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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.

Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the aim of this paper'. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, 'in this paper we propose', 'in this study we propose' and 'in this paper we propose a new method to' are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods., 21 pages, 11 figures

International audience; Whenever a bug or vulnerability is detected in the Linux kernel, the kernel developers will endeavour to fix it by introducing a patch into the mainline version of the Linux kernel source tree. However, many users run older "stable" versions of Linux, meaning that the patch should also be "backported" to one or more of these older kernel versions. This process is error-prone and there is usually a long delay in publishing the backported patch. Based on an empirical study, we show that around 8% of all commits submitted to Linux mainline are backported to older versions, but often more than one month elapses before the backport is available. Hence, we propose a patch backporting technique that can automatically transfer patches from the mainline version of Linux into older stable versions. Our approach first synthesizes a partial transformation rule based on a Linux mainline patch. This rule can then be generalized by analysing the alignment between the mainline and target versions. The generalized rule is then applied to the target version to produce a backported patch. We have implemented our transformation technique in a tool called FixMorph and evaluated it on 350 Linux mainline patches. FixMorph correctly backports 75.1% of them. Compared to existing techniques, FixMorph improves both the precision and recall in backporting patches. Apart from automation of software maintenance tasks, patch backporting helps in reducing the exposure to known security vulnerabilities in stable versions of the Linux kernel.

International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

Background The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

Snellen, I.A.G. et al., In this White Paper, which was submitted in response to the European Space Agency (ESA) Voyage 2050 Call, we recommend the ESA plays a proactive role in developing a global collaborative effort to construct a large high-contrast imaging space telescope, e.g. as currently under study by NASA. Such a mission will be needed to characterize a sizable sample of temperate Earth-like planets in the habitable zones of nearby Sun-like stars and to search for extraterrestrial biological activity. We provide an overview of relevant European expertise, and advocate ESA to start a technology development program towards detecting life outside the Solar System., I.S. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No 694513. A.L.M. acknowledges the financial support of the F.R.S.-FNRS through a postdoctoral researcher grant. MB acknowledges support from the UK Science and Technology Facilities Council (STFC) research grant ST/S000631/1. I.R. acknowledges support from the Spanish Ministry of Science and Innovation and the European Regional Development Fund through grant PGC2018-098153-B-C33, as well as the support of the Generalitat de Catalunya/CERCA programme. E.P. acknowledges support from the Spanish Ministry of Science through grant PGC2018-098153-B-C31. J.M.D. acknowledges funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. 679633; Exo-Atmos) Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We acknowledge financial support from the ASI-INAF agreement n.2018-16-HH.0 We acknowledge financial support from Spanish MCIU SEV-2017-0709 and PID2019-110689RB-I00 awards. We acknowledge financial support from the ASI-INAF agreement n.2018-16-HH.0 AV acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 757561). KH acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 771620). NJM acknowledges funding from the Leverhulme Trust, and a Science and Technology Facilities Council Consolidated Grant (ST/R000395/1). PdV acknowledges funding from the Netherlands Organisation for Scientific Research NWO (Veni Grant No. 639.041.750). IP acknowledges financial support from the ASI-INAF agreements 2019-29-HH.06-HH.0 and 2015-019-R.1-2018. FS acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 678194; FALCONER). MJ acknowledges support from the Knut and Alice Wallenberg foundation (KAW). JLB acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No 805445. T.H. acknowledges support from the European Research Council under the Horizon 2020 Framework Program via the ERC Advanced Grant Origins 83 24 28. JLG acknowledges ISSI Team 464 for useful discussion.

Background In the health care environment, teamwork is paramount, especially when referring to patient safety. We are interested in recent and innovative solutions such as escape games, which is a type of adventure game that may be highly useful as an educational tool, potentially combining good communication skills with successful gamification. They involve teams of 5 to 10 individuals who are “locked” in the same room and must collaborate to solve puzzles while under pressure from a timer. Objective The purpose of this paper was to describe the steps involved in creating and implementing an educational escape game. This tool can then be put into service or further developed by trainers who wish to use it for learning interprofessional collaboration. Therefore, we started with an experience of creating an educational escape game for emergency medicine teams. Methods We chose to develop an educational escape game by using 6 successive steps. First, we built a team. Second, we chose the pedagogical objectives. Third, we gamified (switched from objectives to scenario). Next, we found the human and material resources needed. Thereafter, we designed briefing and debriefing. Lastly, we tested the game. Results By following these 6 steps, we created the first ambulant educational escape game that teaches people, or nurses, doctors, and paramedics, working in emergency medicine to work as a team. Conclusions From a pedagogic point of view, this game may be a good tool for helping people in multidisciplinary fields (medical and paramedical teams) to learn how to work collaboratively and to communicate as a group. Above all, it seems to be an innovative tool that complements medical simulation–based learning and thus consolidates traditional education.

