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A review of the status of computing and computer science education is provided in this yearbook of the British Computer Society (BCS). Following a brief introduction to the BCS and its aims, reports are presented which deal with: 1) the activities of the Society's education division; 2) the BCS examination; 3) BCS examination regulations and syllabi; 4) education in the Society's local branches; and 5) the work of the Computer Education Group and of the British Computer Society Schools' Committee. Future requirements of the computer industry are surveyed, and a report of college computer activities is presented. An experimental project in Berkshire is described, computer education for secondary school teachers is discussed, and a school scheduling system reviewed. Also included are papers on the International Federation for Information Processing (IFIP) and its world conference on computer education, and a list of computer courses available at the post-secondary level. A booklist, a roster of 16mm computer films, and an international bibliography of materials on computer education are also appended. (LB)

This paper describes an efficient combinatorial method for simplification of topological features in a 3D scalar function. The Morse-Smale complex, which provides a succinct representation of a function's associated gradient flow field, is used to identify topological features and their significance. The simplification process, guided by the Morse-Smale complex, proceeds by repeatedly applying two atomic operations that each remove a pair of critical points from the complex. Efficient storage of the complex results in execution of these atomic operations at interactive rates. Visualization of the simplified complex shows that the simplification preserves significant topological features while removing small features and noise.

This paper describes a highly flexible component architecture, primarily designed for automotive control systems, that supports distributed dynamically- configurable context-aware behaviour. The architecture enforces a separation of design-time and run-time concerns, enabling almost all decisions concerning runtime composition and adaptation to be deferred beyond deployment. Dynamic context management contributes to flexibility. The architecture is extensible, and can embed potentially many different self-management decision technologies simultaneously. The mechanism that implements the run-time configuration has been designed to be very robust, automatically and silently handling problems arising from the evaluation of self- management logic and ensuring that in the worst case the dynamic aspects of the system collapse down to static behavior in totally predictable ways.

We present visibility computation and data organization algorithms that enable high-fidelity walkthroughs of large 3D geometric data sets. A novel feature of our walkthrough system is that it performs work proportional only to the required detail in visible geometry at the rendering time. To accomplish this, we use a precomputation phase that efficiently generates per cell vLOD: the geometry visible from a view-region at the right level of detail. We encode changes between neighboring cells' vLODs, which are not required to be memory resident. At the rendering time, we incrementally construct the vLOD for the current view-cell and render it. We have a small CPU and memory requirement for rendering and are able to display models with tens of millions of polygons at interactive frame rates with less than one pixel screen-space deviation and accurate visibility.

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysts on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

International audience; The sensor deployment problem consists in finding an optimal (or near-optimal) way of placing sensors in a given area in order to cover the whole space. However, sometimes it is not necessary to monitor the complete area; one may want to focus on some points considered as target. This leads to the target coverage problem. This problem includes two main processes: (1) placing sensors around the targets, (2) scheduling the activation time redundant sensors in order to lengthen the network lifetime. This paper proposes a multi-objective approach based on genetic algorithms that aims to simultaneously find good positions of sensor nodes and the maximum number of disjoint cover sets. A new chromosome (solution) encoding in which the genes contain both, the position and the identifier of sensor owning group is introduced. The efficiency of the proposed approach is assessed by a comparison with existing methods.

International audience; Objective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in order to conduct more effective comparisons. In this paper, we present a framework to tackle this challenge through a comprehensive analysis of the community structure of overlapping community structured networks. We illustrate our approach with an experimental analysis of a real-world network with a ground-truth community structure that we compare with the output of eight different overlapping community detection procedures, representative of categories of popular algorithms available in the literature. The results allow a better understanding of their behavior. Furthermore, they demonstrate that more emphasis should be put on the topology of the uncovered community structure in order to evaluate the effectiveness of community detection algorithms.

International audience; In this paper, we develop and evaluate a robust single-lead electrocardiogram (ECG) delineation system and waves localization method based on nonlinear filtering approach. This system is built in two phases. In the first phase, we proposed a mathematical model for detecting ECG features like QRS complex peak, P and T-waves onsets and ends from noise free of synthetic ECG signal. Later, we develop a theoretical model to obtain real approach for detecting these features from real noisy ECG signals. To evaluate our method, it has been applied on electrocardiogram signals of QT-MIT standard database, the QRS peak detection obtained sensitivity (Se) 98.88 and a positive productivity P+ =98.43. For P-onset, P-end, T-end evaluations, this approach provides Sensitivity (Se) of 75.16, 71, and 90.7 respectively. Another test has been conducted to evaluate the performance of this approach against different levels of noise with the same synthetic ECG signal. For denoising test on electrocardiogram signals, our approach based on nonlinear filtering scheme (NLFS) gives robust performance against noise.

