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Deep learning technology is widely used in many modern fields and a number of models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems. In this paper, we propose swFLOW: a large-scale distributed framework for deep learning on Sunway TaihuLight. Based on the performance analysis results of convolutional neural network (CNN), we optimize the convolutional layer , and get 10.42× speedup compared to the original version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8 . Particularly, a decentralized implementation of distributed swFLOW system is presented to alleviate bottleneck of the central server. By using distributed swFLOW system, we can scale the batch size up to 4096 among 1024 concurrent processes for cancerous region detection algorithm . The successful application on swFLOW reveals the great opportunity for joint combination of deep learning and HPC system.

Edge computing technologies facilitate the deployment of services on nearby edge servers with a large number of end users and their mobile devices to fulfill personalized demands. Owing to frequent changes in user mobility and demands, service systems deployed in an edge–cloud environment must continuously adapt to ensure that the quality of service (QoS) perceived by the end users is maintained at a stable and satisfactory level. As it is difficult for system operation engineers to manually deal with such frequent and large-scale evolution due to problems of cost and efficiency, self-adaptation of the system is essential. In this paper, we present a programming framework for microservices (EPF4M) and an infrastructure for self-adaptive microservice systems (EI4MS) for the cloud–edge environment based on microservice architecture. Our study follows a “monitoring–analyzing–planning–execution” control loop that empowers the service systems to redeploy the services according to changes in the QoS. A two-phase strategy is adopted to minimize the side effects of the loop on the performance of the service system. A prototype of this framework and infrastructure has been open-sourced and verified through experiments conducted in a real cloud–edge environment. The results demonstrate the usefulness and advantages of our approach.

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning. One suitable method to directly measure the stopping power is proton computed tomography (pCT). Due to the proton interaction mechanisms in matter, pCT image reconstruction faces some challenges: the unique path of each proton has to be considered separately in the reconstruction process adding complexity to the reconstruction problem. This study shows that the GPU-based open-source software toolkit TIGRE, which was initially intended for X-ray CT reconstruction, can be applied to the pCT image reconstruction problem using a straight line approach for the proton path. This simplified approach allows for reconstructions within seconds. To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. Ordered-Subset Simultaneous Algebraic Reconstruction Technique (OS-SART) and Adaptive-Steepest-Descent Projection Onto Convex Sets (ASD-POCS) were used for image reconstruction. Since the accuracy of the approach is limited by the straight line approximation of the proton path, requirements for further improvement of TIGRE for pCT are addressed.

We present a new voice-based programming approach and software framework for collaborative robots (cobots) based on the Web Speech API, which is now supported by most modern browsers. The framework targets human programmable interfaces (HPIs), which can be used by people with little or no programming experience. The framework follows a meta-programming approach by enabling users to program cobots by voice in addition to using a mouse, tablet or keyboard. Upon a voice instruction (such as move, pick, release, etc.), the framework automates the manual tasks required to manipulate the vendor-provided HPI. The main advantages of this approach are simplified, guided programming, which only requires the knowledge of 5–10 voice instructions; increased programming speed by up to 46% compared to the manual approach; and the possibility of sharing programs as videos. The open-source framework was evaluated using two application scenarios.

Augmented reality (AR) is a technology that allows overlaying of virtual elements on top of the physical environment. This enhances the perception and conveys additional information to the user. With the emergence of industry 4.0 concepts in manufacturing landscape, AR found its way to improve existing Human-Machine Interfaces (HMI) on the shop-floor. The industrial setting has a wide variety of application opportunities from AR, ranging from training and digital work instructions to quality inspection and remote maintenance. Even though its implementation in the industry is rising in popularity, it is still mainly restricted to large companies due the limited availability of resources in Small and Medium Size Enterprises (SME). However, SMEs can benefit from AR solutions in its production processes. Therefore, this research aims to develop and present the results of comparison of two simple and cost-effective AR software frameworks for Hand Held Device (HHD) and a Head Mounted Device (HMD), which can be applied for developing AR applications for manufacturing. Two AR applications are developed using these software frameworks which are presented in the case study section. Android device is chosen as a HHD and HoloLens is the HMD used in the case study. The development structure can be reproduced by a wider range of enterprises with diverse needs and resource availability.

We present the scope, concepts, data structures and application programming models of the open-source Lattice Boltzmann library Palabos. Palabos is a C++ software platform developed since 2010 for Computational Fluid Dynamics simulations and Lattice Boltzmann modeling, which specifically targets applications with complex, coupled physics. The software proposes a very broad modeling framework, capable of addressing a large number of applications of interest in the Lattice Boltzmann community, yet exhibits solid computational performance. The article describes the philosophy of this programming framework and lists the models already implemented. Finally, benchmark simulations are provided which serve as a proof of quality of the implemented core functionalities.

