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International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

International audience; The task of tagging papers with semantic metadata in order to analyze their relatedness represents a good foundation for a paper recommender system. The analysis from this paper extends from previous research in order to create a graph of papers from a specific domain with the purpose of determining each article's importance within the considered corpus of papers. Moreover, as non-latent representations are powerful when used in conjunction with latent ones, our system retrieves semantically close words, not present in the paper, in order to improve the retrieval of papers. Our previous analyses used the semantic representation of papers in different semantic models with the purpose of creating visual graphs based on the semantic relatedness links between the abstracts. The current analysis takes a step forward by proposing a model that can suggest which papers are of the highest relevance, share similar concepts, and are semantically related with the initial query. Our study is performed using paper abstracts in the field of information technology extracted from the Web of Science citation index. The research includes a use case and its corresponding results by using interactive and exploratory network graph representations.

International audience; Sentiment analysis has grown to become increasingly important for companies to more accurately understand customer/supplier sentiments about their processes/products and services, and predict customer churn. In particular, existing sentiment analysis aims to better understand their customer’s or supplier’s emotions which are essentially the affirmative, negative, and neutral views of users on tangible or intangible entities e.g., products or services. One of the most prevalent sources to analyse these sentiments is Twitter. Unfortunately, however, existing sentiment analysis techniques suffer from three serious shortcomings: (1) they have problems to effectively deal with streaming data as they can merely exploit (Twitter) hashtags, and (2) neglect the context of Tweets. In this paper, we present SANA: a context-aware solution for dealing with streaming (Twitter) data, analysing this data on the fly taking into account context and more comprehensive semantics of Tweets, and dynamically monitoring and visualising trends in sentiments through dashboarding and query facilities.

International audience; In a context of the end of Moore's law, energy dissipation constitutes a real challenge. Among the new energy efficient paradigms for data processing, bio-inspired computing is very promising, moreover introducing cognitive characteristics. As applications at very high scale are addressed, the size and energy dissipation of both the neuron and synapse cells needs to be minimized. In this context, this paper presents the design of an original artificial neuron, using standard 65nm CMOS technology with optimized energy efficiency and its application in basic neural networks. By recalling brain and neuron features, it is shown why neuron energy efficiency is roughly limited to 1 pJ/spike in biological neuron. Biological and artificial neurons features are carefully compared, highlighting the importance of downscaling. The artificial neuron circuit presented was designed to exhibit wide band spiking frequencies, targeting large scale bio-inspired information processing applications. The most important feature of the fabricated circuits is the neuron energy efficiency in the few fJ/spike range, which improves prior state-of-the-art by two to three orders of magnitude. This performance is achieved by minimizing two key parameters: the supply voltage and the related membrane capacitance. Meanwhile, the obtained standby power at a resting output does not exceed tens of picowatts. The circuit is sized to 35 mu m(2), reaching a spiking output frequency of 26kHz. It is then shown how this artificial technology has already been used for two applications: (i) emulation of bursting mode, important in brain stimulation context or for robotics (locomotion rhythm), (ii) stochastic resonance application, useful to detect an electrical signal buried in noise, phenomena exploited in nature by species. These results already allow envisioning the development of highly integrated neuro-processors (vision application). A variant circuit (biomimetic) could be used for robotics, neuroscience or medical applications.

Part 5: Panel Session – Risk Management of Identity Management; International audience; We motivate and give a brief overview of the Conflicting Incentives Risk Analysis (CIRA) method, explaining key ideas and concepts, offering a small example and give an overview of remaining challenges in relation to the use of CIRA in large scale risk analysis projects.

International audience; Wove paper, made on a papermaking screen or mold having a surface of smooth tightly woven wires, was the predominant paper type used for printing in the twentieth century. To aid in the study and classification of fine art prints on wove paper, the present work compares the results of five different image processing approaches for characterizing surface texture. Using a collection of popular wove papers, a reference dataset of raking light close-up images was assembled. Five research teams undertook their own processing strategies to detect affinities among the paper samples. Their success in identifying similarity groupings are reported.