International audience; Influence in Twitter has become recently a hot research topic since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this paper, we propose a new approach for influence assessment on Twitter network, it is based on a modified version of the conjunctive combination rule in belief functions theory in order to combine different influence markers such as retweets, mentions and replies. We experiment the proposed method on a large amount of data gathered from Twitter in the context of the European Elections 2014 and deduce top influential candidates.

International audience; Various dynasties ruled the Indian sub-continent and left behind enormous and rich cultural heritage that also included intellectually enriched research in the shape of various documents scripted in Urdu. In order to provide efficient access to this knowledge, analysis though digitizing the existing work is the need of hour. In addition to digitization, efficient search mechanisms also need to be implemented to provide users a rapid access to the queried information. In most cases, the digitized documents are complemented by manually assigned tags which not only is a time consuming task but also provides a very limited search facility. Automating the transcription of these documents using Optical Character Recognition (OCR) systems is also challenging due to the very complex cursive nature of Urdu text. To overcome these limitations, a keyword spotting based information retrieval system for document images is introduced in this study. The proposed technique relies on two major modules, document indexing and retrieval. Images of documents are segmented into partial words (ligatures) and identical partial words (PWs) are grouped into clusters. We introduce the concept of considering each (partial) word as a unique shape and a set of shape descriptors is extracted to characterize the PWs. The clusters of PWs are used to index a given set of documents. During retrieval, the query word presented to the system is matched with the clusters in the database and all documents containing instances of the query word are retrieved and presented to the user. The system evaluated on a set of printed Urdu documents in Nastaliq font realized promising precision and recall rates.

International audience; In this paper, we propose a low-cost n-dimensional cache (nD-Cache) architecture for FPGA-Based image and signal processing Systems On Chip (SoCs). The architecture allows efficient access to structured data as in 2D or 3D images. We developed a theoretical model for our architecture. It gives a methodology to define cache's practical implementation based on the application and system parameters. Complexity and performance measurements for selected image processing algorithms like jumping snake and 2D Back-Projection are done and compared to classical solutions like associative caches. The architecture is shown to be efficient for tracking algorithm applications by exploiting spacial and temporal locality. Numerical results indicate that 50% improvement in run-time performance can be achieved.

Preprint; International audience; The design of immersive interaction for mixed reality based on headmounted displays (HMDs), hereafter referred to as Mixed Reality (MR), is still a tedious task which can hinder the advent of such devices. Indeed, the effects of the interface design on task performance are difficult to anticipate during the design phase: the spatial layout of virtual objects and the interaction techniques used to select those objects can have an impact on task completion time. Besides, testing such interfaces with users in controlled experiments requires considerable time and efforts. To overcome this problem, predictive models, such as the Keystroke-Level Model (KLM), can be used to predict the time required to complete an interactive task at an early stage of the design process. However, so far these models have not been properly extended to address the specific interaction techniques of MR environments. In this paper we propose an extension of the KLM model to interaction performed in MR. First, we propose new operators and experimentally determine the unit times for each of them with a HoloLens v1. Then, we perform experiments based on realistic interaction scenarios to consolidate our model. These experiments confirm the validity of our extension of KLM to predict interaction time in mixed reality environments.

International audience; The provision of public data through various Database-as-a-Service (DBaaS) providers has recently emerged as a significant trend, backed by major organisations. This paper introduces Nebula, a non-profit middleware providing multicloud querying capabilities by fully outsourcing its users' queries to the involved DBaaS providers. First, we propose a quoting procedure for those queries, whose need stems from the pay-perquery policy of the providers. Those quotations contain monetary cost and response time estimations, and are computed using provider-generated tenders. Then, we present an agent-based dynamic optimisation engine that orchestrates the outsourced execution of the queries. Agents within this engine cooperate in order to meet the quoted values. We evaluated Nebula over simulated providers by using the Join Order Benchmark (JOB). Experimental results showed Nebula's approach is, in most cases, more competitive in terms of monetary cost and response time than existing work in the multi-cloud DBMS literature.