In recent years, on-line processing of data streams (DaSP) has been established as a major computing paradigm. This is due mainly to two reasons: first, more and more data that are generated in near real-time need to be processed; the second reason is given by the need of efficient parallel applications. However, the above-mentioned areas expose a tough challenge over traditional data-analysis techniques, which have been forced to evolve to a stream perspective. In this work, we apply a novel multiple back end programming framework for stream data and task based parallelism to a multi-staged diffusion magnetic resonance imaging (MRI) toolkit, named pHARDI. The results demonstrate the benefits of using our framework in terms of performance and memory usage. The evaluation carried out also depicts that the speed-up of our parallel framework increases with the problem size. This work was supported by the EU project \ASPIDE: Exascale Programming Models for Extreme Data Processing" under grant 801091 and project TIN2016-79637-P \Towards unification of HPC and Big Data Paradigms" from the Spanish Ministry of Economy and Competitiveness of Spain. This research was partially supported by Madrid regional Government (Spain) under the grant \Convergencia Big data-Hpc: de los sensores a las Aplicaciones. (CABAHLA-CM)" Ref: S2018/TCS-4423.

In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.

In aiming to create added intelligence for Things interfacing with IoT principles in the form of Services, Smart Heating Management is consistently a field of promising research. In the currently demonstrated work, an approach will be described for an operational software framework of modular components that can create intelligence in Housing Units as those presented in our scenario. The framework is based on the lightweight Case Based Reasoning approach and principles in order to describe the Problem and Solution of heating management, as well as to extract knowledge from generic historical data. Collaboration between houses is included through the sharing of anonymized high level problem-solution data, as an instantiation of the same social learning principles that govern human behavior and enable Knowledge diffusion. The approach is validated through historical data acquired in two time periods from the Camden (London) community residencies and demonstrates an average of 22% savings in boiler active time. Knowledge sharing is also analyzed along with the benefits and pitfalls it might produce.

The goal of this work is an comprehensive experimental validation and verification of a computational CFD-DEM software platform. The software platform's modules and performance requirements specifically address the needs of pharmaceutical industry in terms of batch size (i.e., number of particles) and related phenomena (i.e., physical complexity). Moreover, we critically assessed the numerical models implemented. In addition to the review of relevant literature and selection of validation experiments, novel analytical solutions for the spray class in question are presented. As the presented method is independent of the spray model, the solution can be used to prove the correct implementation of various available spray models. The final outcome of this work is a validated software framework, which can be further used to investigate large-scale processes by resolving single particle trajectories in a coupled environment, including heat, mass and momentum transfer.

Crime has been and still is one of the major threats and issues for every government and society around the world. Nowadays, a new generation of (cyber)criminals exploits modern and state-of-the-art technology and tools, especially social media channels, in order to achieve their malicious purposes. Thus, Law Enforcement Agencies (LEAs), analysts and security practitioners are facing the need to engage new methods and tools which can support them in the fight and prevention of crime by minimizing the prediction and response time. This paper focuses on two specific functionalities of a crime prediction and fighting software framework which assists LEAs to adopt and utilize emerging technologies such as Data mining and Big Data tools, Semantic Analysis, Visual Intelligence and more in the context of their everyday operations. In this light, the authors of this paper present an innovative Semantic Engine with a person fusion tool as well as an ontology visualization tool as parts of a discussed framework. Both of these tools are presented in detail in separate sections so that the readers can understand better their architecture and their integration into this common framework alongside with other tools. These two tools use state-of-the-art components and software libraries in order to offer robust and future proof solutions for LEAs. Also, they can be integrated and expanded in future research and commercial projects both as independent tools as well as parts of other integrated frameworks.

For a long time, the embedded industry has been constrained by CPU performance, ROM and RAM capacity and other factors, so the software scale cannot be built too large. The new embedded designers often overturn the original version because they cannot comprehend the previous code. The lack of software framework standard leads to redundant and useless work. By making the analogy to the hierarchical idea of Android architecture, this paper proposes a simple and practical software architecture suitable for the embedded industry. The architecture adopts hierarchical design and each module operates independently, thus realizes high cohesion and low coupling of the system. The C structure variable is used to simulate the properties of the class, and the function pointer is used to replace the function in the class. By doing this, embedded programming changes from process-oriented to object-oriented. In addition, the architecture provides a registration mechanism to solve the upload of high-speed, real-time changing data.

The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost.

This paper presents a comprehensive methodology for the investigation of SSTI in AC/DC transmission networks. The main advantage of the proposed methodology is the modular modeling approach, where various complex components can be modeled in a comprehensive way while at the same time preserving the level of detail needed to capture the relevant dynamics. This is done by enhancing the CCM with an algorithm for the formulation of network state equations. All power system component models necessary for SSTI analysis are developed separately using the CCM principles and are connected according to their topological relations. A small-signal model of a point-to-point MMC-based VSC HVDC is developed to assess its influence on SSTI. The proposed methodology together with the developed models are implemented in a software framework called SNAP. The proposed methodology and the developed models are validated against time-domain simulations in PSS®NETOMAC.

