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With the increasing complexities, such as massive computing devices and strict requirements for collaboration, of industrial production systems, the concept of the cyber-physical production system (CPPS) is considered as a promising approach for addressing these challenges. However, programmability is a challenge in CPPSs. The traditional chimney style of programming requires considerable design effort from engineers to handle the spatiotemporal heterogeneity of cyber-physical model that encompasses computing, temporal, and geometric semantics along with physical dynamics. Thus, an easy-to-use programming model and an integrated programming framework that are suited to this context are required. However, the existing programming models typically fully consider only computing while only partially considering temporal characteristics, and they rarely consider geometric semantics. To solve this problem, this paper proposes a novel event-based programming model, GePro, considering the geometric spatial semantics to realize integrated programming and reduce design effort, especially the reconfiguration of adding new physical devices into the exist system. A prototype of GePro is implemented and verified based on IEC 61499 by the implementation of design and reconfigure assembly CPPS. The results show that using GePro results in a programming time savings of an development life cycle time compared to traditional models for reconfiguration. [ABSTRACT FROM AUTHOR]

High Performance Computing (HPC) delivers huge processing performance, and plays a key role in the national welfare and the people s livelihood. As the source of computation power, high performance processors mostly determine the overall performance of a HPC system, and are being intensively studied in many countries nowadays to maintain a competitive edge in the HPC domain. This paper provides a review of the state-of-the-art processor architectures from mainstream processor designers like NVIDIA，Intel, and AMD. We carry out the review in terms of computation resource organization, memory subsystem design, and the connection technology among multi-cores. Based on the analysis, we summarize the mainstream trends in high performance processor design. We believe that this paper can serve as a reference for future research on HPC oriented processor designs. [ABSTRACT FROM AUTHOR]

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance. [ABSTRACT FROM AUTHOR]

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is one of the successful extensions of the hesitant fuzzy linguistic term set used for describing the uncertain information in a more prominent manner for solving the group decision-making problems. In DHHFLTS, the membership functions are represented in terms of linguistic membership degrees which are a flexible structure for preference elicitation and enrich the ability for rational decision-making with complex linguistic expressions. Driven by the flexibility of DHHFLTS, in this paper, a new decision framework is developed for solving decision-making problems, which provides scientific and rational decisions based on the preference information. For it, initially, a new aggregation operator is proposed for aggregating decision-makers' preferences. Later, the importance of the attribute weights in the problems is determined by formulating a mathematical model and the COPRAS method is extended to the DHHFLTS context for prioritizing alternatives. The applicability of the presented approach is demonstrated through a numeric example related to green supplier selection. A comparative analysis with existing studies is also administered to test the effectiveness and verify the method. [ABSTRACT FROM AUTHOR]

This paper aims to offer a new group decision-making (GDM) method based on interval-valued intuitionistic fuzzy preference relations (IVIFPRs). To furnish this goal, a new additive consistency definition of IVIFPRs is first proposed. Then, a programming model is built to check the additive consistency of IVIFPRs. For incomplete IVIFPRs, two programming models are constructed, which aim at maximizing the consistency and minimizing the uncertainty of missing information. To achieve the minimum total adjustment, a goal programming model is established to repair inconsistent IVIFPRs. Considering the consensus, a programming model for improving the consensus degree is established, which permits different IVIFVs to have different adjustments and makes individual IVIFPRs have the smallest total adjustment to remain more original information. Based on these results, a consistency- and consensus-based GDM method is proposed. At length, a practical example for screening new majors of a private college in China is offered to illustrate the feasibility and efficiency of proposed method. [ABSTRACT FROM AUTHOR]

The Actor-based programming model is largely used in the context of distributed systems for its message-passing semantics and neat separation between the concurrency model and the underlying hardware platform. However, in the context of a single multi-core node where the performance metric is the primary optimization objective, the "pure" Actor Model is generally not used because Actors cannot exploit the physical shared-memory, thus reducing the optimization options. In this work, we propose to enrich the Actor Model with some well-known Parallel Patterns to face the performance issues of using the "pure" Actor Model on a single multi-core platform. In the experimental study, conducted on two different multi-core systems by using the C++ Actor Framework, we considered a subset of the Parsec benchmarks and two Savina benchmarks. The analysis of results demonstrates that the Actor Model enriched with suitable Parallel Patterns implementations provides a robust abstraction layer capable of delivering performance results comparable with those of thread-based libraries (i.e. Pthreads and FastFlow) while offering a safer and versatile programming environment. [ABSTRACT FROM AUTHOR]