The movement of data between processing and memory units, often referred to as the ‘von Neumann bottleneck’ is the main reason for the degraded performance of contemporary computing systems. In an effort to overcome this bottleneck, methods to ‘compute’ at the location of data are being pursued in many emerging memories, including Resistive RAM (ReRAM). Although many prior works have pursued addition in memory, the latency of n-bit addition has not been judiciously optimized, resulting in O(n) or at best O(log(n)). Computing with three states can enable carry-free addition and result in a latency which is independent of operand width (O(1)). In this work, we propose a method to perform carry-free addition completely in memory (a storage array, a processing array and their peripheral circuitry). The proposed technique incurs a latency of 22 memory cycles, which outperforms other in-memory binary adders for n > 32. This speed is achieved at the cost of increased peripheral hardware.

International audience; Conversational information seeking is a major emerging research area because of the increasing popularity of conversational AI systems users utilize to perform their search tasks. Search systems and multiple other user supporting applications benefit from modeling the search tasks users carry out to satisfy their information needs. Most existing search task modeling methods are monolingual, and few methods leverage user clicks even though clicked URLs are crucial for modeling user intent. We propose a language-agnostic, user intent aware approach to model search tasks from user interactions with search systems. The proposed approach leverages user intent modeling from clicked query-document pairs, latent representations of queries in a language-agnostic space, and graph-based clusteringto model search tasks in an unsupervised approach. Experimental results demonstrate the proposed approach outperforms recent work in search task modeling, supporting user queries in multiple languages. It can also produce search task modeling results in the order of milliseconds, an essential aspect for conversational systems and user support applications requiring realtime results.

International audience; The routing is a real-world engineering problem in IoT Collection Networks. It has a direct impact on the network performance. The routing problem can be treated as a multi-objective optimization problem. Our objective is then to study the IoT routing problem using the Multi-Objective Grey Wolf Optimizer (MOGWO) [1], which consists in translating the routing process into a multi-objective optimization problem and implementing its execution with the MOGWO. In this context, the experimental study showed that the MOGWO provides better quality of routing (higher lifetime of the network, more efficient delivery delay and higher number of neighbors).

International audience; For over twenty years the EDOC conference has been the primary annual event for disseminating and discussing the latest developments in the area of enterprise computing. The workshop program is an important satellite event of the EDOC conference. Workshops cover more focused topics and allow for the presentation and discussion of work that is in the earlier development stages. As such, the workshops provide an excellent forum for discussing topics from the area of enterprise computing that have the potential to become important research streams within the next few years, and for discussing topics that are already important in a smaller and more focused setting. In addition to the workshop program, the EDOC Demonstration track offers an exciting and highly interactive outlet for researchers and practitioners to present prototypes and applications in the context of enterprise computing. Four demonstrations are presented this year. 2020 is the year of the COVID-19 pandemic, with a strong impact on research and social life. Despite the challenges of new organization (the conference and the workshops will be held on-line), we are proud to present an interesting program and the four workshops that will be held in conjunction with EDOC this year:• The workshop on Trends in Enterprise Architecture Research, (TEAR)• The workshop on Service-oriented Enterprise Architecture for Enterprise Engineering (SoEA4EE)• The workshop on Privacy and Security in Enterprise Modeling (PriSEM)• The workshop on Frontiers of Process Aware Systems (FoPAS).[...]

International audience; BACKGROUND AND MOTIVATION: The world population is living longer than ever before, Ambient Assisted Living (AAL) research try to ensure people are ageing well by using digital and physical technologies. The development of AAL platforms presents several challenges (e.g., sensor data integration, device interoperability). Many requirements for AAL platforms can be met by using the Internet of Things (IoT) and Cloud Computing paradigm standards. METHODOLOGY: In this paper, we conducted a systematic mapping of the literature on AAL platforms to investigate the correlation between these three paradigms. We focused on AAL platforms that use IoT and Cloud Computing concepts to meet their requirements. Our primary research question is “What are the requirements of AAL platforms in the IoT Era and their relationship with IoT standards?”. We defined as the scope of our systematic mapping the primary studies about AAL platforms published between 2013 to 2017, and we used seven indexed electronic databases. RESULTS: We obtained 35 papers whose AAL platforms developed using IoT or Cloud Computing standards. We then analyse and correlate the requirements, architectures, and evaluation types presented by these platforms. We found patterns by connecting specific requirements with platform design. Webelieve these results assist AAL developers and architectural designers in the creation of new AAL platforms and applications.