The features of modern collaborative robots, mainly their kinematic redundancy combined with the light-weight structure, can be fully exploited in parts assembly. Traditional robot-level paradigm to robot programming, that requires to explicitly specify the motion of the robot and allows to use contact forces for motion supervision only, cannot be easily applied to complex interaction tasks, such as robotic assembly. Instead, by shifting paradigm to skill-based programming, it is possible to specify force control actions at task level and inherently provide compliant capabilities, without the need to specify the motions of the robot. To this end, this paper presents a constraint-based programming framework for the implementation of assembly skills for light-weight redundant robots, enabling a reactive generation of motion trajectories based on force control requirements. The effectiveness of the proposed approach is experimentally validated on a bimanual assembly use case performed with the ABB YuMi dual-arm robot, requiring a peg-in-hole insertion and a cap-rotation task. Estimation of the interaction force/torque additionally enables the execution of the assembly operation without the need for exteroceptive sensors.

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities.

When considering the complex problem of developing new multifunctional structures, it is essential to narrow the vast design space and reduce the infinite number of possible solutions to a finite subset of feasible designs. Nature provides examples of ramified, or branched, topologies that form non-intuitive solutions to various structural design problems. This work focuses on the development of a bio-inspired topology optimization framework that couples genetic algorithms with a parallel rewriting system known as a Lindenmayer System (or L-System), which acts as an analogy to the evolutionary process and formalizes the encoding of a 2-D structure. Example design problems and the solutions determined using this novel framework are presented and compared to ideal solutions, where it is shown that a family of branched solutions allowed to evolve over generations can eventually arrive at effective multifunctional structures. Select designs from each example problem are also thickened into 3-D bodies, which are assessed experimentally via fully functional prototypes, demonstrating that the L-System framework is capable of generating realistic solutions for multifunctional structure development.

Numerous modern applications, from social networking to astronomy, need efficient answering of queries on spatial data. One such query is the All k Nearest-Neighbor Query, or k Nearest-Neighbor Join, that takes as input two datasets and, for each object of the first one, returns the k nearest-neighbors from the second one. It is a combination of the k nearest-neighbor and join queries and is computationally demanding. Especially, when the datasets involved fall in the category of Big Data, a single machine cannot efficiently process it. Only in the last few years, papers proposing solutions for distributed computing environments have appeared in the literature. In this paper, we focus on parallel and distributed algorithms using the Apache Hadoop framework. More specifically, we focus on an algorithm that was recently presented in the literature and propose improvements to tackle three major challenges that distributed processing faces: improvement of load balancing (we implement an adaptive partitioning scheme based on Quadtrees), acceleration of local processing (we prune points during calculations by utilizing plane-sweep processing), and reduction of network traffic (we restructure and reduce the output size of the most demanding phase of computation). Moreover, by using real 2D and 3D datasets, we experimentally study the effect of each improvement and their combinations on performance of this literature algorithm. Experiments show that by carefully addressing the three aforementioned issues, one can achieve significantly better performance. Thereby, we conclude to a new scalable algorithm that adapts to the data distribution and significantly outperforms its predecessor. Moreover, we present an experimental comparison of our algorithm against other well-known MapReduce algorithms for the same query and show that these algorithms are also significantly outperformed.

The HIFI data processing pipeline was developed to systematically process diagnostic, calibration and astronomical observations taken with the HIFI science instrumentas part of the Herschel mission. The HIFI pipeline processed data from all HIFI observing modes within the Herschel automated processing environment, as well as, within an interactive environment. A common software framework was developed to best support the use cases required by the instrument teams and by the general astronomers. The HIFI pipeline was built on top of that and was designed with a high degree of modularity. This modular design provided the necessary flexibility and extensibility to deal with the complexity of batch-processing eighteen different observing modes, to support the astronomers in the interactive analysis and to cope with adjustments necessary to improve the pipeline and the quality of the end-products. This approach to the software development and data processing effort was arrived at by coalescing the lessons learned from similar research based projects with the understanding that a degree of foresight was required given the overall length of the project. In this article, both the successes and challenges of the HIFI software development process are presented. To support future similar projects and retain experience gained lessons learned are extracted., Comment: 18 pages, 5 figures

Multiple-choice examinations offer the ability to grade quickly as well as being able to assess concepts and understanding in a wide range of subjects. Consequently, many large classes use multiple-choice examinations. One problem, however, is that multiple-choice examinations are more prone to cheating than constructed-response style examinations. Multiple-choice examinations offer limited answer options, and these limited options can lead to sharing answers through collusion or gleaning answers from unwitting peers. To counter such cheating, this paper investigates a personalization approach to examinations whereby every student gets their own version of the examination that is different to the rest of their peers. Such personalization approach not only counters cheating, but also encourages students to focus on concepts rather than just answers. A software framework that facilitates generating personalized examination papers is developed, and the paper reports on the experience of using the approach in large classes. It discusses the administrative, technical, and pedagogical challenges posed by personalization and how these challenges might be overcome using the framework as well as accompanying processes. Surveys indicate that both students and staff are positive about using such a system.