International audience; Languages for embedded systems ensure predictable timing behavior by specifying constraints based on either data streaming or reactive control models of computation. Moreover, various toolsets facilitate the incremental integration of application functionalities and the system design by evolutionary refinement and model-based code generation. Modern embedded systems involve various sources of interference in shared resources (e.g. multicores) and advanced real-time constraints , such as mixed-criticality levels. A sufficiently expressive model-ing approach for complex dependency patterns between real-time tasks is needed along with a formal analysis of models for runtime resource managers with timing constraints. Our approach utilizes a model of computation , called Fixed-Priority Process Networks, which ensures functional determinism by unifying streaming and reactive control within a timed automata framework. The tool flow extends the open source TASTE tool-suite with model transformations to the BIP language and code generation tools. We outline the use of our flow on the design of a spacecraft on-board application running on a quad-core LEON4FT processor.

Computer Vision, Imaging and Computer Graphics - Theory and Applications : International Joint Conference, VISIGRAPP 2015, Berlin, Germany, March 11-14, 2015, Revised Selected Papers

Conference of 16th International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2016 ; Conference Date: 17 July 2016 Through 21 July 2016; Conference Code:126004; International audience; The Modular Microserver DataCentre (M2DC) project investigates, develops and demonstrates a modular, highly-efficient, cost-optimized server architecture composed of heterogeneous micro server computing resources, being able to be tailored to meet requirements from various application domains, including the Internet of Things. M2DC is built on three main pillars: a flexible server architecture that can be easily customised, maintained and updated; advanced management strategies and system efficiency enhancements (SEE); well-defined interfaces to surrounding software data centre ecosystem.

International audience; Mobile phone camera-based document video scan- ning is an interesting research problem which has entered into a new era with the emergence of widely used, processing capable and motion sensors equipped smartphones. We present our ongoing research on mobile phone camera-based document image mosaic reconstruction method for video scanning of paper documents. In this work, we have optimized the classic keypoint feature descriptor based image registration method, by employing the accelerometer and gyroscope sensor data. Experimental results are evaluated using optical character recognition (OCR) on the reconstructed mosaic from mobile phone camera-based video scanning of paper documents.

International audience; From a European technological and industrial perspective of the 20th century, Knowledge Management (KM) was viewed as the next step towards reaching a smart knowledge-based economy. But today, in the 21th century of big data and fast moving information, we argue that KM is not enough for reaching a qualitative human-based post-industrial society. We need a broader view in order to understand user needs and respond to their personal desires. In this endeavor, Living labs are a good way to reposition creative people at the center of technologies. But we need also methodologies and tools to accompany the transition from a competitive economy to a more sustainable society. We experimented this move at University of Reunion Island in the domain of e-education. We conceived a new paradigm called Sign Management (SM) for enhancing content producers with multimedia tools on a Creativity Platform. A methodology for co-designing educational e-services was applied in both natural (biodiversity) and cultural (music) domains in order that linear knowledge transmission lets place to an iterative know-how sharing approach between teachers and learners. This sign-based methodology serves as a condition for opening the era of Semiotic Web (Web of Signs) over Semantic Web (Web of Things). The objective is to co-create qualitative educational e-services with people based on a more natural/artificial and intelligent approach in the frame of Living labs.

We deal with the 3D reconstruction of deformed paperlike surfaces given a template and a single perspective image, for which the internal camera parameters are known. The general problem is ill-posed. We show that when the surface rulings are parallel the problem is well-posed. Given a procedure to recover the rulings direction, this particular problem is equivalent to the reconstruction of a 2D curve seen from a set of ID camera pairs given a ID template. Paper can be physically modeled by exploiting local properties. This allows us to formulate the reconstruction problem by non linear variational optimization. We provide experimental results which validate our approach on simulated and real data.

