to plan your trip or you could try to book a tour guide to take you round "The Eternal City." But now there's a third option: Tourism apps, websites and chatbots that use artificial intelligence to tailor itineraries for the user based on their preferences and time. They're rapidly popping up, so NBC News decided to put three of them to the test.

Spectral Indices (SIs) are mathematical equations applied to each pixel image to highlight a specific phenomenon on the ground. Most SIs are computed from the reflectance data produced after some pre-processing stages of multispectral remote sensing images. In which bx and by are the reflectance values of a pixel in bands x and y. If we calculate the value of a SI for each pixel, we can generate an image from SI. In this post, I want to talk about the two most important spectral indices and how to calculate them for a case study in the center of Rome, Italy, using the Sentinel-hub cloud platform.

In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.

Medical Imaging 2019 is an addition to the successful series of Medical Imaging and Clinical Research conferences; it is with immense pleasure and pride that we announce our upcoming "5th World Congress on Medical Imaging and Clinical Research" during June 17-18th, 2019 at Rome, Italy. Medical imaging is a technical process which creates Visual representation of interior body for clinical analysis and medical intervention, as well as visual representation of the function of some organs. Medical imaging seeks to reveal internal structures hidden by the skin and bones. Medical imaging is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. Medical imaging also diagnoses and treats disease.

This is the motto of Euklid, the fintech startup which aims to disrupt the investment banking arena leveraging the potential of the blockchain and artificial intelligence. Thanks to a sound technical background of its Rome, Italy-based team led by CEO & Algo-Trader Antonio Simeone, Algo-Trader Franco Grassi, CTO Francesco Di Leva and lead programmer Mario Giancola, Euklid is pioonering a platform that allows people to make investments based on sophisticated algos, which analyze financial markets with no human analysis. In details, the technology combines elements of bio-computing, soft computing and evolutionary computation to offer a Long-Short Equity Fund, which aims to outperform the market while maintaining a low level of risk thanks to a diversified portfolio including over 70 among the most liquid assets between indexes and stocks. It also performs algo-trading on cryptoassets including Bitcoin and Ethereum. Simeone told me that indexes and stocks have seen a 10% increase yoy (max drawdown 1.5%) while Bitcoin have seen a massive growth (109% yoy).

Fighting cyber attacks with artificial intelligence The next frontier of anti-virus software is leveraging artificial intelligence (AI) to not only predict what threats are out there, but to also actively fight back before they strike. This is according to American-based Cylance's chief marketing officer, Greg Fitzgerald, speaking at the NetEvents Press and Analyst Summit in Rome, Italy.The company says it is "revolutionising cyber security through the use of AI and machine learning to proactively prevent advanced persistent threats and malware". Cylance today announced it is expanding into the Europe, the Middle East and Africa (EMEA) with the establishment of a London-based team led by Evan Davidson, former enterprise sales director at FireEye. It also established a channel partnership with CoreSec Systems, which supplies cyber security and networking solutions in Sweden and Denmark.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. BIOSTEC 2016 will be held 21-23 February, 2016, in Third AAAI Conference on Human 15th International Conference on Rome, Italy Computation and Crowdsourcing. HCOMP 2015 will be held November and Reasoning (KR 2016) 8-11 in San Diego, California. ICAART 2016 will be held 24-26 February, AAAI Fall Symposium.

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule.

Organizing committee for the  Cognitive Robotics Workshop held at AAAI-12.