Many of the world's greatest challenges are complex socio-environmental problems, often framed in terms of integrated assessment, resilience or sustainability. To resolve any of these challenges, it is essential to elicit and integrate knowledge across a range of systems, informing the design of solutions that take into account the complex and uncertain nature of the individual systems and their interrelationships. To meet this scientific challenge, we propose a tiered, system-of-systems modeling framework with these elements: a component-based, software framework that couples a wide range of relevant systems using a modular, system-of-systems structure; a tiered structure with different levels of abstraction that spans bottom-up and top-down approaches; the ability to inform robust decisions in the face of deep uncertainty; and the systematic integration of multiple knowledge domains and disciplines. We illustrate the application of the framework, and identify research and education initiatives that are needed to facilitate its development and implementation.

A lack of consistent data, reliable analysis techniques, and user-friendly tools is inhibiting decision makers’ abilities to assess and evaluate the sustainability performance of manufacturing processes and systems. Recent integration of information technology with advanced manufacturing technologies has led to a new paradigm, termed smart manufacturing or Industry 4.0. To leverage this paradigm shift for the advancement of sustainable manufacturing, efforts have been made to characterize unit manufacturing processes (UMPs) for sustainability assessment and to develop frameworks, methodologies, and standards utilizing information modeling approaches. This paper proposes a grey box object-oriented software framework for sustainability assessment of complex manufacturing systems, utilizing the multiple inheritance principle for composition of UMP models. Information modeling guidelines based on the framework are provided for UMP model developers and UMP performance analysts to construct and analyze system models. To demonstrate the usefulness of the framework and guidelines, a prototype information model is developed for a hybrid manufacturing system composed of fused deposition modeling (an additive process) and CNC milling (a subtractive process).

The intention of this work is to show a prototyping path while developing control algorithms with special emphasis on a hardware platform and a software framework. In principle, both components are born separately so the integration may be complex. What’s more, the project concerns a real-time controlled system. Communication between the control object and the external computer takes place wirelessly. The control and communication tasks must ensure autonomous operation of the controlled object. An additional difficulty is the critical control time due to a choice of a controlled system. It is a two-wheeled unstable mobile vehicle used as a transporter. The work is a review of the software and hardware tools used successfully by the authors. Although the tools themselves are not enough, engineering skills are also welcome.

An algorithm implemented in an open-source python library was developed for building periodic grain boundary models in a universal fashion. The software framework, aimsgb, aims to generate tilt and twist grain boundaries from an input cubic or non-cubic crystal structure for ab-initio and classical atomistic simulation. It can output a coincidence site lattice (CSL) grain boundary for a cubic input structure and a non-CSL grain boundary for a non-cubic input structure. This framework has two useful features: (i) it can calculate all the CSL matrices for generating CSL from a given Sigma (Σ) value and rotation axis, allowing the users to build the specific CSL and grain boundary models; (ii) it provides a convenient command line tool to enable high-throughput generation of tilt and twist grain boundaries by assigning an input crystal structure, Σ value, rotation axis, and grain boundary plane. The developed algorithm in the open-source python library is expected to facilitate studies of grain boundary in materials science. The software framework is available on the website: aimsgb.org.

This study proposes equilibrium models under different behavioral assumptions of labor supply in a ride-sourcing market and then investigates the performance of surge pricing. A time-expanded network is first proposed to delineate possible work schedules of drivers. Based on the proposed network, we provide formulations and algorithms for both neoclassical and income-targeting hypotheses to characterize the labor supply of ride-sourcing drivers, i.e., their choices of work hours. We then investigate the impact of surge pricing using a bi-level programming framework, with the lower-level problem capturing the equilibrium work hour choices while the upper-level one representing revenue-maximizing surge pricing. Compared to static pricing, the platform and drivers are found to generally enjoy higher revenue while customers may be made worse off during highly surged periods. Lastly, a simple regulation scheme to reduce market power is discussed.

In the big data and Manufacturing 4.0 era, there is a growing interest in using advanced analytical platforms to develop predictive modeling approaches that take advantage of the wealthy of data available. Typically, practitioners have their own favorite methods to address the modeling task, as a result of their technical background, past experience or software available, among other possible reasons. However, the importance of this task in the future justifies and requires more informed decisions about the predictive solution to adopt. Therefore, a wider variety of methods should be considered and assessed before taking the final decision. Having passed through this process many times and in different application scenarios (chemical industry, biofuels, drink and food, shipping industry, etc.), the authors developed a software framework that is able to speed up the selection process, while securing a rigorous and robust assessment: the Predictive Analytics Comparison framework (PAC). PAC is a systematic and robust framework for model screening and development that was developed in Matlab, but its implementation can be carried out on other software platforms. It comprises four essential blocks: i) Analytics Domain; ii) Data Domain; iii) Comparison Engine; iv) Results Report. PAC was developed for the case of a single response variable, but can be extended to multiple responses by considering each one separately. Some case studies will be presented in this article in order to illustrate PAC's efficiency and robustness for problem-specific methods screening, in the absence of prior knowledge. For instance, the analysis of a real world dataset reveals that, even when addressing the same predictive problem and using the same response variable, the best modeling approach may not be the one foreseen a priori and may not even be always the same when different predictor sets are used. With an increasing frequency, situations like these raise considerable challenges to practitioners, underlining the importance of having a tool such as PAC to assist them in making more informed decisions and to benefit from the availability of data in Manufacturing 4.0 environments.