Solution comparison is a process used in different classes of evolutionary algorithms. It can be either for choosing a better solution or differentiating a pair of solutions. While there is no doubt that the fitness function allows determining the best solution, its use to distinguish between solutions is questionable, especially for combinatorial optimisation problems. This short paper focuses on the Travelling Salesman Problem (TSP). 39 instances of the TSPLIB are chosen, two solution samples are generated for each instance. A collision analysis of the fitness function one the TSP is presented, then an introduction to an efficient hash function with almost zero collisions.

This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.

Learning how to model a problem described in natural language as a linear program requires students to practice using various and numerous exercises. Moreover, immediate feedback on the validity of their solutions helps a better and faster understanding. In this paper, we present an idea on how students and teachers can automatically evaluate linear programming models. We also describe how this idea was implemented on the learning platform caseine.org and is now used by hundreds of students from various universities.

Background The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty. Objective We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed. Methods The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs). Results For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents. Conclusions We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

International audience; Software development for electronic games has remarkable performance and portability requirements, and the system and low-level languages usually provide those. This ecosystem became homogeneous at commercial levels around C and C++, both for open source or proprietary solutions. However, innovations brought other possibilities that are still growing in this area, including Rust and other system languages. Rust has low-level language properties and modern security guarantees in access to memory, concurrency, dependency management, and portability. The Open Source game engine Amethyst has become a reference solution for game development in Rust, has a large and active community, and endeavors in being an alternative to current solutions. Amethyst brings parallelism and performance optimizations, with the advantages of the Rust language. This paper presents scripting concepts that allow the game logic to be implemented in an external interpreted language. We present a scripting module called Legion Script that was implemented for the entity and component system (ECS) called Legion, part of the Amethyst organization. As a Proof-of-Concept (POC), we perform the Python code interpretation using the Rust Foreign Function Interface (FFI) with CPython. This POC added scripting capabilities to Legion. We also discuss the benefit of using the alternative strategy of developing a POC before contributing to OSS communities in emergent technologies.

International audience; Automation throughout history has caused profound changes in employment dynamics. With the advent of the fourth industrial revolution, a new threat may affect employability, asrobots and AI-based processes can now assume tasks considered exclusive to humans. This position paperaims to motivate the study of the effects of AI and automation on employability, extending it into a collaborative network perspective. The problem is firstly observed from a historical perspective. The collaboration aspects are considered through the analysis of two casestudies. Results suggestthata latent element of collaborative networks, complexity, may have effects in terms of employability.

Background In the recent decades, the number of apps promoting health behaviors and health-related strategies and interventions has increased alongside the number of smartphone users. Nevertheless, the validity process for measuring and reporting app quality remains unsatisfactory for health professionals and end users and represents a public health concern. The Mobile Application Rating Scale (MARS) is a tool validated and widely used in the scientific literature to evaluate and compare mHealth app functionalities. However, MARS is not adapted to the French culture nor to the language. Objective This study aims to translate, adapt, and validate the equivalent French version of MARS (ie, MARS-F). Methods The original MARS was first translated to French by two independent bilingual scientists, and their common version was blind back-translated twice by two native English speakers, culminating in a final well-established MARS-F. Its comprehensibility was then evaluated by 6 individuals (3 researchers and 3 nonacademics), and the final MARS-F version was created. Two bilingual raters independently completed the evaluation of 63 apps using MARS and MARS-F. Interrater reliability was assessed using intraclass correlation coefficients. In addition, internal consistency and validity of both scales were assessed. Mokken scale analysis was used to investigate the scalability of both MARS and MARS-F. Results MARS-F had a good alignment with the original MARS, with properties comparable between the two scales. The correlation coefficients (r) between the corresponding dimensions of MARS and MARS-F ranged from 0.97 to 0.99. The internal consistencies of the MARS-F dimensions engagement (ω=0.79), functionality (ω=0.79), esthetics (ω=0.78), and information quality (ω=0.61) were acceptable and that for the overall MARS score (ω=0.86) was good. Mokken scale analysis revealed a strong scalability for MARS (Loevinger H=0.37) and a good scalability for MARS-F (H=0.35). Conclusions MARS-F is a valid tool, and it would serve as a crucial aid for researchers, health care professionals, public health authorities, and interested third parties, to assess the quality of mHealth apps in French-speaking countries.

Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. Overcoming this challenge is critical when aiming to associate metabolic signatures with potential pathological outcomes. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. An important number of scientific articles discuss relations between chemical compounds and biomedical concepts in various contexts, from biomarkers to therapeutic uses. The extraction of these statements and their interconnection in a graph structure can thus allow us to identify and explore relations strongly supported in the scientific literature.The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Beyond this result, FORUM can also provide insights into complex biological questions and the extracted information could then be used for further developments.Containing more than 8 billion triples and providing more than 8 million relations, FORUM leverages the increasing availability of linked datasets in life science and is built in agreement with FAIR principles. A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.

