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Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.

In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications., This project has received funding from the European Union’s Horizon 2020 research and innovation programme (Grant Agreement Number 814408)., info:eu-repo/semantics/publishedVersion

This report describes the history of the Monterey Workshop series from 1992 to 2020. The objective of the Monterey Workshop series since its inception has been to increase the practical impact of scientific methods in computer science and engineering. This report also describes the creation of the new Monterey workshop website and planning for the 21st Monterey Workshop. Prepared for: Naval Postgraduate School Research Sponsored Programs Office Approved for public release; distribution is unlimited.

Recently, an increasing number of cyber-attacks in the medical field has resulted in great losses in the health care industry, since medical information plays an essential role in human health. To introduce a comprehensive survey about possible cyber-attacks and solutions for these attacks, our paper first presents a brief overview of the necessary background of the dataflow in the medical domain and then identifies the vulnerabilities in each stage of the dataflow. Then, according to the weaknesses identified in the medical system, a classification of cyber-attacks is presented. Additionally, the paper presents research on previous work that focuses on solving these cyber-attacks and identifies the strengths and limitations of the solutions for each attack. More importantly, for data storage assurance, our paper discusses several cybersecurity architectures for the medical domain from the existing literature. The countermeasures from previous papers and architectures that are still weak in terms of resource depletion, attack reduction, applicability, etc. are addressed. Finally, the paper discusses and recommends solutions for future work to decrease cyber-attacks in the medical field so that human health can be guaranteed.

Development boards, Single-Board Computers (SBCs) and Single-Board Microcontrollers (SBMs) integrating sensors and communication technologies have become a very popular and interesting solution in the last decade. They are of interest for their simplicity, versatility, adaptability, ease of use and prototyping, which allow them to serve as a starting point for projects and as reference for all kinds of designs. In this sense, there are innumerable applications integrating sensors and communication technologies where they are increasingly used, including robotics, domotics, testing and measurement, Do-It-Yourself (DIY) projects, Internet of Things (IoT) devices in the home or workplace and science, technology, engineering, educational and also academic world for STEAM (Science, Technology, Engineering and Mathematics) skills. The interest in single-board architectures and their applications have caused that all electronics manufacturers currently develop low-cost single board platform solutions. In this paper we realized an analysis of the most important topics related with single-board architectures integrating sensors. We analyze the most popular platforms based on characteristics as: cost, processing capacity, integrated processing technology and opensource license, as well as power consumption (mA@V), reliability (%), programming flexibility, support availability and electronics utilities. For evaluation, an experimental framework has been designed and implemented with six sensors (temperature, humidity, CO2/TVOC, pressure, ambient light and CO) and different data storage and monitoring options: locally on a µSD (Micro Secure Digital), on a Cloud Server, on a Web Server or on a Mobile Application, This research was partially supported by the Centro Científico Tecnológico de Huelva (CCTH), University of Huelva

Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks—a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use. This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.

This report is the proceedings of the 20th Monterey Workshop on Cyber held at US Naval Postgraduate School in Nov. 27-29, 2018. Naval Postgraduate School Research Sponsored Programs Office Approved for public release; distribution is unlimited.

This work faces the problem of theElectronic System-Level(ESL) HW/SW co-design of dedicated electronic digital systems based on heterogeneous multi-processor architectures. In particular, the work presents a prototypeSystemC-based environment that exploits aDesign Space Exploration(DSE) approach able to suggest an HW/SW partitioning of the system specification and a mapping onto an automatically defined architecture. The descriptions of the reference HW/SW co-design methodology and the main design issues related to the developed DSE SW tools, supported by two reference use cases that allows to understand the role of the DSE step in the whole design flow, represent the core of the paper.

Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users’ needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the "in-platform" implementation of decentralized cognitive algorithms to the realization of smart data aggregations.

Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

Climate change represents a serious threat to the health of our planet and imposed a discussion upon energy waste and production. In this paper we propose a smart grid architecture relying on blockchain technology aimed at discouraging the production and distribution of non-renewable energy as the one derived from fossil fuel. Our model relies on a reverse application of a recently introduced attack to the blockchain based on chain forking. Our system involves both a central authority and a number of distributed peers representing the stakeholders of the energy grid. This system preserves those advantages derived from the blockchain and it also address some limitations such as energy waste for mining operations. In addition, the reverse attack we rely on allows to mitigate the behavior of a classic blockchain, which is intrinsecally self-regulated, and to trigger a sort of ethical action which penalizes non-renewable energy producers. Blacklisted stakeholders will be induced to provide their transaction with higher fees in order to preserve the selling rate.