It is largely recognized that the architectures of embedded systems are becoming more and more complex both at hardware and software levels. Despite the significant advances in the development tools, developing the software of such systems while ensuring their safety is still a difficult task. In this paper, we propose an end-to-end programming framework to ease the development of safe software systems. The programming framework, supported by a proper methodology and workflow, make it possible to design safe/secure software that implements functional requirements while respecting multiple non-functional requirements and mastering architectural complexity, time-to-market and cost. The programming framework is based on five concepts: (1) model-based system engineering: MBSE, (2) design-by-contract approach, (3) formal analysis of models based on symbolic execution, (4) code generation, and (5) static and dynamic code analysis. The effectiveness of the methodology has been demonstrated through multiple use-cases. The framework is realized using CEA LIST ( http://www-list.cea.fr/en/ ) open-source development platforms: Papyrus, Frama-C, and UNISIM-VP. These platforms are results of many research and industrial projects such as FP7-SafeAdapt 1 , FUI-EQUITAS 2 , FP7-STANCE 3 , CATRENE-OpenES 4 , FSN-SESAM Grids 5 , and H2020-VESSEDIA 6 .

Despite significant progress over the past two decades, numerous economic, technical and non-technical challenges have hampered the deployment of carbon capture and storage (CCS) technologies. Recent advances in integrated assessment software have provided powerful new decision support tools to help overcome such challenges and to better evaluate investment and other risks in the integrated capture, transport and storage system. Specifically, the SimCCS software framework provides novel decision support capabilities for CCS project development. In this paper, we illustrate how the development of a new online science gateway platform of the SimCCS software termed SimCCS Gateway is now rapidly expanding the accessibility of this powerful tool. Applications in the SimCCS Gateway platform have been designed to facilitate engagement across the entire CCUS community, including commercial project developers, researchers in the technical and policy spheres, educational users (higher education and K-12) and the general public. The Gateway is providing an innovative new approach to technology transfer as well as outreach to communities in which CCS projects are being proposed and planned. Current applications include projects throughout the US and China, but nations such as Canada and Australia are poised for future implementation owing to availability of key datasets. In this way, the SimCCS Gateway software platform aims to provide a valuable new tool to facilitate the rapid deployment of CCS technologies around the globe.

The combination of the Internet of Things (IoT) with Serious Games is an academic domain of research increasing in popularity. Combining Serious Games with the interconnected hardware and middleware driven ecosystem of IoT develops data-driven games that source data from the local or extended physical environment to progress in the virtual environment of gaming, better-informing players through the game experience. The following paper presents the evaluation of a software framework defined for combining Serious Games and IoT, achieved using a semester-long experiment with a randomised control trial. The study aimed to produce an effective method of measuring student engagement using sensor-based information and a game environment but not improve the level of student engagement with their related academic programme. The results of the empirical experiment provide perceived evidences in the measure of engagement when compared to academic performance. The experiment results are not statistically significant due to the small sample size and the empirical nature of the work. The findings of this paper validate the effectiveness of a software framework for combining Serious Games and IoT and encourages new real-world applications of Smart Serious Games that include healthcare, education, simulation and others.

We introduce in this paper a novel network DEA approach to determine a single and fair efficiency decomposition for series multi-stage processes. We provide direct comparisons with the bargaining approach of Zhou et al. (2013) [1]. First, we prove the equivalence of the proposed approach with the bargaining approach for series two-stage processes in which only the intermediate products are the outputs of the first stage and the inputs to the second one. Next, we reveal the inadequacy of the bargaining approach to be applied in series processes with more than two stages, as it yields multiple non-extreme efficiency decompositions instead of one. On the contrary, we show that our approach provides a single non-extreme efficiency decomposition. In addition to these contributions, our approach is novel in the sense that the obtained efficiency decomposition represents a fair compromise between the stages, as it is as close as possible to the maximum efficiency score and as far away as possible to the minimum efficiency score attained by each stage. To reach such a fair compromise among the stages we employ the weighted min-max method in a multi-objective programming framework, and we guide the search direction on the line that connects the ideal point with the nadir point. We provide graphical illustrations and comparisons based on data drawn from the literature as well as on synthetic data.

Cloud computing is the modern technology, that facilitates to the user various resources, data and files to access from anywhere that a network is available, it is today’s modern computing where we distribute our resources and software from one place to another, but still the security of the data is a major problem in the cloud. However, there is need of processing a huge number of data and maintained the security over cloud. In this research we proposed a software framework (hadoop) with the cloud which helps us to process the huge number of data by using map-reduce model. To add security we use Kerberos authentication protocol which helps us to enhance security level over the cloud and also it is used to provide authentication to the users. We analyze and compare our technique with existing techniques, the results shows that our technique is much more secure and provide user authentication.