One of the most exciting scientific challenges is to detect Earth-like planets in the habitable zones of other stars in the galaxy and search for evidence of life. The ability to observe and characterise dozens of potentially Earth-like planets now lies within the realm of possibility due to rapid advances in key space and imaging technologies. The associated challenge of directly imaging very faint planets in orbit around nearby very bright stars is now well understood, with the key instrumentation also being perfected and developed. Such advances will allow us to develop large transformative telescopes, covering a broad UV-optical-IR spectral range, which can carry out the detailed research programmes designed to answer the questions we wish to answer: Carry out high contrast imaging surveys of nearby stars to search for planets within their habitable zones. Characterise the planets detected to determine masses and radii from photometric measurements. Through spectroscopic studies of their atmospheres and surfaces, search for habitability indicators and for signs of an environment that has been modified by the presence of life. Active studies of potential missions have been underway for a number of years. The latest of these is the Large UV Optical IR space telescope (LUVOIR), one of four flagship mission studies commissioned by NASA in support of the 2020 US Decadal Survey. LUVOIR, if selected, will be of interest to a wide scientific community and will be the only telescope capable of searching for and characterizing a sufficient number of exoEarths to provide a meaningful answer to the question - Are we alone?. This paper is a submission to the European Space Agency Voyage 2050 call for white papers outlining the case for an ESA contribution to a Large UVOIR telescope., Submission to the European Space Agency Voyage 2050 call for white papers

International audience; Algebraic datatypes, and among them lists and trees, have attracted a lot of interest in automated reasoning and Satisfiability Modulo Theories (SMT). Since its latest stable version, the SMT-LIB standard defines a theory of algebraic datatypes, which is currently supported by several mainstream SMT solvers. In this paper, we study this particular theory of datatypes and prove that it is strongly polite, showing also how it can be combined with other arbitrary disjoint theories using polite combination. Our results cover both inductive and finite datatypes, as well as their union. The combination method uses a new, simple, and natural notion of additivity, that enables deducing strong politeness from (weak) politeness.

International audience; The Internet of Things (IoT) is a broad concept comprising a wide ecosystem of interconnected services and devices connected to the Internet. The IoT concept holds fabulous promises, but security aspects tend to be significant barriers for the adoption of large-scale IoT deployments. This paper proposes an approach to assist companies or organisations in the security audit of IoT systems. This approach called Model Learning and Checking Approach (MLCA) combines model learning for automatically extracting models from event logs, and model checking for verifying whether security properties, given under the form of generic LTL formulas hold on models. The originality of MLCA lies in the fact that auditors do not have to craft models or to be expert LTL users. The LTL formula instantiation, which makes security properties concrete, is indeed semi-automatically performed by means of an expert system composed of inference rules. The latter encode some expert knowledge, which can be applied again to the same kind of systems with less efforts. We evaluated MLCA on 5 IoT systems with security measures provided by the European ENISA institute. We show that MLCA is very effective in detecting security issues and provides results within reasonable time.

International audience; In the context of digitalization, some companies are considering a transition to Industry 4.0 to ensure greater flexibility, productivity and responsiveness. The implementation of a relevant performance management system is then a real necessity to measure the degree of achievement of these objectives. In the era of Industry 4.0, the potential access to large amounts of data, i.e. Big Data, poses new challenges to the design and implementation of these systems. With the exponential growth of data generated from different sources, there is a need for extensive exploitation of data for performance management. Given the large volume of data, the speed at which it is generated and the variety of data sources, the manufacturing sector is facing with the challenge of creating value from large data sets. This paper introduces some potential benefits of Big Data for business and in particular its role in performance management systems. However, the key idea is that Big Data are not always neither available nor necessary. Authors focus on the concept of smart data, the result of the transformation of Big Data, and define a set of necessary and sufficient conditions the data should satisfy to be considered as Smart. The paper presents some methods of smart data extraction. Such smart data will be used to feed the performance management system in order to obtain more accurate, timely and representative key performance indicators.