For commuters, the workplace is the second most important place to be able to charge electric vehicles after their own residence. In order to satisfy the charging demand near to the workplaces of commuters, a location and capacity model for charging stations is set up with the aim of minimizing the overall cost, composed of the charging station construction cost and the generalized cost of users. The charging demand of commuting electric vehicles is divided into rigid demand and elastic demand, which depends on whether the electric vehicle has enough power to complete the rest of its daily travel requirements. By introducing a user convenience coefficient to quantify the user's charging expectation, the number of electric vehicles to be served and the demand for charging at the stations are obtained. Further, the optimal sites for charging stations and the quantities of different levels of charging facility equipped are determined by the location and capacity model. An example shows that, as the threshold of the convenience coefficient decreases, the demand for electric energy increases gradually, and the construction becomes less economical. Meanwhile, the location of the charging station is insensitive to the change of electric energy demand. The threshold value for the convenience coefficient is put forward for different planning purposes, providing a reference for planning site selection and quantity of chargers under elastic demand. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Existing programming models tend to tightly interleave algorithm and optimization in HPC simulation codes. This requires scientists to become experts in both the simulated domain and the optimization process and makes the code difficult to maintain or port to new architectures. In this paper, we propose the I N KS programming model that decouples these concerns with two distinct languages: I N KS pia to express the simulation algorithm and I N KS pso for optimizations. We define I N KS pia and evaluate the feasibility of defining I N KS pso with three test languages: I N KS o / C + + , I N KS o / loop and I N KS o / XMP . We evaluate the approach on synthetic benchmarks (NAS and heat equation) as well as on a more complex example (6D Vlasov–Poisson solver). Our evaluation demonstrates the soundness of the approach as it improves the separation of algorithmic and optimization concerns at no performance cost. We also identify a set of guidelines for the later full definition of the I N KS pso language. [ABSTRACT FROM AUTHOR]

To ensure the reasonable application and perfect the theory of decision making with interval multiplicative preference relations (IMPRs), this paper continues to discuss decision making with IMPRs. After reviewing previous consistency concepts for IMPRs, we find that Krejčí's consistency concept is more flexible and natural than others. However, it is insufficient to address IMPRs only using this concept. Considering this fact, this paper researches inconsistent and incomplete IMPRs that are usually encountered. First, programming models for addressing inconsistent and incomplete IMPRs are constructed. Then, this paper studies the consensus of individual IMPRs and defines a consensus index using the defined correlation coefficient. When the consensus requirement does not satisfy requirement, a programming model for improving consensus level is built, which can ensure the consistency. Subsequently, a procedure for group decision making with IMPRs is offered, and associated examples are provided to specifically show the application of main theoretical results. [ABSTRACT FROM AUTHOR]

As data volumes grow rapidly, distributed computations are widely employed in data-centers to provide cheap and efficient methods to process large-scale parallel datasets. Various computation models have been proposed to improve the abstraction of distributed datasets and hide the details of parallelism. However, most of them follow the single-layer partitioning method, which limits developers to express a multi-level partitioning operation succinctly. To overcome the problem, we present the NDD (Nested Distributed Dataset) data model. It is a more compact and expressive extension of Spark RDD (Resilient Distributed Dataset), in order to remove the burden on developers to manually write the logic for multi-level partitioning cases. Base on the NDD model, we develop an open-source framework called Bigflow, which serves as an optimization layer over computation engines from most widely used processing frameworks. With the help of Bigflow, some advanced optimization techniques, which may only be applied by experienced programmers manually, are enabled automatically in a distributed data processing job. Currently, Bigflow is processing about 3 PB data volumes daily in the data-centers of Baidu. According to customer experience, it can significantly save code length and improve performance over the intuitive programming style. [ABSTRACT FROM AUTHOR]

Lattice QCD is a frontier scientific field for studying elementary particles by numerical simulation methods, which has become one of the major scientific research applications of supercomputers.With the rapid development of Chinese supercomputers, the LQCD softwares need to be refactored due to the limitation of its traditional programming model.The characteristics of scientific applications on super computers from the perspective of lattice QCD were reviewed.A novel programming model targeted to Chinese super computers was proposed to adapt large-scale scientific applications with big data processing, which is a promising development direction for the basic softwares of Chinese supercomputing ecosystem.

The subject of this paper is a program verification method that takes into account abortion caused by partial functions in program statements. In particular, boolean expressions of various statements will be investigated that are not well-defined. For example, a loop aborts if its execution begins in a state for which the loop condition is undefined. This work considers the program constructs of nondeterministic sequential programs and also deals with the synchronization statement of parallel programs introduced by Owicki and Gries [7]. The syntax of program constructs will be reviewed and their semantics will be formally defined in such a way that they suit the relational model of programming developed at Eőtvős Loránd University [3, 4]. This relational model defines the program as a set of its possible executions and also provides definition for other important programming notions like problem and solution. The proof rules of total correctness [2, 5, 8, 9, 7] will be extended by treating abortion caused by partial functions. The use of these rules will be demonstrated by means of a verification case study.