15 pages, 9 figures, 5 tables, Optimization problems arise nowadays in all disciplines, not only in the scientific area but also in the field of engineering or economics, and in many others. Currently, challenging optimization problems require solution methods that consume a significant amount of computational resources. The application of High-Performance Computing techniques is a common approach to obtain efficient implementations in traditional parallel computing systems. However, more recent approaches are exploring distributed programming frameworks developed in recent years to achieve efficient computations on clusters and cloud systems. In this paper we present a parallel implementation of the enhanced Scatter Search metaheuristic using Spark. The parallel program was obtained as a particularization of a general software framework we developed to support different realisations of the Scatter Search metaheuristic. The aim of this paper is to provide helpful guidance to readers interested in applying, or developing their own, parallel metaheuristics to solve challenging problems in the Cloud. With the twofold objective of demonstrating the potential of the parallelization with Spark and also of studying the factors that influence the performance of the solution, the proposal has been thoroughly evaluated on two different platforms, a cluster and a cloud platform, using a representative set of parameter estimation problems in the field of Computational Systems Biology, This work was supported by the Ministry of Science and Innovation of Spain (DPI2017-82896-C2-2-R, TIN2016-75845-P and PID2019-104184RB-I00, AEI/FEDER/UE, 10.13039/501100011033); and by Xunta de Galicia and FEDER funds (Centro de Investigación de Galicia accreditation 2019-2022, ref. ED431G 2019/01, as well as Consolidation Program of Competitive Reference Groups, ref. ED431C 2017/04)

Allpix2 (read: Allpix Squared) is a generic, open-source software framework for the simulation of silicon pixel detectors. Its goal is to ease the implementation of detailed simulations for both single detectors and more complex setups such as beam telescopes from incident radiation to the digitised detector response. Predefined detector types can be automatically constructed from simple model files describing the detector parameters. The simulation chain is arranged with the help of intuitive configuration files and an extensible system of modules, which implement separate simulation steps such as realistic charge carrier deposition with the Geant4 toolkit or propagation of charge carriers in silicon using a drift–diffusion model. Detailed electric field maps imported from TCAD simulations can be used to precisely model the drift behaviour of charge carriers within the silicon, bringing a new level of realism to Monte Carlo based simulations of particle detectors. This paper provides an overview of the framework and a selection of different simulation modules, and presents a comparison of simulation results with test beam data recorded with hybrid pixel detectors. Emphasis is placed on the performance of the framework itself, using a first-principles simulation of the detectors without addressing secondary ASIC-specific effects., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 901, ISSN:0168-9002, ISSN:1872-9576

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes. Ministerio de Educación y Ciencia TIN2014-58304-R Junta de Andalucía P11-TIC-7529 Junta de Andalucía P12-TIC-1519

We live in a world were data are generated from a myriad of sources, and it is really cheap to collect and storage such data. However, the real benefit is not related to the data itself, but with the algorithms that are capable of processing such data in a tolerable elapse time, and to extract valuable knowledge from it. Therefore, the use of Big Data Analytics tools provide very significant advantages to both industry and academia. The MapReduce programming framework can be stressed as the main paradigm related with such tools. It is mainly identified by carrying out a distributed execution for the sake of providing a high degree of scalability, together with a fault-tolerant scheme. In every MapReduce algorithm, first local models are learned with a subset of the original data within the so-called Map tasks. Then, the Reduce task is devoted to fuse the partial outputs generated by each Map. The ways of designing such fusion of information/models may have a strong impact in the quality of the final system. In this work, we will enumerate and analyze two alternative methodologies that may be found both in the specialized literature and in standard Machine Learning libraries for Big Data. Our main objective is to provide an introduction of the characteristics of these methodologies, as well as giving some guidelines for the design of novel algorithms in this field of research. Finally, a short experimental study will allow us to contrast the scalability issues for each type of process fusion in MapReduce for Big Data Analytics.

With the richness of present-day hardware architectures, tightening the synergy between hardware and software has attracted a great attention. The interest in unified approaches paved the way for newborn frameworks that target hardware and software co-design. This paper confirms that a unified statistical framework can successfully classify algorithms based on a combination of the heterogeneous characteristics of their hardware and software implementations. The proposed framework produces customizable indicators for any hybridization of processing systems and can be contextualized for any area of application. The framework is used to develop the Lightness Indicator System (LIS) as a case-study that targets a set of cryptographic algorithms that are known in the literature to be tiny and light. The LIS targets state-of-the-art multi-core processors and high-end Field Programmable Gate Arrays (FPGAs). The presented work includes a generic benchmark model that aids the clear presentation of the framework and extensive performance analysis and evaluation., Comment: 20 Pages, 6 Figures, 5 Tables

In this paper a comprehensive residential Energy Management in python, rEMpy, is presented. The framework has a modular structure and the Optimal Scheduler, featuring a task scheduling logic and a configuration structure to represent different subsystems, is the core. A dynamic configuration of the system and data visualization is allowed by the Web Interface. The required forecasts are delegated to the Prediction module. Moreover, the real-time validation of the controlled systems and devices is supported thought the Fault Diagnosis and Overload Manager modules. The cooperation among the modules and the manager capabilities are validated by performing evaluations on both tasks scheduling and storage management on real-case data.