Conference of 7th International Conference on Tests and Proofs, TAP 2013 ; Conference Date: 16 June 2013 Through 20 June 2013; Conference Code:99450; International audience; To help formal verification tools to make their way into industry, they ought to be more widely used in software engineering classes. This tutorial paper serves this purpose and provides a lesson on formal specification and proof of programs with Frama-C, an open-source platform dedicated to analysis of C programs, and acsl, a specification language for C.

In our research, we study the case of a pulp and paper producer who decides to establish a partnership with one buyer. Using two different types of relationship, namely a traditional system without any collaboration scheme and Collaborative Planning, Forecasting and Replenishment, we develop decision models describing the producer and the buyer planning processes. We also identify which approach is more profitable for each actor as well as for the network, based on real costs and parameters obtained from the industrial case. We then test how different incentives can improve the traditional system and provide higher gains for each partner. Our results show that using incentives increases the system's profit by up to 4% if parameters are well defined.

Smoothly bent paper-like surfaces are developable. They are however difficult to minimally parameterize since the number of meaningful parameters is intrinsically dependent on the actual deformation. Previous generative models are either incomplete, i.e. limited to subsets of developable surfaces, or depend on huge parameter sets. We propose a generative model governed by a quasi-minimal set of intuitive parameters, namely rules and angles. More precisely, a flat mesh is bent along guiding rules, while a number of extra rules controls the level of smoothness. The generated surface is guaranteed to be developable. A fully automatic multi-camera three dimensional reconstruction algorithm, including model-based bundle-adjustment, demonstrates our model on real images.

Motion safety for robotic systems operating in the real world is critical (especially when their size and dynamics make them potentially harmful for themselves or their environment). Motion safety is a taken-for-granted and ill-defined notion in the Robotics literature and the primary contribution of this paper is to propose three safety criteria that helps in understanding a number of key aspects related to the motion safety issue. A number of navigation schemes used by robotic systems operating in the real-world are then evaluated with respect to these safety criteria. It is established that, in all cases, they violate one or several of them. Accordingly, motion safety, especially in the presence of moving objects, cannot be guaranteed (in the sense that these robotic systems may end up in a situation where a collision inevitably occurs later in the future). Finally, it is shown that the concept of inevitable collision states introduced by Fraichard and Asama (2004) does respect the three above-mentioned safety criteria and therefore offers a theoretical answer to the motion safety issue.

This article presents a concept model, and the associated tool to help advanced learners to find adapted bibliography. The purpose is the use of an IT representation as educational research software for newcomers in research. We use an ontology based on the ACM's Computing Classification System in order to find scientific articles directly related to the new researcher's domain without any formal request. An ontology translation in French is automatically proposed and can be based on Web 2.0 enhanced by a community of users. A visualization and navigation model is proposed to make it more accessible and examples are given to show the interface of our tool: Ontology Navigator., 7 pages, 3 figures, 1 table

International audience

International audience; The COVID-19 crisis has turned the world upside-down and many aspects of our social and daily lives need to be adapted. In this position paper, we argue that data management techniques for mobile computing scenarios also have to be extended for them to be useful and suited to the new situation. In particular, the concept of social distancing, which has been shown to be one of the most effective measures to cope with the virus expansion, should be incorporated as a natural component of many information services and applications for mobile users. We illustrate this idea with two example use cases: mobile recommender systems and information services for drivers. We outline current challenges and some ideas for future work.

The study of watermarks is a key step for archivists and historians as it enables them to reveal the origin of paper. Although highly practical, automatic watermark recognition comes with many difficulties and is still considered an unsolved challenge. Nonetheless, Shen et al. [2019] recently introduced a new approach for this specific task which showed promising results. Building upon this approach, this work proposes a new public web application dedicated to automatic watermark recognition entitled Filigranes pour tous. The application not only hosts a detailed catalog of more than 17k watermarks manually collected from the French National Archives (Minutier central) or extracted from existing online resources (Briquet database), but it also enables non-specialists to identify a watermark from a simple photograph in a few seconds. Moreover, additional watermarks can easily be added by the users making the enrichment of the existing catalog possible through crowdsourcing. Our Web application is available at https://filigranes.inria.fr/.