A planner aims to target individuals who exceed a threshold in a characteristic, such as wealth or ability. The individuals can rank their friends according to the characteristic. We study a strategy-proof mechanism for the planner to use the rankings for targeting. We discuss how the mechanism works in practice, when the rankings may contain errors., Forthcoming in AEA Papers & Proceedings

An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the images into types and subtypes according to the fracture's location and complexity. In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN). As it is known, CNNs need large and representative datasets with reliable labels, which are hard to collect for the application at hand. In this paper, we design a curriculum learning (CL) approach that improves over the basic CNNs performance under such conditions. Our novel formulation reunites three curriculum strategies: individually weighting training samples, reordering the training set, and sampling subsets of data. The core of these strategies is a scoring function ranking the training samples. We define two novel scoring functions: one from domain-specific prior knowledge and an original self-paced uncertainty score. We perform experiments on a clinical dataset of proximal femur radiographs. The curriculum improves proximal femur fracture classification up to the performance of experienced trauma surgeons. The best curriculum method reorders the training set based on prior knowledge resulting into a classification improvement of 15%. Using the publicly available MNIST dataset, we further discuss and demonstrate the benefits of our unified CL formulation for three controlled and challenging digit recognition scenarios: with limited amounts of data, under class-imbalance, and in the presence of label noise. The code of our work is available at: https://github.com/ameliajimenez/curriculum-learning-prior-uncertainty., Comment: Medical Image Analysis

International audience; In this paper we introduce a new class of Pickup and Delivery problems on circles (or rings). These problems arise in the field of public transportation systems where autonomous (i.e. driverless) vehicles travel on circular networks. We consider a set of stations arranged in a circle and a set of transportation requests. Each request asks for the transportation of a certain quantity from a pickup station to a delivery station. A fleet of capacitated vehicles is available at the depot. In the first part of the paper we propose a classification scheme for these problems. In the second part, we address the variants in which the vehicles are allowed to move in a single direction of the circle (either clockwise or counterclockwise) and the objective is to minimize the number of tours on the ring while serving all the requests. We provide a complexity analysis for this class of problems. We develop polynomial time algorithms for the variants that are polynomially solvable and proofs of NP-hardness for the variants that are NP-hard. In addition, for the latter, we provide mathematical formulations and perform computational tests that show the effectiveness of these formulations. Finally, we compare optimal solutions with those obtained using a straightforward greedy algorithm.

CNRS 4, FNEGE 3, HCERES B; International audience; Research literature on blockchain technology is starting to emerge. Blockchain technology is primarily associated with cryptocurrencies, raising interest in an application in other business sectors, thanks to its characteristics. The decentralized nature of blockchain creates the new concept of a token economy, which can help to trace and valorize intellectual property - one of the essential challenges in co-creation. This paper considers how blockchain technology and tokenization can solve challenges in co-creation projects consisting of large companies, startups and entrepreneurial accelerators. The paper proposes an exploratory analysis based on interviews conducted with French companies and startups. The findings include a framework of (1) challenges, lack of operating rules, trust, and traceability, and (2) the potential benefits of blockchain and tokenization to solve issues that arise. The findings can be useful for both startups and large companies as a starting point for understanding and adopting blockchain technology.

International audience; The Digital Twin has the ability to store historical and current data about the physical object. In IoT, the concept of general-purpose sensing is aiming at determining a set of basic sensing capabilities from which deriving, by means of Artificial Intelligence algorithms and techniques (termed Synthetic Sensing), relevant information for describing an environment. This paper explores the possible relationships and the feasibility of integration of Synthetic Sensing within a Digital Twin framework. The relevant concepts and associated technologies, the challenges, and some of the enabled scenarios that this integration can bring are presented. This approach is in its infancy and there is a strong need to validate and assess its viability, feasibility, and benefits. The paper identifies some challenges and validation steps that can lead to consolidation and broad adoption of this approach. Finally, the paper presents some future work that aiming at proving the approach.

In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark. All the metaheuristics are embedded in a Greedy Randomized Adaptive Search Procedure. The different versions of the archived GRASP approach are compared using large industrial instances. The numerical results show that the proposed approach provides good solutions regarding the preferences. Finally, the comparison of the optimized schedules with the actual factory schedules shows the significant improvements that our approach can bring.

International audience; This paper investigates the gain-scheduled observer-based consensus of linear parameter varying (LPV) multi-agent systems (MASs). The main contribution of the paper is that, differently from LPV observer-based consensus design methods previously reported in the literature, the controller and observer gains are allowed to be functions of some time-varying parameter vector, which can be measured in real-time. It is shown that, under the assumption of synchronized varying parameter trajectories, the design conditions can be described as a feasibility problem involving a finite number of linear matrix inequalities (LMIs). The obtained results are illustrated by means of a numerical example.

In this paper, we propose an image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain. By introducing different families of codes, we show the benefit in terms of image tampering localization and complexity of using control code error localization as an authentication function. Indeed, we first experimentally show that error localization block codes is as precise as using classical error correcting codes (Reed-Solomon and BCH codes) to locate image tampering. However, their corresponding decoding algorithms complexity is at least quadratic which make them impractical for some real time applications. To solve this problem, we introduce error-control codes called Error-Locating codes where error localization is reduced to a single syndrome computation performed with low number of binary operations (detailed later in the paper). We provide comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes.

