Abstract: The new uses of affective social robots, conversational agents, and the so-called “intelligent” systems, in fields as diverse as health, education or transport reflect a phase of significant change in human-machine relations which should receive great attention. How will Human co-learn, co-create and co-adapt with the Machine? Notably, how will vulnerable people will be protected against potential threats from the machine? The first results from an original pre-experiment, conducted by the proposed Chair’s team in June 2019 in partnership with an elementary school, shows that an AI machine (Pepper robot or Google Home) is more efficient at nudging than adults. HUMAAINE’s aim is to study these interactions and relationships, in order to audit and measure the potential influence of affective systems on humans, and finally to go towards a conception of "ethical systems by design" and to propose evaluation measures. The planned scientific work focuses on the detection of social emotions in human voice, and on the study of audio and spoken language manipulations (nudges), intended to induce changes in the behavior of the human interlocutor. This work also uses the contributions of behavioral economics highlighted by the recent 2017 Nobel Laureate Richard Thaler, in our case, applied to human-machine interactions. The roap map for the Chair’s work includes experimental studies to evaluate ethical aspects and confidence in the Human-Machine couple, as well as by demystification of these technologies among the general public which naturally tends towards anthropomorphism. This project combines the scientific research in artificial intelligence with the implementation of an innovative methodology to evaluate and improve the ethics of the HM affective interaction, despite the current opacity of the AI systems. This project pushes forward a strong interdisciplinary collaboration already existing between affective computing, behavioral economics, linguistics, and natural language processing. The researchers will disseminate the results of the chair through their Master courses and workshops with Collège des Bernardins. HUMAAINE is supported by CNRS-LIMSI, Cognition Institute (Institut Carnot), and by the future of society (Harvard Initiative for Learning and Teaching), ENSC (Bordeaux) and presents great synergy with DATAIA Institute. The Domicile foundation, IRCEM and the Collège des Bernardins are the first to support HUMAAINE, followed by industials (CareCever, Renault, etc.) and other foundations (MAIF, Anne de Gaulle). We are also building cooperation with international research teams with Japan (Osaka Univ.), Germany (DFKI), and also Canada (Observatory/MILA).

From the original idea to the actual distribution on TV, VOD and DVD, the production process and subsequent “life” of a TV program are divided into numerous stages, involving numerous actors (e.g. distribution, production, direction, screenwriting, casting, post-production or dubbing) and thus leading to the generation of a huge amount of heterogeneous metadata. However, only a few metadata eventually survive the tortuous production and distribution processes, making their integration difficult into novel TV-centric products. Even for TV productions where one actor manages the whole production pipeline (e.g. Canal+ in France or BBC in the UK), most of the metadata do get lost at one point or another. The MetaDaTV network proposal aims at initiating a European research community around metadata associated with TV productions (such as dramas, documentaries or TV films) and at gathering interested partners from all over Europe toward a joint European project submission (Horizon 2020).

The MultiSem project will propose novel advanced models for multilingual semantic processing. Existing data-driven models employ robust machine learning techniques for handling vast amounts of textual data but overlook the intricacies of the mechanisms involved in language processing which should be reflected in automatic methods. At the same time, findings in the computational semantics field fail to make their way to large-scale NLP systems, mainly due to the focus on small lexical samples which restricts the potential of the models to scale up and be used on unrestricted text. Interaction between disciplines has thus been limited up to now and the mutual potential benefits of their respective research remain unclear. At this moment of burgeoning interest in multilingual processing and semantics-related research, the MultiSem project proposes to bridge the gap between disciplines by combining the efficiency and robustness of state of the art approaches to semantic analysis with linguistically motivated semantic representations. The main novelty of the semantic processing models proposed in MultiSem is that they will be able to adapt processing to different lexical items and text types, inspired by findings regarding the organisation of semantic information in the mental lexicon and the role of context in meaning activation. It has been shown that instead of considering all possible interpretations for words in context, human bilinguals and translators restrict their choice to specific senses. This focus is largely influenced by the parameters of the communicative context and by the domain and topic of the processed texts, while a finer-grained filtering occurs only when needed for improving text understanding. Based on these findings, the models developed in MultiSem will differentiate semantic processing according to the disambiguation needs of specific words, contexts and textual genres. To achieve this ambitious goal, we intend to combine continuous space representations and topic models with traditional vector-space models for ambiguity resolution. The selection of the optimal representation for specific lexical items and text types will be guided by the output of an ambiguity type detection mechanism, combined with genre and domain identification techniques. These parameters have up to now been left unexploited in favor of models that adopt a uniform approach (either topic-based or fine-grained) for handling different words and types of text. This is largely due to the difficulty of identifying the disambiguation needs of specific lexical items and texts, a challenge that MultiSem intends to address. The models that will be developed will be mainly data-driven and enriched with knowledge from large-scale semantic resources which have been shown to improve the performance of machine learning semantic processing methods. The combination of high-level ambiguity resolution techniques (topic models and neural networks) with fine-grained (vector-based) models, and the exploitation of the knowledge available in these resources will enhance the descriptive and processing capacities of the models. The research that will be conducted in MultiSem will renew the scientific perspectives in multilingual NLP, but also in linguistics and semantics due to the knowledge that will be extracted from large volumes of data. The proposed multi-layer ambiguity resolution models will also be exploited for improving lexical selection in translation applications. Lexical errors are found to be the predominant type of errors in automatically produced translations and could be avoided if Machine Translation (MT) systems were able to identify the meaning of words and larger textual units. By improving the quality of the generated translations, MultiSem will enhance the experience of numerous users of MT systems and will have an important social impact given the current pressing demand for quality processing of large volumes of digital content.

