Ricerc@Sapienza

OBJECTIVES: We aimed to apply deep learning to detect white spot lesions in dental photographs.METHODS: Using 434 photographic images of 51 patients, a dataset of 2781 cropped tooth segments was generated. Pixelwise annotations of sound enamel as well as fluorotic, carious or other types of hypomineralized lesions were generated by experts and assessed by an independent second reviewer. The union of the reviewed annotations were used to segment the hard tissues (region-of-interest, ROI) of each image. SqueezeNet was employed for modelling.

In official statistics, interest for data integration has been increasingly growing, due to the need of extracting information from different sources. However, the effects of these procedures on the validity of the resulting statistical analyses has been disregarded for a long time. In recent years, it has been largely recognized that linkage is not an error-free procedure and linkage errors, as false links and/or missed links, can invalidate the reliability of estimates in standard statistical models.

Recent literature from the stream of the Viable Systems Approach (vSa) highlighs the need to shift from the concept of Artificial Intelligence (AI) to that of Intelligence Augmentation (IA) in complex decision making. IA is defined as an intelligence given by the integration and interaction between wise people and AI entities. More specifically, in the interpretative framework of the vSa Information Variety Model (IVM), IA qualifies the ability to approach a problem by changing the system’s Information Variety.

Among the construction methods developed for tunneling, mechanized excavation by Tunnel Boring Machines (TBMs) is currently considered a preferred option for technical and safety reasons in an urban environment, where damage induced on pre-existing building and services should be minimized.

Visual search of relevant targets in the environment is a crucial robot skill. We propose a preliminary framework for the execution monitor of a robot task, taking care of the robot attitude to visually searching the environment for targets involved in the task. Visual search is also relevant to recover from a failure. The framework exploits deep reinforcement learning to acquire a common sense scene structure and it takes advantage of a deep convolutional network to detect objects and relevant relations holding between them.

We look at program synthesis where the aim is to automatically synthesize a controller that operates on data structures and from which a concrete program can be easily derived. We do not aim at a fully-automatic process or tool that produces a program meeting a given specification of the program’s behaviour. Rather, we aim at the design of a clear and well- founded approach for supporting programmers at the design and implementation phases. Concretely, we first show that a program synthesis task can be modeled as a generalized planning problem.

Precision agriculture is gaining increasing attention because of the possible reduction of agricultural inputs (e.g., fertilizers and pesticides) that can be obtained by using hightech equipment, including robots. In this paper, we focus on an agricultural robotics system that addresses the weeding problem by means of selective spraying or mechanical removal of the detected weeds. In particular, we describe a deep learning based method to allow a robot to perform an accurate weed/crop classification using a sequence of two Convolutional Neural Networks (CNNs) applied to RGB images.

The world population currently counts more of 617 million people over 65 years old. COVID-19 has exposed this population group to new restrictions, leading to new difficulties in care and assistance by family members. New technologies can reduce the degree of isolation of these people, helping them in the execution of healthy activities such as performing periodic sports routines. NAO robots find in this a possible application; being able to alternate voice commands and execution of movements, they can guide elderly people in performing gymnastic exercises.

Proceedings of ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020)

Nondeterministic strategies are strategies (or protocols, or plans) that, given a history in a game, assign a set of possible actions, all of which are winning. An important problem is that of refining such strategies. For instance, given a nondeterministic strategy that allows only safe executions, refine it to, additionally, eventually reach a desired state of affairs. We show that strategic problems involving strategy refinement can be solved elegantly in the framework of Strategy Logic (SL), a very expressive logic to reason about strategic abilities.