This paper aims to take stock of recent advances in the field of energy-quality (EQ) scalable circuits and systems, as promising direction to continue the historical exponential energy downscaling under diminished returns from technology and voltage scaling. EQ-scalable systems explicitly trade off energy and quality at different levels of abstraction and sub-systems, dealing with “quality” as an explicit design requirement, and reducing energy whenever the application, the task, or the dataset allow quality degradation (e.g., vision and machine learning). A general framework for EQ-scalable systems based on the concept of quality slack is presented along with scalable architectures. A taxonomy of techniques to trade off energy and quality, a VLSI perspective, and possible quality control strategies are then discussed. The state of the art is surveyed to put the advances in its different sub-fields into a unitary perspective, emphasizing the on-going and prospective trends. At the component level, the generality of the EQ-scaling concept is shown through several examples, ranging from logic to analog circuits, to memories, data converters, and accelerators. Interesting implications of the joint adoption of EQ scaling and machine learning are also discussed, suggesting that their synergy gives ample room for further energy and performance improvements. From a level of abstraction viewpoint, EQ scaling is discussed from the circuit level to architectures, the hardware–software interface, the programming language, the compiler level, and run-time adaptation. Several case studies are discussed to put EQ scaling in the context of real-world applications.

The community accepted the need for a detailed simulation of main memory. Currently, the CPU simulators are usually coupled with the cycle-accurate main memory simulators. However, coupling CPU and memory simulators is not a straight-forward task because some pieces of the circuitry between the last level cache and the memory DIMMs could be easily overlooked and therefore not accounted for. In this paper, we take an approach to quantify the missing cycles in the main memory simulation. To that end, we execute a memory intensive microbenchmark to validate a simulation infrastructure based on ZSim and DRAMsim2 modeling an Intel Sandy Bridge E5-2670 system. We execute the same microbenchmark on a real Sandy Bridge E5-2670 machine identifying a missing 20 ns in the simulator measurements. This is a huge difference that, in the system under study, corresponds to one-third of the overall main memory latency. We propose multiple schemes to add an extra delay in the simulation model to account for the missing cycles. Furthermore, we validate the proposals using the SPEC CPU2006 benchmarks. Finally, we repeat the main memory latency measurements on seven mainstream and emerging computing platforms. Our results show that latency between the Last Level Cache (LLC) and the main memory ranges between tens and hundreds of nanoseconds, so we emphasize on properly adjust and validate these parameters in system simulators before any measurements are performed. Overall, we believe this study would improve main memory simulation leading to the better overall system analysis and explorations performed in the computer architecture community. This work was supported by the Collaboration Agreement between Samsung Electronics Co. Ltd. and BSC, Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272) and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.

New Software Development techniques with respect to cloud computing and eServices had modified IS architectures which were well established and consolidated in the past. The new methodologies of (software, micro/e) “Services” has pushed towards the adoption of software development organization independent from traditional tiered-architecture with the result of reducing both scale-up and down times as well as interruption times due to migration to different platforms. The eServices development organization relays on MicroServices architecture so that it is decomposing the legacy architecture in micro-components, each one with an independent life-cycle but interconnected and correlated, i.e. eServices. Each eService is hosted within a single container which has a proper software lifecycle and with minimal set of executable operating system libraries. The analysis goes into details about the structure and the development of eServices with MicroServices architecture. The paper discusses the new technological tendencies under the lens of an Organizational approach.

In order for a user to develop rich and valid knowledge structures, not only is considerable predefined content required, but other equally important types of knowledge as well. In other words, to display real understanding and to build and extend their knowledge structures, users have to call on several resources as well as representation, retrieval and construction mechanisms. Apart from the specific content, on which traditional educational environments focus on, a user depends heavily on general content knowledge and on systems of knowledge that is more abstract and elusive than typical content. In this article, we document our myCourse approach presenting three application scenarios. In these scenarios, knowledge creation is a process of emergence and co-evolution of the individual, the social group and the wider society.