International audience; A Software Product Line (SPL) is a set of programs, called variants, which are generated from a common artifact base. Delta-Oriented Programming (DOP) is a flexible approach to implement SPLs. This short paper summarises the contributions published in [10]. A foundation for rigorous development of delta-oriented product lines of statecharts is provided by defining: a core language for statecharts, DOP on top of it, an analysis ensuring that a product line is well-formed (i.e., all variants can be generated and are well-formed statecharts). An implementation of the analysis has been applied to an industrial case study.

Summarizing video content is an important task in many applications. This task can be defined as the computation of the ordered list of actions present in a video. Such a list could be extracted using action detection algorithms. However, it is not necessary to determine the temporal boundaries of actions to know their existence. Moreover, localizing precise boundaries usually requires dense video analysis to be effective. In this work, we propose to directly compute this ordered list by sparsely browsing the video and selecting one frame per action instance, task known as action spotting in literature. To do this, we propose ActionSpotter, a spotting algorithm that takes advantage of Deep Reinforcement Learning to efficiently spot actions while adapting its video browsing speed, without additional supervision. Experiments performed on datasets THUMOS14 and ActivityNet show that our framework outperforms state of the art detection methods. In particular, the spotting mean Average Precision on THUMOS14 is significantly improved from 59.7% to 65.6% while skipping 23% of video.

International audience; Providing recommendations to users evolving in physical spaces has been a research trend since a few years now, especially with the rise of recommending engines dedicated to cultural heritage spaces like museums or tourism. The evolution of our civilisation towards more and more pervasiveness of technology in our environment leads to define the spaces in which human-being evolve as Cyber-Physical-Social Systems (CPSS), in which humans cohabit with sensors, actuators, IT devices or robots, all of these entities interacting together. In this position paper, we formalised the recommendation problem in physical spaces from the general perspective of CPSS, introducing its multiple dimensions.

International audience; The development of the IoT raises specific questions in terms of privacy, especially with respect to information to users and consent. We argue that (1) all necessary information about collected data and the collecting devices should be communicated electronically to all data subjects in their range and (2) data subjects should be able to reply also electronically and express their own privacy choices. In this position paper, we take some examples of technologies and initiatives to illustrate our position (including direct and registry-based communications) and discuss them in the light of the GDPR and the WP29 recommendations.

Locating and fixing bugs is well-known to be a time consuming task. Advanced approaches such as object-centric or back-in-time debuggers have been proposed in the literature. still in many scenarios developers are left alone with generic tools such as manual breakpoints and execution stepping that, while usable, cannot adapt to specific debugging scenarios to make the life of developers easier. In this position paper we explore several advanced on-line debugging techniques such as contextual breakpoints and on-line execution comparison, that could help developers solve complex debugging scenarios. We analyse the open research challenges these techniques pose, as well as the underlying mechanisms they require. We present early but promising prototypes we built using the Pharo programming language. We finally identify future research paths by analysing existing research and connecting it to the techniques we presented before.

International audience; The size of available graphs has drastically increased in recent years. The real-time visualization of graphs with millions of edges is a challenge but is necessary to grasp information hidden in huge datasets. This article presents an end-to-end technique to visualize huge graphs using an established Big Data ecosystem and a lightweight client running in a Web browser. For that purpose, levels of abstraction and graph tiles are generated by a batch layer and the interactive visualization is provided using a serving layer and client-side real-time computation of edge bundling and graph splatting. A major challenge is to create techniques that work without moving data to an ad hoc system and that take advantage of the horizontal scalability of these infrastructures. We introduce two novel scalable algorithms that enable to generate a canopy clustering and to aggregate graph edges. These two algorithms are both used to produce levels of abstraction and graph tiles. We prove that our technique guarantee a quality of visualization by controlling both the necessary bandwidth required for data transfer and the quality of the produced visualization. Furthermore, we demonstrate the usability of our technique by providing a complete prototype. We present benchmarks on graphs with millions of elements and we compare our results to those obtained by state of the art techniques. Our results show that new Big Data technologies can be incorporated into visualization pipeline to push out the size limits of graphs one can visually analyze.

