Using Inductive Logic Programming to globally approximate Neural Networks for preference learning: challenges and preliminary results

In this paper we explore the use of Answer Set Programming (ASP), and in particular the state-of-the-art Inductive Logic Programming (ILP) system ILASP, as a method to explain black-box models, e.g. Neural Networks (NN), when they are used to learn user preferences. To this aim, we created a dataset of users preferences over a set of recipes, trained a set of NNs on these data, and performed preliminary experiments that investigate how ILASP can globally approximate these NNs. Since computational time required for training ILASP on high dimensional feature spaces is very high, we focused on the problem of making global approximation more scalable. In particular we experimented with the use of Principal Component Analysis (PCA) to reduce the dimensionality of the dataset while trying to keep our explanations transparent

Towards an Inductive Logic Programming Approach for Explaining Black-Box Preference Learning Systems

In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas

An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments

Integrated Project 2: App Development and Education

In the second integrated project, a prototype of a tablet-based application was developed to promote early vocabulary learning among migrant preschool-aged children. Across Europe, half of the population is either multilingual or lives in a multicultural and multilingual environment. Meanwhile, a portion of children do not speak the language of school instruction at home. Insufficient language skills in the early years can potentially have a far-reaching and long-lasting impact on children’s educational trajectory. The goal of this interdisciplinary project was to address this issue by developing the prototype of a vocabulary learning app, following the latest literature regarding vocabulary learning and design principles to design apps for second language acquisition (L2)

Early Language Development in the Digital Age (e-LADDA)

Modern digital technologies are transforming rapidly the environment in which children are growing up and developing skills. This new digital reality has both changed the nature of the linguistic input provided to young children, but also affords new ways of interaction with communication agents, such as tablets and robots. The goal of e-LADDA is to establish whether the new and intuitive interactions afforded by digital tools impact on young children’s language development and language outcomes in a positive or adverse way. We further aim to identify exactly what factors in both the technology itself and the communication channel advance language learning and growth or may impede it. This goal will be pursued in e-LADDA from a highly interdisciplinary and cross-sectorial perspective, bridging between research disciplines and methodologies and in collaboration with industry and the non-academic public sector

GRAd-COV2, a gorilla adenovirus-based candidate vaccine against COVID-19, is safe and immunogenic in younger and older adults

International audienceSafe and effective vaccines against coronavirus disease 2019 (COVID-19) are essential for ending the ongoing pandemic. Although impressive progress has been made with several COVID-19 vaccines already approved, it is clear that those developed so far cannot meet the global vaccine demand alone. We describe a COVID-19 vaccine based on a replication-defective gorilla adenovirus expressing the stabilized prefusion severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein named GRAd-COV2. We assessed the safety and immunogenicity of a single-dose regimen of this vaccine in healthy younger and older adults to select the appropriate dose for each age group. For this purpose, a phase 1, dose-escalation, open-labeled trial was conducted including 90 healthy participants (45 aged 18 to 55 years old and 45 aged 65 to 85 years old) who received a single intramuscular administration of GRAd-COV2 at three escalating doses. Local and systemic adverse reactions were mostly mild or moderate and of short duration, and no serious adverse events were reported. Four weeks after vaccination, seroconversion to spike protein and receptor binding domain was achieved in 43 of 44 young volunteers and in 45 of 45 older participants. Consistently, neutralizing antibodies were detected in 42 of 44 younger-age and 45 of 45 older-age volunteers. In addition, GRAd-COV2 induced a robust and T helper 1 cell (T H 1)-skewed T cell response against the spike protein in 89 of 90 participants from both age groups. Overall, the safety and immunogenicity data from the phase 1 trial support the further development of this vaccine