A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GU...A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then two approaches are adopted to create GUI test sample cases. That is to say, an improved ant colony optimization (ACO) algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test. The sequence goes through all object event points in the event-flow graph. On the other hand, the spanning tree obtained by deep breadth-first search (BFS) approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test. Finally, these cases are applied to test the new GUI. Moreover, according to the above-mentioned model, a corresponding prototype system based on Microsoft UI automation framework is developed, thus giving a more effective way to improve the GUI automation test in Windows OS.展开更多
Accompanied by the advent of current big data ages,the scales of real world optimization problems with many decisive design variables are becoming much larger.Up to date,how to develop new optimization algorithms for ...Accompanied by the advent of current big data ages,the scales of real world optimization problems with many decisive design variables are becoming much larger.Up to date,how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation.So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics.As a branch of the swarm intelligence based algorithms,particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years.This reviewpaper mainly presents its recent achievements and trends,and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.展开更多
Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and...Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.展开更多
The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved i...The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.展开更多
Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original a...Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original article has been corrected.展开更多
文摘A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then two approaches are adopted to create GUI test sample cases. That is to say, an improved ant colony optimization (ACO) algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test. The sequence goes through all object event points in the event-flow graph. On the other hand, the spanning tree obtained by deep breadth-first search (BFS) approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test. Finally, these cases are applied to test the new GUI. Moreover, according to the above-mentioned model, a corresponding prototype system based on Microsoft UI automation framework is developed, thus giving a more effective way to improve the GUI automation test in Windows OS.
文摘Accompanied by the advent of current big data ages,the scales of real world optimization problems with many decisive design variables are becoming much larger.Up to date,how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation.So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics.As a branch of the swarm intelligence based algorithms,particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years.This reviewpaper mainly presents its recent achievements and trends,and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.
文摘Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.
基金Estonian Science Foundation grants PSG136,PRG632,PUT1170the University of Tartu(PLTOM20903)the European Regional Development Fund(Centre of Excellence EcolChange).
文摘The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.
文摘Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original article has been corrected.