International audience; When deploying network service function chains the focus is usually given on metrics such as the cost, the latency, or the energy and it is assumed that the underlying cloud infrastructure provides resiliency mechanisms to handle with the disruptions occurring in the physical infrastructure. In this position paper, we advocate that while usual performance metrics are essential to decide on the deployment of network service function chains, the notion of resiliency should not be neglected as the choice of virtual-to-physical placement may dramatically improve the ability of the service chains to handle with failures of the infrastructure without requiring complex resiliency mechanisms.

International audience; Formal verification is still rarely applied to the IoT (InternetofThings)software, whereas IoT applications tend to become increasingly popular and critical.This short paper promotes the usage of formal verification to ensure safetyand security of software in this domain. We present a successful case study ondeductive verification of a memory allocation module of Contiki, a popular open-source operating system for IoT. We present the target module, describe how thecode has been specified and proven using Frama-C, a software analysis platformfor C code, and discuss lessons learned.

International audience; Heterogeneous image segmentation is one of the most important tasks in image processing. It consists in partitioning the image into a set of disjoint regions. In this paper, we propose a new unsupervised image segmentation method that we call Unsupervised Image Segmentation (UIS). Our proposal performs an efficient image partition efficiently into primitive regions. This process is ensured by a local adaptive Kmeans and a novel centroids initialization. Then, similar sets are agglomerated to form homogeneous regions. For that, a low-level feature merging is employed according to a hierarchical linkage approach. Finally, in case of over-segmentation, appearing outlier regions are removed using a post process stage. Therefore, the UIS method allows to determine automatically the image region number. Indeed, it extends the Kmeans clustering to obtain meaningful regions. Several experiments were conducted using two heterogeneous image datasets. A comparison with well-known segmentation methods was also performed using the Liu's factor measure.

In the context of the development of Serious Games, this position paper sheds light on their possible use to support and enhance social inclusion through the presentation of three research projects in this domain. We first describe the context of our works and research. We then report on several events joining the scientific community that focused on the research on Digital Games for Empowerment and Inclusion. Last, we propose some results of our experience in this domain. The presented work particularly highlights the importance of user-centered design in interdisciplinary cooperation with psychologists, sociologists, vulnerable end users and the practitioners. We stress the relevance of progress beyond the state-of-the-art in recently focused areas of AI, such as, in computational theory-of-mind, in affective multimodal computing, in learning of socio-emotional skills, and in recovery of human attention processes. Typical examples are given to illustrate the projects' research results.

International audience; Characterisation and interpretation of flat ancient material objects, such as those found in archaeology, palaeoenvironments, palaeontology and cultural heritage, have remained a challenging task to perform by means of conventional X-ray tomography methods due to their anisotropic morphology and flattened geometry. To overcome limitations of mentioned methodologies for such samples, a new imaging modality based on Compton scattering is proposed in this work. Classical X-ray tomography treats Compton scatter data as noise in the image formation process, while in Compton scatter tomography the conditions are set such that Compton data becomes the principal image contrasting agent. Under this conditions, we are able, on the first hand, to avoid relative rotations between the sample and the imaging setup, and on the other hand, to obtain three dimensional data even when the object is supported by a dense material by exploiting back-scattered photons. Mathematically this problem is addressed by means of a Conical Radon transform and its inversion.The image formation process and object reconstruction model are presented. The feasibility of this methodology is supported by numerical simulations.