Much clinical and biomedical knowledge is contained in the text of published articles, Electronic Health Records (EHRs) or online patient forums and is not directly accessible for automatic computation. Natural Language Processing (NLP) techniques have been successfully developed to extract information from text and convert it to machine-readable representations. The most advanced applications have focused on identifying clinically relevant entities and concepts from English text. However, for many biomedical informatics tasks it is necessary to go beyond the identification of isolated instances in single documents – the context of concept occurrences and the nature of the relationships between co-occurring concepts are often crucial for a specific understanding of the analyzed text. Furthermore, while most of the literature is available in English, EHRs in French hospitals are written in French. Therefore, it is important to develop advanced methods for French that will provide structured representations of clinical text compatible with existing representations for English. This research project will focus on the following aims: 1. Providing material for text analysis in a specialized domain (i.e. the biomedical domain) in French 2. Adaptation to a specialized domain of NLP tools developed for the general language 3. Application to the automatic detection of links between clinical characteristics and medical history of patients described in EHRs, predictive biomarkers identified by immunologic or genetic studies and evidence of such associations reported in the literature The proposed research is innovative and will provide an in-depth study of multiple biomedical texts in French (EHRs) and in English (literature). It will be guided by linguistic principles and by the application to personalized medicine. A global approach should ensure that the methods used can be generalized to other biomedical applications.

The ETAE project suggests significant advancements about the emergence and control of hydrodynamic instabilities in closed recirculating flows with a free surface. This generic flow configuration is present in numerous industrial contexts. The present aim is, from well-designed excitations by electro-active actuators, to manipulate the flow, and thus to identify the mechanisms promoting large-scale vortical instabilities arising in the presence of external mechanical noise. Bringing together the experimental/numerical skills on rotating flows at LIMSI, and the experience of GEEPS about modelling and conception of active actuators, will address important issues about the effect of parasitic noise on closed fluid systems. The exploratory side about actuators opens wide perspectives on the application of new measurement and control techniques in a fluid set-up, in close interaction with the development of new active materials expected to contribute in the future to fluid control strategies. The study of instabilities in closed rotating flows, triggered by rotating disks, has been one of the key topics for which LIMSI is internationally recognised. Such flows have now become classical topics due to their genericity and their importance in geophysical or industrial contexts. Using an experimental device consisting of a rotating vessel partially filled with liquid and a free surface, the team at LIMSI has shown evidence for instability modes due to the free surface. The flow before the instability is axisymmetric, and this axisymmetry is broken by instability modes above a given threshold (for the angular velocity of the disk). Two cases can be identified: weak deformations of the free surface where the instability manifests itself as a array of large-scale vortices, versus strong deformations where the free surface itself has broken its axisymmetry. From an experimental point of view, measurements of the free surface height in real time demands novel techniques. Besides, the strong deformation case remains even today a true challenge for numerical simulation. However, even in the weal deformation case, threshold measurements have revealed significant departures between experimental results and numerical predictions. Sensibility methods, developed only recently in the context of open flows, appear as relevant tools to understand the effect of generic external unsteadiness (of weak amplitude and mechanical origin) on the fluid system. Moreover, adding a perfectly controlled vibration to a given flow should explain, and more importantly reduce the mismatch between observed thresholds. Devising such actuators, the associated measurement methods, and integrating them into an efficient feedback loop represent as for today important technological challenges. This project is at the junction between active control of rotating flows at LIMSI and modelling of active-material-based actuators at GEEPS. Bringing together such skills is expected to lead to both fundamental and practical progress about the sensibility of confined flows to unavoidable parasitic vibrations. The exploratory side about actuators in the large deformation regime opens new important perspectives on the development of fluid-structure simulation codes as well as on the characterisation of electro-active materials.