International audience; Developing products for improving plant nutritional status, e.g. fertilizers or plant growth regulators, is an important topic to move towards sustainability in agriculture and to ensure to feed the world population. A key challenge is to identify when, what, and how much nutrients to add to plants' growth environment. In this paper, we study a use case on how to characterize rapeseed plant nutrient deficiencies during their growth. A promising approach consists of deriving data from spectroscopy of leaves, and using this representation to predict what kind of deficiency (if any) plants are undergoing. We are considering three research questions: 1) from which day after onset of a nutrient deficiency we can identify it, 2) whether leaves that have sprouted under nutrient-rich conditions can still help in identifying problems. Third, and most importantly, performing the spectroscopy on the full range of wavelengths is expensive, which under production conditions allows for only relatively few samples. We therefore explore how to perform dimensionality reduction and preprocessing to achieve good predictive accuracy. We show that 1) deficiencies can be identified early on, 2) leaf generations help to predict nutrient deficiencies, and 3) that preprocessing increases the accuracy and dimensionality reduction can be performed without loss of accuracy. Along the way, we find that our some of our industry partners' assumptions about the data do not seem to be borne out by our empirical results, and that the subset of data they initially selected turns out to be too easy to model. The full data leads to more informative insights.

The goal of this work is to provide a brief classification of some Shortest Path Problem (SPP) variants that include edge constraints and that find applications in several different contexts, including optical networks, transportation and logistics. One of the most broad and notable classes of edge-constrained SPPs is given by Resource Constrained Shortest Path Problems (RCSPPs). In RCSPPs, in addition to the customary directed graph $G=(V, A)$ and edge-distance function, a L-dimensional vector of resources R is defined. Essentially, each resource is related to relevant link attributes that need to be accounted for in the planning of the path. Accordingly, a path P* is optimal whenever it is minimal w.r.t. the distance function, and satisfies the restrictions enforced on the resources Other variants can involve colors assigned to the nodes and/or the edges of the graph and/or reload costs. These kinds of problems have relevant applications in network reliability. Particularly interesting is the so-called k-Color SPP, where a color is assigned to each edge and the minimum path from a given source node s to a target node t must not cross more than k differently colored edges. As for the use of reload cost, a reload cost r(b, c) is assigned to each pair of colors (b, c) and represents the amount to be paid if in a path P an arc of color c is traversed after an arc of color b.

International audience; Internet of Things (IoT) is a network where several and various interconnected, smart objects continuously generate alarge amount of data transmitted over Internet. Several efforts and research works in IoT focused on hardware constraints, lowcost, application design, as well as good communication technologies. However, due to the intrinsic characteristics of diversity,heterogeneity, large-scale, dynamic and large volume of data generated and various IoT applications needs, using traditionaldatabase management mechanisms and analytics architectures is not generally suitable. Then, it is challenging to provideefficient IoT data storage and query processing mechanisms for satisfying IoT application needs. This paper identifies the mainspecifications and mechanisms of data and query management for IoT and classifies them. Moreover, this work presentsadvances on data and query management mechanisms on IoT, shows their advantages and limits and discusses the challengingopen research issues that need to be addressed for providing guidelines for further contributions.

Hashtags are crucial social data that are useful for multiple purposes, such as tweets' classification, indexing, content categorization, search or recommendation. Hashtags recommendation on Twitter is an important issue, seeing the importance of this social data and the need of social users to be provided with personalized information that fits their interests. Users recommendation is also an eminent task as it allows a user to broaden his network by users with similar interests. This work focuses on how hashtags can be analyzed for recommendation purposes. First, we present a state of the art reviewing social analysis, users and hashtags' recommendation. We then present a new architecture of hashtags and users recommender system based on hashtags' semantic analysis. This proposal is based on ontology as a semantic resource. We build social user profiles, analyze hashtags and study their contextual and temporal cooccurrence. We then propose two novel ranking schemes. The first is HF-IUTF scheme that weights hashtags in the user profile. We use it to rank and filter the representative hashtags of each user's social profile. The second contribution is HF-IGHF scheme: Given a group of users, this method identifies the hashtags representative of the group. We apply spectral clustering algorithm to obtain similar clusters. We can then recommend similar users of the same cluster and representative new and recent hashtags of users in the cluster. The originality of this architecture is the way user profiles will be semantically indexed and analyzed, and ranking schemes that are independent of the way the similarity group is built.