International audience; Most of business documents , in particular invoices , are composed of an existing color template and an added filled-in text by the users. The direct layout analysis without separating the preprinted form from the added text is difficult and not efficient. Previous works use both local features and global layout knowledge to separate the pre-printed forms and the added text. Although for real applications , they are even exposed to a great improvement. This paper presents the first pixel-based image registration of color business documents based on the NonLocal-Means (NLM) method. We prove that the NLM , commonly used for image denoising , can be also adapted to images registration at the pixel level. Our intuition tends to look for a similar neighbourhood from the first image I1 into the second image I2 and provide both an exact image registration with a precision at pixel level and noise removal. We show the feasibility of this approach on several color images of various invoices and forms in real situation and its application to the layout analysis. Applied on color documents , the proposed algorithm shows the benefits of the NLM in this context .

International audience; The level of guarantees that can provide systems leveraging MANets cannot reach that of more traditional networks due to their highly dynamic nature. Real MANets (based on mobile phones in our case) operate in an environment without any infrastructure where the nodes are intermittently connected over short or long periods of time. Solving the traditional problems that are identification, security of communications, content delivery and localization, therefore requires specific approaches. In addition, the mobile phones that make the MANet, offer different communication technologies that must be efficiently exploited in multilevel perspective, from the shorter range wireless radio standards to the longer range ones. This multilevel perspective must also integrate issues related to user preferences, energy saving and limited financial means. In this paper, we first propose a preliminary model taking into account all the elements previously mentioned in the context of a mobile phone oriented MANet. Next, we focus on two aspects that are the secure exchange of profiles and the choice of the transmission technology. We then describe the mobile application that we have developed based on this model and the first performance analysis that we have conducted. Finally, we detail the remaining challenges that must be solved in this novel approach and that will constitute the next steps of the research described in this position paper.

Part 2: Short Papers; International audience; In this position paper we examine some of the aspects of trust models, deployment, use and ‘misuse,’ and present a manifesto for the application of computational trust in sociotechnical systems. Computational Trust formalizes the trust processes in humans in order to allow artificial systems to better make decisions or give better advice. This is because trust is flexible, readily understood, and relatively robust. Since its introduction in the early ’90s, it has gained in popularity because of these characteristics. However, what it has oftentimes lost is understandability. We argue that one of the original purposes of computational trust reasoning was the human element - the involvement of humans in the process of decision making for tools, importantly at the basic level of understanding why the tools made the decisions they did. The proliferation of ever more complex models may serve to increase the robustness of trust management in the face of attack, but does little to help mere humans either understand or, if necessary, intervene when the trust models fail or cannot arrive at a sensible decision.

International audience; This short paper accompanies an invited lecture on a historical survey of geometric computer vision problems. It presents some early works on image-based 3D modeling, multi-view geometry, and structurefrom- motion, from the last three centuries. Some of these are relatively well known to photogrammetrists and computer vision researchers whereas others seem to have been largely forgotten or overlooked. This paper gives a very brief summary of an ongoing historical study.

Motivation: Piwi-interacting RNA (piRNA) is the most recently discovered and the least investigated class of Argonaute/Piwi protein-interacting small non-coding RNAs. The piRNAs are mostly known to be involved in protecting the genome from invasive transposable elements. But recent discoveries suggest their involvement in the pathophysiology of diseases, such as cancer. Their identification is therefore an important task, and computational methods are needed. However, the lack of conserved piRNA sequences and structural elements makes this identification challenging and difficult. Results: In the present study, we propose a new modular and extensible machine learning method based on multiple kernels and a support vector machine (SVM) classifier for piRNA identification. Very few piRNA features are known to date. The use of a multiple kernels approach allows editing, adding or removing piRNA features that can be heterogeneous in a modular manner according to their relevance in a given species. Our algorithm is based on a combination of the previously identified features [sequence features (k-mer motifs and a uridine at the first position) and piRNAs cluster feature] and a new telomere/centromere vicinity feature. These features are heterogeneous, and the kernels allow to unify their representation. The proposed algorithm, named piRPred, gives promising results on Drosophila and Human data and outscores previously published piRNA identification algorithms. Availability and implementation: piRPred is freely available to non-commercial users on our Web server EvryRNA http://EvryRNA.ibisc.univ-evry.fr Contact: tahi@ibisc.univ-evry.fr