Despite the increasing importance of multiple priors in various domains of economics and the significant theoretical advances concerning them, choice-based incentive-compatible multiple-prior elicitation largely remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and two procedures for eliciting it. The method does not rely on specific assumptions about subjects’ ambiguity attitudes or probabilistic sophistication. To demonstrate its feasibility, we implement it in two incentivized experiments to elicit the multiple-prior equivalent of subjects’ cumulative distribution functions over continuous-valued sources of uncertainty. We find a predominance of non-degenerate probability intervals among subjects for all explored sources, with intervals being wider for less familiar sources. Finally, we use our method to undertake the first elicitation of the mixture coefficient in the Hurwicz α-maxmin EU model that fully controls for beliefs.

The problem of estimating the angular speed of a solid body from attitude measurements is addressed. To solve this problem, we propose an observer whose dynamics are not constrained to evolve on any specific manifold. This drastically simplifies the analysis of the proposed observer. Using Lyapunov analysis, sufficient conditions for global asymptotic stability of a set wherein the estimation error is equal to zero are established. In addition, the proposed methodology is adapted to deal with angular speed estimation for systems evolving on the unit circle. The approach is illustrated through several numerical simulations., V1 is the extended version of the paper submitted to IEEE Control Systems Letters. V2 is the extended version of the paper accepted for publication. V3-V8 fix some minor oversights in the printed version of the paper and adds some additional technical details. Corrections with respect to the published version are colored in blue and there are footnotes explaining them

International audience; The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space that is learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In the recent years, a series of papers have presented different extensions of the VAE to sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and/or corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompass a large subset of these temporal VAE extensions. Then we present in details seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate those models with existing classical temporal models (that are also presented for the sake of completeness). We reimplemented those seven DVAE models and we present the results of an experimental benchmark that we conducted on the speech analysis-resynthesis task (the PyTorch code will be made publicly available). An extensive discussion is presented at the end of the paper, aiming to comment on important issues concerning the DVAE class of models and to describe future research guidelines.

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs.

International audience; This paper focuses on similarity caching systems, in which a user request for an object o that is not in the cache can be (partially) satisfied by a similar stored object o , at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

International audience; In this paper we consider the model of single insertion-deletion systems that at each step insert or delete a single symbol in a context-free manner (i.e. at any position in the word). The corresponding operation is performed if the word contains a set of permitting (that have to be present in the word) and/or forbidding (that must not be present in the word) strings of some size. The main result of this paper states that if forbidding strings of size 2 and permitting strings of size 1 are used then computational completeness can be achieved; moreover, checking for a single permitting symbol is sufficient. We also show that in the case of systems having rules with forbidding conditions only, all regular languages can be obtained. Finally, we show the computational non-completeness in the case of systems using rules with forbidding strings of size 1 (single symbols) and permitting strings of any finite size.

International audience; Serious Games (SG) have become popular and their efficiency has been proven in numerous domains throughout the years. You can find them for a plethora of subjects, in bountiful forms and consequently, to answer various needs. They are particularly efficient to reproduce concrete situations and to train on specific objectives. However, most of them are only designed for one player. One explanation might be their design that seems indeed more complex for multiplayer SG. This paper focuses on a certain type of multiplayer SG: those based on multirole. Thus, this paper aims at drawing a state of the art of multi-role SG and their design, as a first step to propose a design model of multi-role SG.

International audience; This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban development. Based on an extensive literature review, the paper first presents the smart city concept, emphasizing smart city architecture and the role of data in smart city solutions. The second part presents the Internet of Things, focusing on IoT technology, the use of IoT in smart city applications, and security. Finally, the paper presents crowdsourcing with particular attention to mobile crowdsourcing and its role in smart cities. The paper shows that IoT and crowdsourcing have a crucial role in two fundamental layers of smart city applications, namely, the data collection and services layers. Since these two layers ensure the connection between the physical and digital worlds, they constitute the central pillars of smart city projects. The literature review also shows that the smart city development still requires stronger cooperation between the smart city technology-centered research, mainly based on the IoT, and the smart city citizens-centered research, mainly based on crowdsourcing. This cooperation could beneficiate in recent developments in the field of crowdsensing that combines IoT and crowdsourcing.