In this paper, we address the design of a communication library which particularly targets distributed iterative computing, including randomly executed asynchronous iterations. The well-known MPI programming framework is considered, upon which unique generic routines are proposed for both blocking and non-blocking communication modes. This allows for developing unique software applications to experiment both classical and asynchronous iterative methods through the same programming pattern. Convergence detection issues are investigated on pure algorithmic aspects, from which we provide an efficient exact approach to compute global convergence residual norms, by means of non-blocking synchronization. Point-to-point message reception and sending requests are carefully handled in view of producing the least possible delays about transmitted iterative data. Extensive experiments with an existing MPI-based scientific application validate both the proposed MPI-like programming framework and design options to achieve best performances of asynchronous iterative computing.

A stochastic programming framework is proposed for radiation therapy treatment planning. This framework takes into account uncertainty in setting up or positioning patients identically day-to-day during the treatment period. Because uncertainties are unavoidable, constraint violations are tolerated to some degree in practice. Under this assumption, a chance-constrained programming (CCP) framework is developed to handle setup uncertainties in treatment planning. The proposed framework can be employed under different distributional assumptions. The goal of the proposed approach is to maximize both the statistical confidence level of a treatment plan and the homogeneity of the dose distributions. This novel perspective provides a user-centric and personalized optimization model that allows a trade-off between sufficient tumor coverage and sparing healthy tissues under uncertainty. We describe testing of the performance of the proposed CCP models in terms of plan quality, robustness, and homogeneity and confidence level of the constraints using clinical data for a prostate cancer patient. Optimized CCP plans are also compared to plans developed using a deterministic approach that does not take uncertainties into account. Numerical experiments confirmed that the CCP is able to control setup uncertainties in target coverage and sparing of organs-at-risk.

Computational medicine more and more requires complex orchestrations of multiple modelling & simulation codes, written in different programming languages and with different computational requirements, which when validated need to be run many times on large cohorts of patients. The aim of this paper is to present a new open source software, the VPH Hypermodelling Framework (VPH-HF). The VPH-HF overcomes the limitations of most workflow execution environments by supporting both Taverna and Muscle2; the addition of Muscle2 support makes possible the execution of very complex orchestrations that include strongly-coupled models. The overhead that the VPH-HF imposes in exchange for this is small, and tends to be flat regardless of the complexity and the computational cost of the hypermodel being executed. We recommend the use of the VPH-HF to orchestrate any hypermodel with an execution time of 200 s or higher, which would confine the VPH-HF overhead to less than 10%. The VPH-HF also provide an automatic caching system over the execution of every hypomodel, which may provide considerable speed-up when the orchestration is run repeatedly over large numbers of patients or within stochastic frameworks, and the input sets are properly binned. The caching system also makes it easy to form large input set/output set databases required to develop reduced-order models, and the framework offers the possibility to dynamically replace single models in the orchestration with reduced-order versions built from cached results, an essential feature when the orchestration of multiple models produces a combinatory explosion of the computational cost.

Supply chain event management exploits synergies between IT and logistics and refers to the set of methods and technologies used to efficiently integrate events from all actors and processes of the supply chain. In the context of supply chain event management, we examine how events can be used to leverage situation awareness. In our approach, situation awareness is facilitated by providing the capability to detect situations, which are represented as correlations between simple events, complex events and supply chain objects (e.g., suppliers, 3PL companies, retailers and material resources). We introduce a two-phase event correlation method which first correlates simple events into complex events and then events with supply chain objects. We describe how the proposed model has been implemented in a software framework and we conduct evaluation tests to examine its situation detection capabilities.

International audience; In recent years, power control and resource allocation techniques for cellular communication systems are very active research areas. Power control is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. One of the most popular power control problems is based on maximizing the weighted sum of data rates under the peak power constraints for all users. It is a difficult nonconvex optimization problem for which standard approach Geometric Programming is not applicable in large scale setting. In this paper, we propose an efficient method based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), an innovative approach in nonconvex programming framework for solving this problem. The purpose is to develop fast and scalable algorithms able to handle large scale systems. The two main challenges in DC programming and DCA that are the effect of DC decomposition and the efficiency of solution methods to convex subproblems are carefully studied. The computational results on several datasets show the robustness as well as the efficiency of the proposed method in terms of both quality and rapidity, and their superiority compared with the standard approach Geometric Programming.

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management.

Robotic software frameworks simplify the development of robotic applications. The more powerful ones help to build such applications as a distributed collection of interoperating software nodes. The communications inside those robotic systems are amenable of being attacked and vulnerable to the security threats present on any networked system. With the robots increasingly entering in people’s daily lives, like autonomous cars, drones, etc. security on them is a central issue gaining attention. This paper studies several well known communication middleware used by robotic frameworks running on robots with regular computers, and their support for cybersecurity. It analyzes their performance when transmitting regular robotic data of different sizes, with or without security features, on several network settings. The experiments show that security, when available, does not significantly decrease the quality of the robotic data communication in terms of latency and packet loss rate.