In this paper, efficient solutions for requantization transcoding in H.264/AVC are presented. By requantizing residual coefficients in the bitstream, different error components can appear in the transcoded video stream. Firstly, a requantization error is present due to successive quantization in encoder and transcoder. In addition to the requantization error, the loss of information caused by coarser quantization will propagate due to dependencies in the bitstream. Because of the use of intra prediction and motion-compensated prediction in H.264/AVC, both spatial and temporal drift propagation arise in transcoded H.264/AVC video streams. The spatial drift in intra-predicted blocks results from mismatches in the surrounding prediction pixels as a consequence of requantization. In this paper, both spatial and temporal drift components are analyzed. As is shown, spatial drift has a determining impact on the visual quality of transcoded video streams in H.264/AVC. In particular, this type of drift results in serious distortion and disturbing artifacts in the transcoded video stream. In order to avoid the spatially propagating distortion, we introduce transcoding architectures based on spatial compensation techniques. By combining the individual temporal and spatial compensation approaches and applying different techniques based on the picture and/or macroblock type, overall architectures are obtained that provide a trade-off between computational complexity and rate-distortion performance. The complexity of the presented architectures is significantly reduced when compared to cascaded decoder-encoder solutions, which are typically used for H.264/AVC transcoding. The reduction in complexity is particularly large for the solution which uses spatial compensation only. When compared to traditional solutions without spatial compensation, both visual and objective quality results are highly improved.

In this paper, we examine spatial resolution downscaling transcoding for H.264/AVC video coding. A number of advanced coding tools limit the applicability of techniques, which were developed for previous video coding standards. We present a spatial resolution reduction transcoding architecture for H.264/AVC, which extends open-loop transcoding with a low-complexity compensation technique in the reduced-resolution domain. The proposed architecture tackles the problems in H.264/AVC and avoids visual artifacts in the transcoded sequence, while keeping complexity significantly lower than more traditional cascaded decoder---encoder architectures. The refinement step of the proposed architecture can be used to further improve rate-distortion performance, at the cost of additional complexity. In this way, a dynamic-complexity transcoder is rendered possible. We present a thorough investigation of the problems related to motion and residual data mapping, leading to a transcoding solution resulting in fully compliant reduced-size H.264/AVC bitstreams.

This paper presents a web-based assessment system, which supports parameterized questions, incorporating remote web services/applications, in order to use them during the evaluation process. The proposed system introduces a dynamic technique for creating a new type of parameterized web-based questions which mix static and dynamic content and thus, it operates as an integration framework, incorporating these remote web services/applications as part of the evaluation process. The technique is based on question templates, which contain the details of the remote services/applications that provide the correct answer in each question. The incorporation of remote web-based services into a parameterized assessment system, as part of the evaluation process, constitutes an advanced feature that provides new capabilities, such as flexibility and extensibility to any assessment system.

Remote access to real laboratories can enhance traditional educational paths with practical experiences. However, most of the existing remote laboratories cannot communicate with each other and they are not yet completely integrated with common educational platforms such as Learning Management Systems. These problems could be tackled by offering to end users remote experiments as distributed services using web service technology. The paper examines the architectures of remote laboratories developed by three institutions: DIBE ISILab (Internet Shared Instrumentation Laboratory), HPI DCL (Distributed Control Laboratory) and MIT iLab. Their front-end services are compared and discussed. The paper details how end-user applications interact with these remote labs and reports on the results of a preliminary test of interoperability between remote labs, providing a hint on how different remote experiments can be shared between different institutions.

Mobile digital TV environments demand flexible video compression like scalable video coding (SVC) because of varying bandwidths and devices. Since existing infrastructures highly rely on H.264/AVC video compression, network providers could adapt the current H.264/AVC encoded video to SVC. This adaptation needs to be done efficiently to reduce processing power and operational cost. This paper proposes two techniques to convert an H.264/AVC bitstream in Baseline (P-pictures based) and Main Profile (B-pictures based) without scalability to a scalable bitstream with temporal scalability as part of a framework for low-complexity video adaptation for digital TV broadcasting. Our approaches are based on accelerating the interprediction, focusing on reducing the coding complexity of mode decision and motion estimation tasks of the encoder stage by using information available after the H. 264/AVC decoding stage. The results show that when our techniques are applied, the complexity is reduced by 98 % while maintaining coding efficiency.

The distribution of services on multi-agent systems allows it to reduce to the agents their computational load. The functionality of the system does not reside in the agents themselves, however it is ubiquitously distributed so that allows you to perform tasks in parallel avoiding an additional computational cost to the elements in the system. The distribution of services that offers SCODA (Distributed and Specialized Agent Communities) allows an intelligent management of these services provided by agents of the system and the parallel execution of threads that allow to respond to requests asynchronously, which implies an improvement in the performance of the system at both the computational level as the level of quality of service in the control of these services. The comparison carried out in the case of study that is presented in this paper demonstrates the existing improvement in the distribution of services on systems based on SCODA.