This paper targets the execution of data science (DS) pipelines supported by data processing, transmission and sharing across several resources executing greedy processes. Current data science pipelines environments provide various infrastructure services with computing resources such as general-purpose processors (GPP), Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Tensor Processing Unit (TPU) coupled with platform and software services to design, run and maintain DS pipelines. These one-fits-all solutions impose the complete externalization of data pipeline tasks. However, some tasks can be executed in the edge, and the backend can provide just in time resources to ensure ad-hoc and elastic execution environments. This paper introduces an innovative composable "Just in Time Architecture" for configuring DCs for Data Science Pipelines (JITA-4DS) and associated resource management techniques. JITA-4DS is a cross-layer management system that is aware of both the application characteristics and the underlying infrastructures to break the barriers between applications, middleware/operating system, and hardware layers. Vertical integration of these layers is needed for building a customizable Virtual Data Center (VDC) to meet the dynamically changing data science pipelines' requirements such as performance, availability, and energy consumption. Accordingly, the paper shows an experimental simulation devoted to run data science workloads and determine the best strategies for scheduling the allocation of resources implemented by JITA-4DS., This paper has been submitted to a Journal JWE special number. arXiv admin note: substantial text overlap with arXiv:2103.07978

International audience; Abstract Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, we provide an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors. The proposed methodology is based on a time-separated representation within the standard Proper Generalized Decomposition, where the time coordinate is transformed into a multi-dimensional time through new separated coordinates, each representing one scale, while continuity is ensured in the scale coupling. For instance, when considering two different time scales, the governing Partial Differential Equation is commuted into a nonlinear system that iterates between the so-called microtime and macrotime, so that the time coordinate can be viewed as a 2D time. The macroscale effects are taken into account by means of a finite element-based macro-discretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the time domain. The resulting separated representation allows us a very fine time discretization without impacting the computational efficiency. The proposed formulation is explored and numerically verified on thermal and elastodynamic problems.; Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, we provide an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors. The proposed methodology is based on a time-separated representation within the standard Proper Generalized Decomposition, where the time coordinate is transformed into a multi-dimensional time through new separated coordinates, each representing one scale, while continuity is ensured in the scale coupling. For instance, when considering two different time scales, the governing Partial Differential Equation is commuted into a nonlinear system that iterates between the so-called microtime and macrotime, so that the time coordinate can be viewed as a 2D time. The macroscale effects are taken into account by means of a finite element-based macro-discretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the time domain. The resulting separated representation allows us a very fine time discretization without impacting the computational efficiency. The proposed formulation is explored and numerically verified on thermal and elastodynamic problems.

International audience; This paper develops a state-feedback Model Predictive Control (MPC) framework for Nonlinear Parameter Varying (NLPV) systems with explicit Lipschitz nonlinearities along the input trajectory. Considering a guess for the evolution of the scheduling parameters along the prediction horizon, the proposed optimization procedure for the MPC design includes a terminal stage cost, a contracting terminal region constraint and nominal predictions scheduled with respect to this guess. The terminal region is taken so that it is monotonically decreasing to a predetermined set, which guarantees recursive feasibility of the algorithm with respect to a bound on the admissible uncertainties (introduced from the scheduling parameter guess). The terminal set is a scheduled robust control invariant set for Lipschitz nonlinear systems, computed through some proposed LMIs. The inclusion of the terminal ingredient also serves to demonstrate Input-to-State Quadratic Stability. This paper ends with a successful numerical simulation example of the technique applied to the control of a Semi-Active automotive suspension system equipped with Electro-Rheological dampers. Comparisons are given with respect to open-loop behaviour and to a Robust LQR controller.

Building renovation is a complex collaborative process requiring the interaction between planners, architects, civil engineers, energy experts, and managers of (pre-)manufacturing plants supplying building elements, components of energy supply and distribution systems “just in sequence” to densely used urban spaces – where the majority of buildings under renovation are located. Therefore, the availability of a complete, comprehensive Building Information Model, amalgamating current and future product and process models is of outstanding importance. Approaches, suggesting so-called “monolithic” building information models did not deliver the expected “value for money” since the efforts required to set up and maintain such digital models requested more resources than available. Therefore, the authors present in this paper an alternative approach to information, knowledge management, and sharing in the AECO-sector, i.e. modular ontologies. The flexible and dynamic approach to combine new and available modules of information addresses more responsively the needs of the AECO sector. Furthermore, such BIM models overcome limitations in adaptability, extensibility, etc. of current “openBIM models. Due to this the shift towards using semantic web technologies for knowledge base and semantic interoperability has been increased in the AECO industry. The work presented in the paper introduces a recently developed linked data, an ontology-based framework that harmonizes and orchestrates ontologies recently developed for the construction domain. It studies inter-model and inter ontology relationships to address concepts that are currently absent from “building ontologies”. The developed framework can be used to support collaborative environments in the engineering and manufacturing sector supporting the efficient sharing of information between architects, engineers, manufacturing plants, and assembly crews on the construction site.

International audience; The paper presents the experience collected in a case study in the construction equipment concerning the use of physical prototypes for the development of product-service systems (PSS) enabled by newdigital technologies. The paper firstly presents how a scaled physical prototype has been deployed to foster value co-creation with customers about the cross-disciplinary opportunity of the transition toward autonomous and electrical construction sites. Secondly, the paper presents the lessons learned during the empirical study.

