BPEL is used for specifying Web services. In spite of numerous recent efforts in both statically analyzing service specifications and support for service execution, there is still an urgent need for quality assurance ...
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BPEL is used for specifying Web services. In spite of numerous recent efforts in both statically analyzing service specifications and support for service execution, there is still an urgent need for quality assurance for BPEL services in two aspects: (1) lack of tools, techniques to aid under-standing BPEL service specifications and execution in order to make informed decisions about the correctness of its observed functionality, (2) maneuverability in exploring a service execution to identify the source of an erroneous service. In this paper, we develop an Execution Analysis tool for BPEL (EA4B) to address both aspects. EA4B defines an execution log for BPEL. EA4B can read the execution log for post-execution debugging or for near real-time monitoring. EA4B provides an interactive GUI and can walk-through its execution. In addition, EA4B can be integrated with static analysis tools such as WSAT: Error traces generated by WSAT are translated to log files and visually displayed. EA4B provides an execution analysis tool to ease the understanding of a BPEL service and aid in development and debugging tasks.
Over the past decade, there is a growing interest in block-based programmingthat aims support early learning and teaching of programming for young children. Block-based programming is suitable for children aged betwe...
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Over the past decade, there is a growing interest in block-based programmingthat aims support early learning and teaching of programming for young children. Block-based programming is suitable for children aged between 8 to 12 years old who are too young to understand and learn the logic and scripting language in view of their low maturity level and lack of exposure to programming. However, the visual Integrated Development Environment (IDE) of block programming allows children aged between 8 to 12 years old to quickly grab the underlying logics, code the program and solve the given problem. this paper explores the learning of block programming using Scratch programming among schoolchildren between the ages of 9 to 11 years old. the school children are characterized into three categories who are (1) Australian kids based in Perth, (2) Malaysian kids based in Perth and (3) Malaysian kids based in Kuala Lumpur. these school children have been selected as they have been exposed to Scratch programmingthrough a week long session of programming camp. the preliminary analysis indicates that the children have enjoyed learning Scratch programing. they feel the language is fun and easy to learn. they are very interested to learn more and would eventually like to master the programming language. they look forward to enrolling to the Scratch programing classes in schools if it is being offered and even as programs conducted in external classes. the findings gathered are still preliminary in nature, therefore, a much-detailed study needs to be carried out before these findings can be generalized and concluded. the learning of programming like Scratch has indirectly promoted the learning of Science, Technology, Engineering and Mathematics (STEM) in Malaysia and Australia.
the research and development (R&D) project selection is concerned with how to evaluate and identify the best subset of projects under some resource constraints. this paper discusses a fuzzy multi-criteria evaluati...
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the research and development (R&D) project selection is concerned with how to evaluate and identify the best subset of projects under some resource constraints. this paper discusses a fuzzy multi-criteria evaluation approach for R&D project selection which uses algebraic operations of fuzzy variables to characterize the strength and weakness of each R&D alternatives. Furthermore, the fuzzy performance indexes can be obtained by aggregating the criteria weights and fuzzy ratings of alternatives. Finally, the expected values of fuzzy performance indexes are estimated through fuzzy simulation to rank these R&D alternatives. To illustrate the effectiveness of this method, a case study is presented to evaluate the competing software development projects.
the proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation through a family of non-squares approximators for critic adaptive solution ...
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the proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation through a family of non-squares approximators for critic adaptive solution of the Discrete algebraic Riccati Equation (DARE), associated withthe problem of Discrete Linear Quadratic Regulator (DLQR). the proposed method is evaluated in a multivariable dynamic system of 4th order with two inputs and it is compared with standard recursive least square algorithm.
Smartphones, PDA, Sensors, Actuators, Phidgets and Smart Objects (i.e. objects with processing and networking capabilities) are more and more present in everyday's life. Merging all these technologies withthe Int...
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Smartphones, PDA, Sensors, Actuators, Phidgets and Smart Objects (i.e. objects with processing and networking capabilities) are more and more present in everyday's life. Merging all these technologies withthe Internet is often described as 'Internet of things' (IoT). In the IoT vision, things around us provide a pervasive network of interacting and interconnected devices. However building IoT applications is a long and arduous work, reserved for specialists, requiring specific knowledge's in terms of network protocols and programming languages. the lack of widespread and easy-to-configure solutions is an obstacle for the development of this area. A universal framework, offering simplification and standardization, could facilitate the emergence of this promising field in terms of applications and business. IoT needs a solid foundation for rapid, simple development and deployment of new services. In this paper, we present D-LITe, a universal framework for building IoT applications over heterogeneous sets of small devices. D-LITe offers solutions for deploying application's logic, and executing it on Smart Objects despite their heterogeneity. An implementation of D-LITe on tiny devices, such as TelosB motes, allows to show that our framework is realistic even withthe constraints of such devices.
the mathematical modeling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of product. the literature ...