Time-of-Flight 3D imaging, using the indirect measuring method that employs photonic mixing devices, increases in popularity. This is due to the recent availability of accurate and miniaturized Time-of-Flight cameras that can be integrated into small embedded devices. However, providing a system comprising a camera and hardware-accelerated processing, which is useable for various application types with diametrically opposed use-case requirements, is not trivial. Here we introduce a fast and flexible hardware/software co-processing framework for Time-of-Flight 3D imaging, which can be used for various kinds of use-case applications. This is achieved in particular by providing fine-grained hardware-acceleration for Time-of-Flight algorithms and computer vision applications. Furthermore, this hardware/software framework enables and features state-of-the-art multi-core, mixed-criticality, and real-time concepts, which is essential for critical applications in the automotive and industrial domains. We demonstrate the feasible implementation and integration of the framework by means of a mixed-critical automotive system demonstrator. This demonstrator enables various types of use-case scenarios, such as gesture recognition or indoor navigation, by employing the AURIX automotive processor from Infineon Technologies and the Xilinx Zynq platform. For instance, if a typical gesture recognition use-case is addressed, the framework is capable to provide high-quality depth images and 3D-point cloud data with nearly 100 FPS and introduces an average calculation error of only 0.08 mm. Most importantly, a high level of flexibility in software and hardware is preserved in order to support other use-case scenarios at the same time.

The ability to create and manage knowledge is commonly recognized as one the most important quality of the modern and innovative organization. The management of knowledge and skills, as well as the management of company competencies has turned out to be essential factors influencing performance of industrial processes. In the paper, the authors present the method and tools for competence modelling for enterprise knowledge management. Modelling competences as quantitative measures of knowledge workers performance provides a framework for effective human resource management within the knowledge based organisation. The authors explore possibilities of integrating existing competence description standards with mathematical methods of competence analysis into one common software framework. The product of this idea is the concept of reusable Competence Object Library.

In the recent years a tendency in information systems development has manifested itself to use a (meta)data framework. While using software frameworks (user interface elements and engines, whole functional subsystems) has been common practice for decades by now, it is only relatively recently that developers began more and more often to shift away from relying on static subject domain conceptual schemes. This is in response partly to the tendency of growth of subject domain model in conceptual diversity, and partly in attempt to capture the shifting nature of subject domains. In this paper we describe an architecture and supporting data structures for a reusable engine that allows describing concepts, storing data objects with respect to the conceptual structure, and controllable modification of data and concepts.

When designing production lines, a series of complex, interdependent phases need to be considered. These phases include the facility layout design, the allocation of tasks to available resources, the workload balancing as well as the validation of the proposed design against demand. This paper addresses all these phases in an integrated way, with the aim of reducing the overall time required to finalise the layout and production line design, while improving the overall performance of the manufacturing system under process and demand uncertainty, utilising a sophisticated, simulation-based software framework. The proposed approach is demonstrated using a realistic case scenario.

Today software systems play a critical role in society’s infrastructures and many are required to provide uninterrupted services in their constantly changing environments. As the problem domain and the operational context of such software changes, the software itself must be updated accordingly. In this paper we propose to support dynamic software updating through language semantic adaptation; this is done through use of micro-languages that confine the effect of the introduced change to specific application features. Micro-languages provide a logical layer over a programming language and associate an application feature with the portion of the programming language used to implement it. Thus, they permit to update the application feature by updating the underlying programming constructs without affecting the behaviour of the other application features. Such a linguistic approach provides the benefit of easy addition/removal of application features (with a special focus on non-functional features) to/from a running application by separating the implementation of the new feature from the original application, allowing for the application to remain unaware of any extensions. The feasibility of this approach is demonstrated with two studies; its benefits and drawbacks are also analysed.

Context In recent years, software environments such as the cloud and Internet of Things (IoT) have become increasingly sophisticated, and as a result, development of adaptable software has become very important. Self-adaptive software is appropriate for today's needs because it changes its behavior or structure in response to a changing environment at runtime. To adapt to changing environments, runtime verification is an important requirement, and research that integrates traditional verification with self-adaptive software is in high demand. Objective Model checking is an effective static verification method for software, but existing problems at runtime remain unresolved. In this paper, we propose a self-adaptive software framework that applies model checking to software to enable verification at runtime. Method The proposed framework consists of two parts: the design of self-adaptive software using a finite state machine and the adaptation of the software during runtime. For the first part, we propose two finite state machines for self-adaptive software called the self-adaptive finite state machine (SA-FSM) and abstracted finite state machine (A-FSM). For the runtime verification part, a self-adaptation process based on a MAPE (monitoring, analyzing, planning, and executing) loop is implemented. Results We performed an empirical evaluation with several model-checking tools (i.e., NuSMV and CadenceSMV), and the results show that the proposed method is more efficient at runtime. We also investigated a simple example application in six scenarios related to the IoT environment. We implemented Android and Arduino applications, and the results show the practical usability of the proposed self-adaptive framework at runtime. Conclusions We proposed a framework for integrating model checking with a self-adaptive software lifecycle. The results of our experiments showed that the proposed framework can achieve verify self-adaptation software at runtime.