INSPEC Accession Number:8979846; This paper appears in: Engineering in Medicine and Biology Magazine, IEEE This study reports on a few elements of the scientific production of the IEEE. Other features could be displayed that would be interesting for a better understanding of the trajectories of the societies, journals, etc. The possibility of projecting new data onto the current spaces allows researchers to see if journals are static (the concepts and methods remaining stable) or dynamic (evolutions, ruptures can be tracked). In other words, this type of analysis can be used as a strategic tool to follow the impact and trends in engineering sciences. The fact that the authors concentrate on the IEEE publications prohibits any comparison with other societies publishing engineering papers. Such insights are feasible through the analysis of the INSPEC database. This could bring other clues on the coverage, the competition, and the reaction to new areas.

International audience; Points of interest (POIs) in a city are specific locations that present some significance to people; examples include restaurants, museums, hotels, theatres and landmarks, just to name a few. Due to their role in our social and economic life, POIs have been increasingly gaining the attention of location-based applications, such as on-line maps and social networking sites. While it is relatively easy to find on the Web basic information about a POI, such as its geographic location, telephone number and opening hours, it is more challenging to have a deeper knowledge as to what other people say about it. What if a person wants to know all the restaurants in parsi that serve good seafood and provide a kind service? Typically, the answer to this question has to be looked for on websites that let people leave comments and opinions on POIs, a time-consuming manual task that few are willing to do. This search would be better supported by search engines if information mined from opinions were available in a structured form, such as RDF. Inthis position paper, we describe a general approach to enrich an existing RDF repository about POIs with data obtained from social networking sites.

International audience; In this position paper we argue that aspects are well-suited to describe and implement a range of strategies to make secure JavaScript-based applications. To this end, we review major categories of approaches to make client-side applications secure and discuss uses of aspects that exist for some of them. We also propose aspect-based techniques for the categories that have not yet been studied. We give examples of applications where aspects are useful as a general means to flexibly express and implement security policies for JavaScript.

Research and Development in Intelligent Systems XXVII Incorporating Applications and Innovations in Intelligent Systems XVIII; International audience; Norms impose obligations, permissions and prohibitions on individual agents operating as part of an organisation. Typically, the purpose of such norms is to ensure that an organisation acts in some socially (or mutually) beneficial manner, possibly at the expense of individual agent utility. In this context, agents are normaware if they are able to reason about which norms are applicable to them, and to decide whether to comply with or ignore them. While much work has focused on the creation of norm-aware agents, much less has been concerned with aiding system designers in understanding the effects of norms on a system. The ability to understand such norm effects can aid the designer in avoiding incorrect norm specification, eliminating redundant norms and reducing normative conflict. In this paper, we address the problem of norm understanding by providing explanations as to why a norm is applicable, violated, or in some other state. We make use of conceptual graph based semantics to provide a graphical representation of the norms within a system. Given knowledge of the current and historical state of the system, such a representation allows for explanation of the state of norms, showing for example why they may have been activated or violated.

International audience; The FORCES project aims at providing robust and declarative for- malisms for analyzing systems in the emerging areas of Security Protocols, Biological Systems and Multimedia Semantic Interaction. This short paper describes FORCES's motivations, results and future research directions.

International audience; The AUTOI project is creating a virtual communication resource overlay with autonomic characteristics to adapt the services and resources offered to meet changing user needs, business goals, and environmental conditions. Self-knowledge enables the network to reconfigure itself in the face of change to adapt its services according to business goals. The requirements of an information model, to support self-knowledge (concepts, characteristics and behaviour) are presented. This information model plus ontologies provide a common language to represent the self-management of the overlay. This position paper details the requirements in specifying such an information model and language, and describes how the model and language will be used within the project.