International audience; VR has now come from industry to everyday application. Mainstream software and devices allow artists to create contents with a fast learning curve. Since 2014, with the launch of Google Cardboard and 360 cameras at a reasonable price, with the massive success of Unity 3D and Unreal UDK, real-time immersion no longer stands in the hands of experts but spreads to creative enthusiasts which result in a huge production of content. Like at the early age of photography and then cinema, slowly emerge questions about composition, narrative structure and visual grammar. This article is a raw presentation of issues of narrative grammar in 360.

International audience; Nowadays, more and more sources (connected devices, social networks, etc.) emit real-time data with fluctuating rates over time. Existing distributed stream processing engines (SPE) have to resolve a difficult problem: deliver results satisfying end-users in terms of quality and latency without over-consuming resources. This paper focuses on parallelization of operators to adapt their throughput to their input rate. We suggest an approach which prevents operator congestion in order to limit degradation of results quality. This approach relies on an automatic and dynamic adaptation of resource consumption for each continuous query. This solution takes advantage of i) a metric estimating the activity level of operators in the near future ii) the AUTOSCALE approach which evaluates the need to modify parallelism degrees at local and global scope iii) an integration into the Apache Storm solution. We show performance tests comparing our approach to the native solution of this SPE.

International audience; Projector-camera systems, used in Spatial AugmentedReality, automatically adapt the video projections tothe scene objects according to the visualization conditions.This paper introduces a novel non-invasive (without StructuredLight) method based on a combination of traditionalFeature Matching (FM) and more computationally effectiveOptical Flow (OF). It requires only one projected and oneacquired image at a time in the most difficult case when bothprojected content and geometric transformations changeevery frame. It detects scene changes when OF fails andthus should be replaced by FM. In the experiments, weshow that the method yields a more precise and less shakycompensation for different types of projected videos, and isup to 2.8 times faster than previous FM-based works.

International audience; Part of the research effort in automatic person discovery in multimedia content consists in analyzing the errors made by algorithms. However exploring the space of models relating algorithmic errors in person discovery to intrinsic properties of associated shots (e.g. person facing the camera) - coined as post-hoc analysis in this paper - requires data cura- tion and statistical model tuning, which can be cumbersome. In this paper we present a visual and interactive tool that facilitates this exploration. Adequate statistical building blocks are defined, and coordinated by visual and interactive components inspired from the literature in information visualization. A case study is conducted with multimedia researchers to validate the tool. Real data obtained from the MediaEval person discovery task was used for this experiment. Our approach yielded novel insight that was completely unsuspected previously.

International audience; Recent advances in processor manufacturing has led to integrating tens of cores in a single chip and promise to integrate many more with the so-called manycore architectures. Manycore architectures usually integrate many small power efficient cores, which can be 32-bit cores in order to maximize the performance per Watt ratio. Providing large physical memory (e.g. 1 TB) to such architectures thus requires extending the physical address space (e.g. to 40 bits). This extended physical space has early been identified as a problem for 32-bit operating systems as they can normally support at maximum 4 GB of physical space, and up to 64 GB with memory extension techniques. This paper presents a scalable solution which efficiently manages large physical memory. The proposed solution decomposes the kernel into multiple units, each running in its own space, without the virtual memory mechanism (directly in physical mode). User applications, however, continue to run in the virtual mode. This solution allows the kernel to manage a large physical memory, while allowing user space applications to access nearly 4 GB of virtual space. It has been successfully implemented in ALMOS, a UNIX-like operating system, running on the TSAR manycore architecture, which is a 32-bit virtual 40-bit physical manycore architecture. Moreover, the first results show that this approach improves both the scalability and the performance of the system.

International audience; —Redundant data transmissions are likely to happen repeatedly in Wireless Sensor Networks (WSNs). According to the literature survey, energy-efficiency predominately relies on data aggregation rather than routing or duty-cycling approaches. As data redundancy dominates the power usage in communication costs, in order to identify and reduce the redundant data transmissions by individual nodes, we propose a hybrid data ag-gregative window function (DAWF) algorithm for exploiting both spatial and temporal data redundancies in WSNs. Furthermore, the proposed novel approach aims to process the hybrid filtration using both compressive and prediction-based techniques in sensor nodes (SN) as well as in cluster-head (CH) nodes. In this regard, the experimental study case of this work show that the DAWF mechanism can suppress a huge amount of temporal redundant data transmissions in sensor nodes while providing reliable data messages towards the base station (BS). Moreover, DAWF CH can also suppress a large amount of spatial redundancies by utilizing the optimum DAWF parameters of the CH node.