ALAS-KA is a tool that extends the learning analytics features of the Khan Academy platform.ALAS-KA includes new visualizations for the entire class and individual students of more than 20 new learning indicators.ALAS-KA helps teachers to make decision supported by the high level information provided.ALAS-KA enables students to gain awareness of their learning process for self-reflection.ALAS-KA can be used by the course instructors to detect class tendencies and learner models. The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In this paper, we describe ALAS-KA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-KA, which have not been available previously in the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-KA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-KA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos III de Madrid (UC3M) and were taken by more than 300 students.

© 2015 Elsevier B.V. All rights reserved. Abstract The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard. This article presents DaSH - the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics., This work has been supported by the Spanish Severo Ochoa award SEV-2011-00067 and project TIN2012-34557 from the Ministry of Science and Innovation, by the Generalitat de Catalunya 2014-SGR-1051 award, the Spanish Ministry of Education (TIN2007-60625 and CSD2007-00050) and the RoMoL project (ERC Advanced Grant Agreement 321253). We thankfully acknowledge the Microsoft Research support through the BSC-Microsoft Research Center and the European Commission through the HiPEAC-3 Network of Excellence.

Please refer only to original source: Koper, R., Tattersall, C. (2004). New directions for lifelong learning using network technologies. British Journal of Educational Technology, 35 (6), 689-700.

Scalable video coding is a recent extension of the advanced video coding H.264/AVC standard developed jointly by ISO/IEC and ITU-T, which allows adapting the bitstream easily by dropping parts of it named layers. This adaptation makes it possible for a single bitstream to meet the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or quality scalability, combined or separately. Since the scalable video coding design requires scalability to be provided at the encoder side, existing content cannot benefit from it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to scalable video coding in baseline and main profile is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, time savings of around 63 % for baseline profile and 60 % for main profile are achieved while maintaining the coding efficiency.

Multiuser immersive learning scenarios hold strong potential for lifelong learning as they can support the acquisition of higher order skills in an effective, efficient, and attractive way. Existing virtual worlds, game development platforms, and game engines only partly cater for the proliferation of such learning scenarios as they are often inadequately tuned for learning. First, this study aims to identify architectures that more effectively support the development of multiuser immersive learning scenarios. Second, this study takes up the challenge to define and assemble more flexible architectures that cater for fast and easy development, which will become important in the current period of economic breakdown. Third, this study describes how such architectures should enable research into guidelines for multiuser immersive learning scenario design and development. This study outlines a method for defining and setting up such architectures by using experts and existing literature.

International audience; In the context of Component-based Programming, which addresses the implementation stage of a component-based software engineering development process, this paper describes a specification and an operational integration of an inheritance system into a self-contained new component-based programming language named Exil. Our proposal completes and extends related works by making it possible to apply inheritance to the full description of components, i.e. both to structural (description of provisions and requirements, of component architecture) and behavioral (full implementations of services) parts in component descriptions. Inheritance in Exil is designed to be used in conjunction with composition to maximize reuse capabilities and expressive power. Exil implementation proposes a clear operational solution for inheritance and for achieving and testing substitutions.

International audience; Many protocols and communication standard appear in the Substation Automation System (SAS) world. Advanced communication standards a low the exchange of peer to peer information between primary devices in order to accomplish distributed automation application inside SAS. Evaluating the conformity of primary devices towards security and SAS architectures towards required specifications is a non trivial task. For that, we propose a co-simulation approach based on hardware and software in the loop add-on modules. An architecture combining both actual and virtual devices is simulated using this approach and dynamic scenarios are constructed for generating transaction delay performance index. Finaly, we provide an adequate explanation about the Co Simulation outcomes according to the type of real devices and the configuration of dynamic scenarios.

peer-reviewed Service-based architectures have now become commonplace, creating the need to address their systematic maintenance and evolution. We propose to enable transformation-driven evolution for service architectures in a semi-automated fashion. In contrast to the existing solutions like service wrapping, migration or run-time adaptation etc., the proposal supports primitive and customisable architectural transformations to support an incremental evolution for service architectures. An empirical approach is adopted to investigate the extent to which the architecture evolution tasks (i.e. modeling, transformation and refinement) can be automated and validated in context to the central hypothesis for architecturecentric software evolution. Based on the initial results, we plan to proceed toward modeling and automating the architectural evolution in a formal way.