Maritime transport and traffic is following the growing slope of trade exchanges between continents and nations. Maritime risks and economic constraints increase at the same speed, together with international competition. This context may be the best asset to join private interests of companies with general concerns of maritime regions for safer seas. The real challenge is to converge rapidly towards standard packages of e-services playing on their economical added value to induce safer attitudes in maritime transport. The position paper demonstrates that most components of a global e-transport and e-navigation service do exist, or could be developed; more, the costs of these technologies could largely be decreased if open, standard, solutions were used instead of proprietary, disparate, "maritime specific" instrumentation. Maritime ITS (MITS) may be our chance to transform the myth of safer seas in reality if we manage to crystallize, from now, the user demand for universal, low cost, transport and navigation aid services with nations will to decrease maritime risks under all their forms. This goal requires accelerating international cooperation works for avoiding invasion of disparate, proprietary, mono task solutions, on and offshore.

International audience; Knowledge Management (KM) is one of the key progress factors in organizations. It aims at capturing explicit and tacit knowledge of an organization in order to facilitate the access, sharing, and reuse of that knowledge as well as creation of new knowledge and organizational learning. We introduce in this tutorial knowledge engineering techniques that help at structuring information and knowledge and we present techniques defined in CSCW to handle design rationale and negotiation. An example of collective knowledge is then defined: Project memory. Approaches that help to keep track project knowledge are then detailed. We extend our tutorial by presenting multi-agents techniques and the socio semantic web approach which helps to represent concepts built collectively in an organization. These approaches can be illustrated in real applications in several domains: design, safety, marketplace, etc.

We report on Raman measurements of single‐ and few‐layer graphene flakes. Raman mapping in combination with scanning force microscopy allows us to locally relate the thickness of the graphite flake with the spectral properties. It turns out that the width of the D’ line is highly sensitive to the transition from single‐ to double‐layer graphene. The defect‐induced D line is found to be most prominent at steps between sections of different height and along the edge of the graphite flake.

Using the fact that effective wireless range decraise in inverse function of local traffic density, we show that a variable traffic density impacts the curvature of paths in a dense wireless ad hoc network the same way a variable optical density bends light paths. We set up the general laws that paths must satisfy in presence of traffic flow density. We quantify the effect and we give some example of configurations treated analytically.

International audience; The development of HLT tools inevitably involves the need for language resources. However, only a handful number of languages possesses such resources. This paper presents the development of HLT tools for the African language Naija (Nigerian Pidgin), spoken in Nigeria. Particularly, this paper is focusing on developing language resources for a tokenizer, an automatic speech system for predicting the pronunciation of the words and their segmentation.

This paper describes the participation of SYSTRAN to the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018). We participate for the first time using a neural sentence similarity classifier which aims at predicting the relatedness of sentence pairs in a multilingual context. The paper describes the main characteristics of our approach and discusses the results obtained on the data sets published for the shared task.

International audience; This paper presents a multidisciplinary case study of practice with machine learning for computer music. It builds on the scientific study of two machine learning models respectively developed for data-driven sound synthesis and interactive exploration. It details how the learning capabilities of the two models were leveraged to design and implement a musical instrument focused on embodied musical interaction. It then describes how this instrument was employed and applied to the composition and performance of aego, an improvisational piece with interactive sound and image for one performer. We discuss the outputs of our research and creation process, and build on this to expose our personal insights and reflections on the multidisciplinary opportunities framed by machine learning for computer music.

International audience; This paper presents a study about ambiguous French prepositions, stressing out their roles as dependencies introducers, in order to derive some translation heuristics into English, based on a FrenchEnglish set of parallel texts. These heuristics are formulated out of statistical observations and use some up-to-date results in Machine Translation (MT). Their originality mostly relies upon two items: (1) The importance given to syntax and dependency relations, along with lexicons, the latter being well browsed by the present literature in the domain (2) The existence of intrinsic semantics in prepositions, something rather discarded in NLP literature devoted to statistical MT, that tends to point at the most appropriate translation. An experiment has been run on corpora in both languages, using a dependency parser in the source language, and results looked to be encouraging for a “step by step approach” for MT improvement.