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin., Extended technical report -- short version to appear at ICCV 2015

The focus of existing virtual communities is centered on a particular product or social interaction and the role of mobile devices is restricted to exchange a limited amount of contents. Herewith we envisage that the upcoming virtual communities will exploit the potential of social interaction and context information to offer personalized services to its members and mobile devices will play a significant role in this process. As a step towards this direction, in this paper we propose a business model for the mobile virtual communities in which the mobile device takes on the role of a content producer and content consumer. Though there are a number of research issues which need to be addressed to realize such virtual communities, in this paper we focus on the service requirements, architecture and open source software implementation of a technical platform for the content producer and consumer mobile devices.

The concept of liquid clocks introduced in this paper is a significant step towards a more precise compile-time framework for the analysis of synchronous and polychromous languages. Compiling languages such as Lustre or Signal indeed involves a number of static analyses of programs before they can be synthesized into executable code, e.g., synchronicity class characterization, clock assignment, static scheduling or causality analysis. These analyses are often equivalent to undecidable problems, necessitating abstracting such programs to provide sound yet incomplete analyses. Such abstractions unfortunately often lead to the rejection of programs that could very well be synthesized into deterministic code, provided abstraction refinement steps could be applied for more accurate analysis. To reduce the number of false negatives occurring during the compilation process, we leverage recent advances in type theory — with the definition of decidable classes of value-dependent type systems — and formal verification, linked to the development of efficient SAT/SMT solvers, to provide a type-theoretic approach that considers all the above analyses as type inference problems. To simplify the exposition of our new approach in this paper, we define a refinement type system for a minimalistic, synchronous, stream-processing language to concisely represent, analyze, and verify logical and quantitative properties of programs expressed as stream-processing data-flow networks. Our type system provides a new framework for representing logical time (clocks) and scheduling properties, and to describe their relations with stream values and, possibly, other quantas. We show how to analyze synchronous stream processing programs (a la Lustre, Signal) to enable previously described analyses involved in compiling such programs. We also prove the soundness of our type system and elaborate on the adaptability of this core framework by outlining its extensibility to specific models of computations and other quantas.

International audience; GPGPUs and other accelerators are becoming a mainstream asset for high-performance computing. Raising the programmability of such hardware is essential to enable users to discover, master and subsequently use accelerators in day-today simulations. Furthermore, tools for high-level programming of parallel architectures are becoming a great way to simplify the exploitation of such systems. For this reason, we have extended NT2 (Numerical Template Toolbox), a C++ scientific computing library which can generate code for SIMD and multi-threading systems in a transparent way. In this paper, we study how to introduce an accelerator-based programming model into this library to allow developers to reap the benefits of such an architecture from a simple, MATLAB like code. After a brief description of the NT2 framework, we explain how our accelerator programming model has been designed and integrated in a pure C++ library. We conclude by showing the applicability and performance of this tool on some practical applications.

International audience; Service-oriented computing has been successfully adopted by the industry. This raises however new challenges, especially with respect to service selection and ranking in dynamic environments. Current solutions for service selection and ranking lack flexibility to handle dynamic environments. This paper proposes to integrate algorithms based on the Formal Concept Analysis theory to extend service-oriented component models. This solution improves the self-adaptation of service-oriented component models. The resulting framework externalizes service selection and ranking. Results are integrated in the Apache Felix iPOJO component model.

International audience; In recent years, service-oriented computing has been successfully adopted by the industry and used in numerous projects. This raises however new challenges, especially with respect to service selection and ranking in dynamic environments. Selection and ranking are mostly based on first-order predicates and comparison functions, expressed in the consumer side. Such approaches suffer from several shortcomings. The expression language is too complex for most non-technical persons and yet not powerful enough to capture external information, which limits the use of contextual information. Also, current solutions lack flexibility to handle dynamic environments. This paper proposes to integrate algorithms based on the Formal Concept Analysis theory to extend service-oriented component models. The resulting framework externalizes service selection and ranking. Results are integrated in the Apache Felix iPOJO component model.

International audience; Organizations, companies and Web platforms hold large amounts of unused data. These data are trapped in separate data sources, locked up in legacy formats and only reachable through several different protocols, making usage difficult. It is therefore necessary to manage this multiplicity of data sources in order to build a solution able to combine this multi-origin data into a coherent smart data set. We define a meta-model and models to describe data source diversity in a flexible way. We therefore propose an adaptive architecture that generates data integration workflows at runtime. We evaluate our approach to offer scalability, responsiveness, and dynamic and transparent data source management. We apply our approach in a live scenario from a French company to show how it adapts to industrial needs and facilitates smart data production and reuse. This paper describes our models and strategies and presents our resource-oriented architecture.