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the mathematical modeling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of product. the literature study demonstrates that conventional approaches such as statistical regression, response surface methodology, etc. requires physical understanding of the process for the erection of precise and accurate models. the statistical assumptions of such models induce ambiguity in the prediction ability of the model. Such limitations do not prevail in the nonconventional modeling approaches such as Genetic programming (GP), Artificial Neural Network (ANN), Fuzzy logic (FL), Genetic Algorithm (GA), etc. and therefore ensures trustworthiness in the prediction ability of the model. the present work discusses about the notion, application, abilities and limitations of Genetic programming for modeling of machining processes. the characteristics of GP uncovered from the current review are compared with features of other modeling approaches applied to machining processes.
In this paper, we introduce a Mixed Integer Linear programming (MILP) model to design an energy efficient cloud computing platform for Internet of things (IoT) networks. In our model, the IoT network consisted of four...
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In this paper, we introduce a Mixed Integer Linear programming (MILP) model to design an energy efficient cloud computing platform for Internet of things (IoT) networks. In our model, the IoT network consisted of four layers. the first (lowest) layer consisted of IoT devices, e.g. temperature sensors. the networking elements (relays, coordinators and gateways) are located within the upper three layers, respectively. these networking elements perform the tasks of data aggregation and processing of the traffic produced by IoT devices. the processing of IoT traffic is handled by Virtual Machines (VMs) hosted by distributed mini clouds and located within the IoT networking elements. We optimized the number of mini clouds, their location and the placement of VMs to reduce the total power consumption induced by traffic aggregation and processing. Our results showed that the optimal distribution of mini clouds in the IoT network could yield a total power savings of up to 36% compared to processing IoT data in a single mini cloud located at the gateway layer.
Modern equipment such as unmanned CNC has replaced traditional manual operations, which would greatly increase the production capacity of the production line. the key factor determining the efficiency of a manufacture...
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ISBN:
(数字)9781728162973
ISBN:
(纸本)9781728162980
Modern equipment such as unmanned CNC has replaced traditional manual operations, which would greatly increase the production capacity of the production line. the key factor determining the efficiency of a manufacture system is the operating logic. therefore, studying this topic is of great significance to industries. this paper firstly introduces a specific smart manufacture system and analysis the factors which would influence the efficiency. then 0-1 integer programming is adopted to find the optimal queue. Because of the complexity of the model is too high, Monte Carlo and some more limitations are applied to the independent variable matrix to simplify the model. At last, the simulation based on greedy algorithm is designed to find the optimal dynamic scheduling strategy and one overall empirical formula is generated through it.
the paper proposes a semi-naive method for processing recursive loops in the dependency graph for a given query. the process goes through two phases. During the expand phase answers are generated using translation to ...
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ISBN:
(纸本)9789549641332
the paper proposes a semi-naive method for processing recursive loops in the dependency graph for a given query. the process goes through two phases. During the expand phase answers are generated using translation to base conjunctions. Entries in recursive predicates with undistinguished arguments are also stored in the database for further processing. During the shrink phase the occurrences of recursive predicates in rule-bodies are replaced withthe answers already generated during the expand phase. thus, the whole rule-body becomes a base conjunction, which generates new answers. the proposed method is suitable for queries with bound arguments, it reduces unnecessary computations and generates only facts relevant to the given query.
this investigation focuses on design-under-uncertainty problems that employ a probabilistic performance as objective function and consider its estimation through stochastic simulation. this approach puts no constraint...
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this investigation focuses on design-under-uncertainty problems that employ a probabilistic performance as objective function and consider its estimation through stochastic simulation. this approach puts no constraints on the computational and probability models adopted, but involves a high computational cost especially for design problems involving complex, high-fidelity numerical models. A framework relying on kriging metamodeling to approximate the system performance in an augmented input space is considered here to alleviate this cost. A sub region of the design space is defined and a kriging metamodel is built to approximate the system response (output) with respect to boththe design variables and the uncertain model parameters (random variables). this metamodel is then used within a stochastic simulation setting (addressing uncertainties in the model parameters) to approximate the system performance when estimating the objective function for specific values of the design variables. this information is then used to search for a local optimum within the previously established design sub domain. Only when the optimization algorithm drives the search outside this domain, a new metamodel is generated. the process is iterated until convergence is established and an efficient sharing of information across these iterations is established to adaptively tune characteristics of the kriging metamodel.
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