Consistency analysis is a crucial topic for preference relations. This paper studies the consistency of interval linguistic fuzzy preference relations (ILFPRs) using the constrained interval linguistic arithmetic and introduces a new consistency definition. Then, several properties of this definition are researched. Meanwhile, the connection between this concept and a previous one is discussed. Following this concept, programming models for judging the consistency and for deriving consistent ILFPRs are constructed, respectively. Considering the case that incomplete ILFPRs may be obtained, a programming model for obtaining missing judgments following the consistency discussion is built. Afterwards, the consensus for group decision making (GDM) is studied and a model for adjusting individual ILFPRs to reach the consensus threshold is established. Consequently, an interactive procedure for GDM with ILFPRs is presented. A practical problem is provided to illustrate the utilization of the new algorithm and comparative discussion is offered.

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Intuitionistic fuzzy multiplicative preference relations (IFMPRs), as an extension of multiplicative preference relations, can denote the decision‐makers' (DMs') preferred and nonpreferred degrees simultaneously. Just as any other type of preference relations, consistency is crucial to guarantee the rational ranking orders. Thus, this paper introduces a new consistent concept for IFMPRs that is a natural extension of crisp case and overcomes the issues in the previous concepts of consistency. To judge the consistency of IFMPRs, several programming models are constructed, and an approach to deriving completely consistent IFMPRs is presented. Considering incomplete case, consistency‐based models are built to determine missing values that can address incomplete IFMPRs with the ignored objects, namely, all information for them is unknown. After that, group decision‐making with IFMPRs is studied. To measure the agreement degree between the DMs' individual IFMPRs, a new consensus index is defined, and an interactive algorithm to improve the consensus is offered. Based on the consistency and consensus analysis, a new method to group decision‐making with IFMPRs is developed. Finally, case studies are offered to show the application of the new procedure and to compare it with previous methods. [ABSTRACT FROM AUTHOR]

This paper reviews and analyzes several consistency concepts for interval fuzzy preference relations (IFPRs). On the basis of the comparisons, one can find that Krejčí's additive and multiplicative consistency concepts are more reasonable and flexible. Considering the issues that previous methods cannot fully address regarding incomplete and inconsistent IFPRs, this paper studies incomplete and inconsistent IFPRs using custom programming models based on Krejčí's concepts. Meanwhile, programming models for judging the additive and multiplicative consistency of IFPRs are constructed, respectively. Considering the consensus of IFPRs in group decision-making, a consensus index is defined, and programming models for improving the consensus levels of individual IFPRs are built. On the basis of the consistency and consensus analysis, two consistency-based algorithms for group decision-making with inconsistent and incomplete IFPRs are offered. One method is based on Krejčí's additive consistency concept, and the other uses Krejčí's multiplicative consistency concept. Meanwhile, associated examples are provided to show the application of the new methods. [ABSTRACT FROM AUTHOR]

To address the preferred and nonpreferred degrees of linguistic variables, this paper introduces intuitionistic linguistic preference relations (ILPRs) that apply intuitionistic linguistic variables (ILVs) to denote the decision makers' preferences. To judge the consistency of ILPRs, a consistent concept is introduced, and a consistency index is defined. When ILPRs are unacceptably inconsistent, a method to improve the consistency is introduced. Then, an approach to rank ILVs is introduced. In some situations where ILPRs might be incomplete, the consistency‐based linear programming model is constructed to evaluate the missing values. Considering group decision making, a group consensus index is defined, and its several desirable properties are discussed. Meanwhile, an acceptability‐based consistency and consensus approach is developed, and the associated example is offered to show the efficiency of the proposed procedure. [ABSTRACT FROM AUTHOR]

