Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
With the rapid development and wide application ofmultimedia technology, the demand for the actual development ofmultimedia\nsoftware inmany industries is increasing.How to measure and improve the quality ofmultimedia software is an important problem\nto be solved urgently. In order to calculate the complicated situation and fuzziness of software quality, this paper introduced a\nsoftware quality evaluationmodel based on the fuzzymatter element by using amethod known as the fuzzymatter element analysis,\ncombined with the TOPSIS method and the close degree. Compared with the existing typical software measurementmethods, the\nresults are basically consistent with the typical softwaremeasurement results.Then, Pearson simple correlation coefficientwas used\nto analyse the correlation between the existing four measurement methods and the metric of practical experience, whose results\nshow that the results of software quality measures based on fuzzy matter element aremore in accordance with practical experience.\nMeanwhile, the results of this method are muchmore precise than the results of the other measurementmethods....
Business Process Modeling (BPM) is a mechanism that separates all business\naspects from the underlying technological and implementation features of a\nsystem. The aim is to capture an organizationâ??s processes and achieve its\nbusiness objectives. Currently, there are many solutions for Business Process\nModeling and Design offered by vendors. However, the selection of one solution\nor another by customers is usually conducted in an ad-hoc manner.\nGiven the underlying environment that a customer might have and their limitations,\nthere is no standard methodology that can help in the selection of\nthe most appropriate solution. This paper therefore highlights the key characteristics\nof BPM solutions in the market to facilitate an understanding of\nthe compatibility of a given solution with customerâ??s environments; hence,\ncustomers can then make informed decisions regarding their selections....
The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs. Existing\nGPU implementations have limited performance with compromising on productivity. HPGraph, our high-performance bulksynchronous\ngraph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to\ngeneralized sparse matrix operations on GPU as the backend. HPGraph strikes a balance between performance and productivity\nby coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model for\nusers to implement various graph algorithms with relatively little effort. We evaluate the performance of HPGraph for four graph\nprimitives (BFS, SSSP, PageRank, and TC). Our experiments show that HPGraph matches or even exceeds the performance of\nhigh-performance GPU graph libraries such as MapGraph, nvGraph, and Gunrock. HPGraph also runs significantly faster than\nadvanced CPU graph libraries....
Validation is critical to the success of software trustworthiness measurement.A large number of software trustworthiness measures\nare proposed; however, most of them are not validated from a theory perspective. Therefore, they lack theoretical foundation and\nwill induce unnecessary cost and useless calculation. In this paper, we bring measurement theory into software trustworthiness\nmeasurement, construct a source codes oriented software trustworthinessmeasure based on extensive structure in themeasurement\ntheory, and validate the developed measure by use of axiomatic approaches. Compared with some software trustworthiness\nmeasures that are already presented, this measure can evaluate software trustworthiness better from a theory perspective....
Today, more and more complex tasks are emerging. To finish these tasks within a reasonable\ntime, using the complex embedded system which has multiple processing units is necessary.\nHardware/software partitioning is one of the key technologies in designing complex embedded\nsystems, it is usually taken as an optimization problem and be solved with different optimization\nmethods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied\nand have the advantages of strong robustness and excellent global search ability. Due to the high\ncomplexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve\nthe problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied\nto hardware/software partitioning. In order to improve the performance of the BSO, we analyzed\nits optimization process when solving the hardware/software partitioning problem and found the\ndisadvantages in terms of the clustering method and the updating strategy. Then we proposed the\nimproved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting\nthe cluster points and improved the updating strategy by decreasing the number of updated individuals\nin each iteration. Based on the simulation methods which are usually used to evaluate the performance\nof the hardware/software partitioning algorithms, we generated eight benchmarks which represent\ntasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and\ntwo improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the\nhighest quality within the shortest running time among these algorithms....
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