International audience; Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

International audience; Protocols for password-based authenticated key exchange (PAKE) allow two users sharing only a short, low-entropy password to establish a secure session with a cryptographically strong key. The challenge in designing such protocols is that they must resist offline dictionary attacks in which an attacker exhaustively enumerates the dictionary of likely passwords in an attempt to match the used password. In this paper, we study the resilience of one particular PAKE against these attacks. Indeed, we focus on the Secure Remote Password (SRP) protocol that was designed by T. Wu in 1998. Despite its lack of formal security proof, SRP has become a de-facto standard. For more than 20 years, many projects have turned towards SRP for their authentication solution, thanks to the availability of open-source implementations with no restrictive licenses. Of particular interest, we mention the Stanford reference implementation (in C and Java) and the OpenSSL one (in C). In this paper, we analyze the security of the SRP implementation inside the OpenSSL library. In particular, we identify that this implementation is vulnerable to offline dictionary attacks. Indeed, we exploit a call for a function computing modular exponentiation of big numbers in OpenSSL. In the SRP protocol, this function leads to the call of a non-constant time function, thereby leaking some information about the used password when leveraging cache-based Flush+Reload timing attack. Then, we show that our attack is practical, since it only requires one single trace, despite the noise of cache measurements. In addition, the attack is quite efficient as the reduction of some common dictionaries is very fast using modern resources at negligible cost. We also prove that the scope of our vulnerability is not only limited to OpenSSL, since many other projects, including Stanford's, ProtonMail and Apple Homekit, rely on OpenSSL, which makes them vulnerable. We find that our flaw might also impact projects written in Python, Erlang, JavaScript and Ruby, as long as they load the OpenSSL dynamic library for their big number operations. We disclosed our attack to OpenSSL who acknowledged the attack and timely fixed the vulnerability.

A large number of evaluation methods, called semantics, have been proposed in the literature for assessing strength of arguments. This paper investigates their equivalence. It argues that for being equivalent, two semantics should have compatible evaluations of both individual arguments and pairs of arguments. The first requirement ensures that the two semantics judge an argument in the same way, while the second states that they provide the same ranking of arguments. We show that the two requirements are completely independent. The paper introduces three novel relations between semantics based on their rankings of arguments: weak equivalence, strong equivalence and refinement. They state respectively that two semantics do not disagree on their strict rankings; the rankings of the semantics coincide; one semantics agrees with the strict comparisons of the second and it may break some of its ties. We investigate the properties of the three relations and their links with existing principles of semantics, and study the nature of relations between most of the existing semantics. The results show that the main extensions semantics are pairwise weakly equivalent. The gradual semantics we considered are pairwise incompatible, however some pairs are strongly equivalent in case of flat graphs including Max-based (Mbs) and Euler-based (Ebs), for which we provide full characterizations in terms respectively of Fibonacci numbers and the numbers of an exponential series. Furthermore, we show that both semantics (Mbs, EMbs) refine the grounded semantics, and are weakly equivalent with the other extension semantics. We show also that in case of flat graphs, the two gradual semantics Trust-based and Iterative Schema characterize the grounded semantics, making thus bridges between gradual semantics and extension semantics. Finally, the other gradual semantics are incompatible with extension semantics.

International audience; In fair resource allocation, envy freeness (EF) is one of the most interesting fairness criteria as it ensures no agent prefers the bundle of another agent. However, when considering indivisible goods, an EF allocation may not exist. In this paper, we investigate a new relaxation of EF consisting in minimizing the Ordered Weighted Average (OWA) of the envy vector. The idea is to choose the allocation that is fair in the sense of the distribution of the envy among agents. The OWA aggregator is a well-known tool to express fairness in multiagent optimization. In this paper, we focus on fair OWA operators where the weights of the OWA are decreasing. When an EF allocation exists, minimizing OWA envy will return this allocation. However, when no EF allocation exists, one may wonder how fair min OWA envy allocations are. After defining the model, we show how to formulate the computation of such a min OWA envy allocation as a Mixed Integer Program. Then, we investigate the link between the min OWA allocation and other wellknown fairness measures such as max min share and EF up to one good or to any good. Finally, we run some experiments comparing the performances of our approach with MNW (Max Nash Welfare) on several criteria such as the percentage of EF up to one good and any good.

Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that Bitcoin miners prioritize transactions based on their offered "transaction fee-per-byte." Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available., This is a pre-print of our paper accepted to appear to ACM IMC 2021

International audience; Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.