Hesitant information is powerful and flexible to denote decision maker's judgments. Hesitant multiplicative preference relations (HMPRs) own the advantages of preference relations and hesitant fuzzy sets that permit the decision makers (DMs) to compare objects by using several values. Just as other types of preference relations, how to derive the priority weight vector is a crucial step. According to the principle of the consistency concept for multiplicative preference relations, this paper first introduces a new consistency concept for HMPRs, which avoids the disadvantages of the previous ones. Using the new concept, models to judge the consistency of HMPRs are built. Then, a consistency probability‐based method to derive the hesitant fuzzy priority weight vector from HMPRs is offered. Considering the incomplete case, consistency‐based programming models to determine the missing values are constructed. To address group decision making with HMPRs, a distance measure is defined to determine the weights of the DMs, and a consensus index is proposed. Then, a consistency and consensus‐based group decision‐making algorithm is performed. Finally, two practical examples, an investment problem and a water conservancy problem are offered to illustrate the feasibility and efficiency of the new algorithm. Comparison analysis from the numerical and theoretical aspects verifies the potential application of the new procedure. [ABSTRACT FROM AUTHOR]

Highlights • A new type of preference relations, LIFPRs, is introduced. • A natural additive consistency concept for LIFPRs is defined. • Models to judge the consistency and derive additively consistent LIFPRs are built. • Model to determine missing values is constructed. • A new method is provided that can address inconsistent and incomplete ILFPRs. Abstract Linguistic intuitionistic fuzzy sets can be regarded as a qualitative form of intuitionistic fuzzy sets. This type of fuzzy sets uses a linguistic membership degree and a linguistic non-membership degree to represent the qualitative preferred and non-preferred judgments of decision makers. Preference relation is a useful and efficient tool for decision making that only requires the decision makers to compare two objects at one time. Taking the advantages of linguistic intuitionistic fuzzy sets and preference relations, this paper introduces linguistic intuitionistic fuzzy preference relations (LIFPRs) and studies their application to decision making. To ensure the ranking of objects reasonably, an additive consistency concept is introduced, and several of its desirable properties are discussed. To cope with inconsistent and incomplete LIFPRs, programming model-based methods to derive additively consistent LIFPRs and determine missing values are constructed, respectively. Subsequently, an approach to multi-criteria decision making with LIFPRs is offered, and the application of the new approach is illustrated by using a decision-making problem about evaluating mobile phones. [ABSTRACT FROM AUTHOR]

Interval-valued hesitant fuzzy preference relations (IVHFPRs) are useful that allow decision makers to apply several intervals in [0, 1] to denote the uncertain hesitation preference. To derive the reasonable ranking order from group decision making with preference relations, two topics must be considered: consistency and consensus. This paper focuses on group decision making with IVHFPRs. First, a multiplicative consistency concept for IVHFPRs is defined. Then, programming models for judging the consistency of IVHFPRs are constructed. Meanwhile, an approach for deriving the interval fuzzy priority weight vector is introduced that adopts the consistency probability distribution as basis. Subsequently, this paper builds several multiplicative consistency-based programming models for estimating the missing values in incomplete IVHFPRs. A consensus index is introduced to measure the agreement degree between individual IVHFPRs, and a method for increasing the consensus level is presented. Finally, a multiplicative consistency-and-consensus-based group decision-making method with IVHFPRs is offered, and a practical decision-making problem is selected to show the application of the new method. [ABSTRACT FROM AUTHOR]

With the increasing complexities, such as massive computing devices and strict requirements for collaboration, of industrial production systems, the concept of the cyber-physical production system (CPPS) is considered as a promising approach for addressing these challenges. However, programmability is a challenge in CPPSs. The traditional chimney style of programming requires considerable design effort from engineers to handle the spatiotemporal heterogeneity of cyber-physical model that encompasses computing, temporal, and geometric semantics along with physical dynamics. Thus, an easy-to-use programming model and an integrated programming framework that are suited to this context are required. However, the existing programming models typically fully consider only computing while only partially considering temporal characteristics, and they rarely consider geometric semantics. To solve this problem, this paper proposes a novel event-based programming model, GePro, considering the geometric spatial semantics to realize integrated programming and reduce design effort, especially the reconfiguration of adding new physical devices into the exist system. A prototype of GePro is implemented and verified based on IEC 61499 by the implementation of design and reconfigure assembly CPPS. The results show that using GePro results in a programming time savings of an development life cycle time compared to traditional models for reconfiguration.

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.

The application of big data and artificial intelligence is promoted by data mining and intelligent analysis of a large number of static data using big data computing platform.In the face of the growing demand for real-time dynamic data processing generated by the Internet of things,dataflow computing has been gradually introduced into some big data processing platforms.Aiming at the programming model of data flow,the traditional software engineering design method for dataflow analysis and the structure definition and model reference provided by the current dataflow programming model for big data processing platform was compared,the differences and shortcomings were analyzed,and the main features and key elements of the dataflow programming model were summarized.The main methods of dataflow programming and the combination with the mainstream programming tools were analyzed,and the basic framework and programming mode of visual dataflow programming tools were presented according to the dataflow computing business requirements of big data processing.