Personalized multimedia content which suits user preferences and the usage environment, and as a result improves the user experience, gains more importance. In this paper, we describe an architecture for personalized video adaptation and presentation for mobile applications which is guided by automatically generated annotations. By including this annotation information, more intelligent adaptation techniques can be realized which primarily reduce the quality of unimportant regions in case a bit rate reduction is necessary. Furthermore, a presentation layer is added to enable advanced multimedia viewers to adequately present the interesting parts of a video in case the user wants to zoom in. This architecture is the result of collaborative research done in the EU FP6 IST INTERMEDIA project.

The popularity of streaming multimedia services has greatly increased in recent years. Telco- and cable-providers have started offering a plethora of multimedia services in the access and aggregation network, including video on demand, interactive digital television, and time-shifted TV. However, these services introduce additional challenges, such as stringent time constraints, and high bandwidth requirements. To overcome these problems, we explore the advantages of delivering such multimedia content using deadline-aware scheduling and caching algorithms. These algorithms decide when to send and store which content. This enables the network to optimize bandwidth consumption and satisfy deadline constraints. The designed algorithm was evaluated and compared to classical deadline-unaware delivery protocols. This allows us to study the efficiency of the new algorithm, and identify the scenarios in which deadline-aware scheduling improves delivery of multimedia content.

The appropriate deployment of web service operations at the service provider site plays a critical role in the efficient provision of services to clients. In this paper, the authors assume that a service provider has several servers over which web service operations can be deployed. Given a workflow of web services and the topology of the servers, the most efficient mapping of operations to servers must then be discovered. Efficiency is measured in terms of two cost functions that concern the execution time of the workflow and the fairness of the load distribution among the servers. The authors study different topologies for the workflow structure and the server connectivity and propose a suite of greedy algorithms for each combination. Journal of Database Management

The basic problem of high-level synthesis is the mapping of a behavioral description of a digital system into an RTL design consisting of a data path and a control unit. The authors introduce the FSMD model, which forms the basis for synthesis. They discuss the main considerations in a high-level synthesis environment: the input description language, the internal representation, and the main synthesis tasks-allocation, scheduling, and binding. They conclude with some problems that must be solved to make high-level synthesis a widely accepted methodology. >

This special issue of the Journal of Interactive Media in Education centres around the book Learning Design: A Handbook on Modelling and Delivering Networked Education and Training Contributions cover topics such as the design of LD tools (editors and players), the role of ontologies and patterns in learning design, introducing LD in institutions and using LD with other specifications. Editors: Colin Tattersall and Rob Koper.

By means of a systemic approach toward analysis and design of complex systems, we introduce the issue of implementing open computational systems on service-oriented architectures. We start by studying general systems theory, as it accounts for analysis and modeling of complex systems, and then compare three different implementation strategies toward system implementation. As such, the comparison is grounded in the notion of supporting architectures and, more specifically, in the practical case of a service-oriented layered architecture for communicating entities (SOLACE). More material can be found on http://www.soclab.bth.se

We adopt the direction towards new educational and pedagogic paradigms, where learning is a process of emergence and co-evolution of the individual, the social group and the wider society. In this direction, Service-Oriented Architectures are becoming a popular system paradigm for e-learning. In this article, we present our research and development efforts to provide a social networking learning platform for developing services which address the personal learning needs of the users and enable them to create value. We also present the specific characteristics of our community driven service framework and discuss our approach in comparison to other similar approaches and frameworks.

International audience; Current systems aiming at engaging students in Self-Regulated Learning processes are often prompt-based and domain-dependent. Such metacognitive prompts are either difficult to interpret for novices or ignored by experts. Although domain-dependence per se cannot be considered as a drawback, it is often due to a rigid structure which prevents from moving to another domain. We detail here a system that addresses these limitations. This two-loop system provides texts to be learned through summarization. The first loop is called Reading, in which the student formulates a query and is provided with texts related to this query. Then the student judges whether each text presented could be summarized. The second loop is called Writing, in which the student writes out a summary of the texts, then gets an assessment from the system. In order to automatically perform various comprehension-centered tasks (i.e., texts that match queries, assessment of summaries), our system uses LSA (Latent Semantic Analysis), a tool devised for the semantic comparison of texts.