International audience; —Sequential pattern mining under various constraints is a challenging data mining task. The paper provides a generic framework based on constraint programming to discover sequence patterns defined by constraints on local patterns (e.g., gap, regular expressions) or constraints on patterns involving combination of local patterns such as relevant subgroups and top-k patterns. This framework enables the user to mine in a declarative way both kinds of patterns. The solving step is done by exploiting the machinery of Constraint Programming. For complex patterns involving combination of local patterns, we improve the mining step by using dynamic CSP. Finally, we present two case studies in biomedical information extraction and stylistic analysis in linguistics.

International audience; Service Creation Environments facilitate the creation of complex services and play a major role in the software industry. In this context, we aim at developing a domainspecific framework (networking domain) that uses a chain of existing ”off-the-shelf” tools that are integrated together fromthe design phase to the verification activities. In this paper, we propose a new meta-model that extends ArchiMate to provide a domain-specific modeling language concerning the Deep Sea Observatories (DSO). We instantiate, as a case study, DSO model with identification and localization functions from this language, and apply it to our framework that relies on an IMS platform to evaluate the service model. These functions can beorchestrated with other services (e.g. military or civil reaction) or interconnected with other SOSystems. On the one hand, this illustrates our approach in relying on Enterprise Architecture (EA) framework that respects: multiple-views, perspectives of stakeholders, and domain specificities. On the other hand, it shows the reusability of our framework by changing applications from different domains: Video Conference as a Telecom Service, and Localizations for DSO.

International audience; Negotiation is a fundamental mechanism in multi-agent systems since it allows self-interested agents to come to mutual agreements and to distribute resources efficiently and effectively. However, in competitive situations, the agents need to negotiate repeatedly with one another, thus developing mechanisms that are effective over repeated interactions is a key challenge. At present, few research work deals with designing mechanisms for complex multilateral negotiations in which agents’ interactions are fully decentralized. We tackle the problem of multilateral negotiation where a group of agents has to make a joint decision. The explanatory breadth of this mechanism is illustrated by looking at its applicability in situations in which agents are self-interested, have asymmetric information, are motivated by individual interests but they have to converge to an agreement, and behave opportunistically. We search to guide the agents in their process, promote even partial solutions and facilitate the achievement of agreements specially in presence of large sets of agents where negotiation is notably difficult and convergence is hardly achieved. To this end, the protocol we propose leads the agents in efficiently coordinating their negotiations and to coming to an agreement based on provided rules. Negotiation illocutions are formalised to allow interactions between the agents. In addition rules are provided for the agents to manage their interactions. We introduce an illustrative scenario and detail the properties of our protocol. Experimental results show that our protocol guides the agents towards an agreement with reasonable communication and computational complexity.

International audience; Energy harvesting has emerged as an efficient way for powering wireless devices in order to extend their lifetime. In this paper a dynamic power manage- ment strategy is described that guarantees the correct execution of every feasible application for a given energy harvester, energy storage unit and processor. The so-called ED-H strategy was recently proved to be optimal in that sense that ED-H optimizes the Quality of Service defined as the ratio of deadline success for the real-time jobs. We propose to describe this novel strategy for dynamic power management and scheduling.

Software applications that are provided through services following the Software as a service (SaaS) paradigm generally need to be compliant to some standards. In order to select the software application that provides the most conformant service interface, we develop a formal framework that tests the compliance and reports quantitative results that help experts to take the right decision. Unlike existing work that focuses on a single dimension when checking the compliance of a software application with the corresponding standard (for example the functional dimension exclusively, or the syntactic dimension exclusively), in this work we consider multiple dimensions at the same time. This provides more comprehensive results. We implemented a prototype and we tested our framework on a case study regarding the selection of software applications for our collaborative platform.

International audience; This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.

This work proposes a maximum power point tracking algorithm based on neural networks embedded in a low-cost 8-bit microcontroller. The obtained device can correctly track the maximum power point even under abrupt changes in solar irradiance and improves the dynamic performance of the power converter that connects photovoltaic power plants into the ac grid. Indeed, traditional maximum power point tracking algorithms such as 'perturb & observe' and 'incremental conductance' are able to track the point of maximum power in most cases but they can fail under rapidity changing atmospheric conditions. The use of a microcontroller allows for easy updates and enhancement by simply adding code libraries. Furthermore, it can be interfaced via standard communication means to other control devices, integrated into control schemes and remote-controlled through its embedded web server. The proposed approach has been validated through experimental and simulated results.