International audience; While the recent progress on automated generalisation helped National Mapping Agencies to derive topographic maps more and more quickly, there are still practical cartographic issues that require attention. For instance, embankments are represented with line symbols showing the slope of the embankment. This paper proposes an automated algorithm, called ReBankment that displaces the embankment lines from the roads and rivers that overlap the embankment symbol. ReBankment is based on a triangulation to identify neighbourhoods, and on a least squares adjustment to displace and distort the embankment line while preserving its shape. The paper also proposes how to handle complex cases and scaling issues. ReBankment is tested on real data from a 1:25k scale topographic map. Keywords map generalisation and least squares and displacement and topographic map and embankment

International audience; Business process optimization is a strategic activity in organizations because of its potential to increase profit margins and reduce operational costs. One of the main challenges in this activity is concerned with the problem of optimizing allocation and sharing of resources. Companies are continuously adjusting their resources to their needs following different strategies. However, the dynamic provisioning strategies are hard to compare. This paper proposes an automatic analysis technique to evaluate and compare the execution time and resource occupancy of a business process relative to a workload and a provisioning strategy. Four different strategies are presented, which are guided-respectively-by recent resource usage, recent resource request, predicted behavior, and a combination of available strategies. Such analysis is performed on models conforming to an extension of BPMN with quantitative information, including resource availability and constraints. Within this framework, the approach is fully mechanized using a formal and executable specification in the Maude rewriting logic framework, which relies on existing techniques and tools for simulating probabilistic and real-time specifications. The paper includes results on the extensive experimentation that has been carried out for validation purposes.

International audience; Widely linear (WL) receivers have been developed in the past for single antenna interference cancel- lation (SAIC) of one rectilinear (R) or quasi-rectilinear (QR) data-like multi-user interference (MUI) or co-channel interference (CCI) in particular. The SAIC technology has been implemented in global system for mobile communications (GSM) handsets in particular and has been further analyzed for voice ser- vices over adaptive multi-user channels on one slot (VAMOS) standard. It remains of great interest for several current and future applications using R or QR signals, such as anti-collisions processing in radio frequency identification (RFID) or in satellite-AIS systems and to densify 5G and Beyond 5G (B5G) net- works through one dimensional signaling or over-loaded large MU-MIMO systems. It may be required to cancel the inter-symbol interference (ISI) of control and non-payload communications (CNPC) links of unmanned aerial vehicles (UAV) and the inter-carrier interference (ICI) of filter bank multi-carrier offset quadrature amplitude modulation (FBMC-OQAM), which are now candidate for B5G mobile networks. For these challenging applications, the development of enhanced WL filtering based SAIC or Multiple Antenna Interference Cancellation (MAIC) receivers for R and QR signals may be of great interest. Such a receiver, corresponding to a three-input WL frequency shift (FRESH) receiver, has been introduced recently for QR signals. However this WL receiver is not robust to a data-like MUI having a residual frequency offset (FO), which occurs for most of the previous applications. In this context, the paper first extends, for arbitrary propagation channels, the standards (for R and QR MUI) and the enhanced (for QR MUI) SAIC/MAIC WL receivers to MUI with a non-zero FO. Then, it shows the less efficiency of the two-input WL receiver for QR MUI with a non-zero FO and the performance improvement obtained with the three-input WL receiver. Finally, it analyzes, both analytically and by simulations, for R and QR MUI, the impact of the MUI FO on the performance of the proposed receivers. The results of the paper should allow the develop- ment of new powerful WL receivers for UAV CNPC links, anti-collisions AIS systems and for FBMC-OQAM networks in particular.

International audience; Virtual steering techniques enable users to navigate in larger Virtual Environments (VEs) than the physical workspace available. Even though these techniques do not require physical movement of the users (e.g. using a joystick and the head orientation to steer towards a virtual direction), recent work observed that users might unintentionally move in the physical workspace while navigating, resulting in Unintended Positional Drift (UPD). This phenomenon can be a safety issue since users may unintentionally reach the physical boundaries of the workspace while using a steering technique. In this context, as a necessary first step to improve the design of navigation techniques minimizing the UPD, this paper aims at analyzing and modeling the UPD during a virtual navigation task. In particular, we characterize and analyze the UPD for a dataset containing the positions and orientations of eighteen users performing a virtual slalom task using virtual steering techniques. Participants wore a head-mounted display and had to follow three different sinusoidal-like trajectories (with low, medium and high curvature) using a torso-steering navigation technique. We analyzed the performed motions and proposed two UPD models: the first based on a linear regression analysis and the second based on a Gaussian Mixture Model (GMM) analysis. Then, we assessed both models through a simulation-based evaluation where we reproduced the same navigation task using virtual agents. Our results indicate the feasibility of using simulation-based evaluations to study UPD. The paper concludes with a discussion of potential applications of the results in order to gain a better understanding of UPD during steering and therefore improve the design of navigation techniques by compensating for UPD.