Interaction analysis of ships mooring and maneuvering in harbors is pursued in this paper by using Bayesian probabilistic models. A number of ship-to-ship interactions are deduced from the navigation rules in port areas, and then used to train different Event-based Dynamic Bayesian Networks (E-DBNs). When data of two interacting ships are injected into the network, inference is performed in order to verify if the interaction between the vessels is known or not, and in the latter case actions to preserve the port safety can be taken. Results are drawn in the final part of the paper by including into the networks data provided by a simulator of realistic trajectories relative to an existing port.

This paper addresses the scientific challenges related to high level implementation strategies which steer the NEMO (Nucleus for European Modelling of the Ocean) code toward the effective exploitation of the opportunities offered by exascale systems. We consider, as case studies, two components of the NEMO ocean model (OPA-Ocean PArallelization): the Sea Surface Height equation solver and the Variational Data Assimilation module. The advantages rising from the insertion of consolidated scientific libraries in the NEMO code are highlighted: such advantages concern both the “software quality” improvement (see the software quality parameters like robustness, portability, resilience, etc.) and the reduction of time spent for software development and maintenance. Finally, we consider the Shallow Water equations as a toy model for NEMO ocean model to show how the use of PETSc objects predisposes the application to gain a good level of scalability and efficiency when the most suitable level of abstraction is used.

Business model innovation comprises many theoretical models, frameworks and methodologies; however, there is still little in the way of digital support for them using Computer Aided Design software. In this paper, we present an analysis of real-world usage data from the first generation of such tools that were designed to support the business model canvas. We first present how two artifacts implement features such as colors for grouping, custom attributes and positioning of elements. An examination of how digital support brings about new opportunities in business modeling allows us to compare this with a paper-based version. We then analyze how variations of these features have been used in the real world. Finally, we examine the implications of our observations for the next generation of tools and the advancement of research for dedicated Computer Aided Design tools to support strategic objects such as business models.

International audience; The initialization at the beginning of all type of simulation is recurring. In the field of multi-agents, spatial environments on a micro or macro scale, or more abstract context, such as mapping out energy sources for example, need often to be modelled. The initialization of these environments wherein evolve agents is generally tedious and time-consuming depending on the fineness of the mesh of the cutting in case of a 2D spatial representation. In this paper, we present MASC (Map Sectors Creator), a user-oriented tool that permits easy initialization through the creation of a mesh directly usable in multi-agents systems simulations platforms. The automation of cutting out maps followed by the generation of directly usable code snippets optimize the working time on initialization and allow to focus on the simulations results and observations.

International audience; We propose an effective method to compute the skypattern cubes thanks to a relaxation strategy in the pattern mining process. Our approach is based on the fact that each node of the cube can be approximated by the set of edge-skypatterns (a relaxed form of skypatterns) w.r.t. the whole set of measures M. Then we transform the problem into a skyline cube mining in |M| dimensions. The set of edge-skypatterns can be efficiently mined by using either a dynamic CSP method or an extended version of a static method based on the theoretical relationships between patterns and condensed representations of skypatterns. Experiments conducted on \dataset{UCI} datasets and on a real-life dataset (Mutagenicity) show the relevance and performance of our approach.

International audience; Contract Net Protocol (CNP) is probably the most widely used task allocation protocol in distributed multi-agent systems (MAS). However it is limited in some issues and has serious drawbacks if it is applied in such real world applications where temporal interaction aspects are of great importance and fault-tolerance is a crucial issue. Many researchers have proposed various methods to expand and to improve it but those challenges have not been much addressed. To cope with these limitations, this paper proposes a formal model that extends the conventional contract net with real time constraints, often defined as interaction duration and message deadlines, and fault tolerance to handle the agent death exception. In this study we concentrate on the reliability of the awarded contractor which may die while carrying out the assigned task. In the proposed approach a timeout mechanism is modeled to detect the crash failure of the contractor, hence a proper termination of the negotiation process can be timely performed by the manager ensuring the failure recovery. We model the extended CNP with timed colored Petri nets and show that it terminates correctly either in a safety case or in a failure situation. The model analysis by means of CPN tools proves that the protocol meets the key properties namely model correctness, deadline respect, absence of deadlocks and live locks, absence of dead code, agent terminal states consistency, concurrency and validity.