Multi-criteria decision analysis deals with decision problems in which multiple criteria need to be considered. The criteria might be measured on different scales so that comparability is difficult. One approach to he...Multi-criteria decision analysis deals with decision problems in which multiple criteria need to be considered. The criteria might be measured on different scales so that comparability is difficult. One approach to help the user to organize the problem and to reflect on his or her assessment on the decision is Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH). Here the user needs to provide qualitative judgment about differences of attractiveness regarding pairs of options. MACBETH was implemented in the M-MACBETH software using the additive aggregation model. The present article introduces the software tool “AniFair” which combines the MACBETH approach with the Choquet integral as an aggregation function, because the Choquet integral enables the modeling of interaction between criteria. With the Choquet integral, the user can define constraints on the relative importance of criteria (Shapley value) and the interaction between criteria. In contrast to M-MACBETH, with every instance of “AniFair” the user is made available at least two aggregation level. “AniFair” provides Graphical User Interfaces for the entering of information. The software tool is introduced via an example from the Welfare Quality Assessment protocol for pigs. With this, “AniFair” is applied to real data that were collected from thirteen farms in Northern Germany by an animal welfare expert. The “AniFair” results enabled a division of the farms into five groups of comparable performance concerning the welfare principle “Good feeding”. Hereby, the results differed in how much the interaction between criteria contributed to the Choquet integral values. The shares varied from 5% to 55%. With this, the vulnerability of aggregation results towards relative importance of and interaction between criteria was stressed, as changes in the ranking due to the definition of constraints could be shown. All results were exported to human readable txt or csv files for further analyses, and advice could be given to the farmers on how to improve their welfare situation.展开更多
Prototyping is one of the core activities of User-Centered Design (UCD) processes and an integral component of Human-Computer Interaction (HCI) research. For many years, prototyping was synonym of paper-based mockups ...Prototyping is one of the core activities of User-Centered Design (UCD) processes and an integral component of Human-Computer Interaction (HCI) research. For many years, prototyping was synonym of paper-based mockups and only more recently we can say that dedicated tools for supporting prototyping activities really reach the market. In this paper, we propose to analyze the evolution of prototyping tools for supporting the development process of interactive systems. For that, this paper presents a review of the literature. We analyze the tools proposed by academic community as a proof of concepts and/or support to research activities. Moreover, we also analyze prototyping tools that are available in the market. We report our observation in terms of features that appear over time and constitute milestones for understating the evolution of concerns related to the development and use of prototyping tools. This survey covers publications published since 1988 in some of the main HCI conferences and 118 commercial tools available on the web. The results enable a brief comparison of characteristics present in both academic and commercial tools, how they have evolved, and what are the gaps that can provide insights for future research and development.展开更多
新类型新版本的手机应用数量与日俱增,使得传统的人工测试方法已经无法负荷,因此需要研究人员提出更加有效的自动化测试方法。在自动化测试的过程中,Android应用程序的GUI(Graphical User Interface),即图形用户界面,发挥着极其重要的作...新类型新版本的手机应用数量与日俱增,使得传统的人工测试方法已经无法负荷,因此需要研究人员提出更加有效的自动化测试方法。在自动化测试的过程中,Android应用程序的GUI(Graphical User Interface),即图形用户界面,发挥着极其重要的作用,GUI自动化测试凭借其出色的测试覆盖率和故障检测性能,成为研究人员的重点研究对象。文中对当前GUI自动化测试的相关研究进行梳理和总结,选取其中有代表性、普遍性的自动化测试框架进行详细剖析,从测试策略、探索策略、错误报告、是否支持重放、测试环境、支持的事件类型、是否使用APP源码、是否开源、系统事件识别方法几个方面来对挑选出的自动化测试工具进行相应的分类、分析与对比。同时选取部分有代表性的自动化测试框架进行对比实验,以探究测试效率以及各自的优缺点。最后提出当前研究所面临的挑战以及未来的发展前景。展开更多
With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing th...With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing the number of deep-learning framework users.However,to design a deep neural network,a considerable understanding of the framework is required.To solve this problem,a GUI(Graphical User Interface)-based DNN(Deep Neural Network)design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However,the existing GUI-based DNN design tool has certain limitations such as poor usability,framework dependency,and difficulty encountered in changing GUI components.In this study,a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover,the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency,we applied ONNX(Open Neural Network Exchange),which is an exchange standard for neural networks,and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally,to address the difficulty encountered in changing GUI components,we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.展开更多
文摘Multi-criteria decision analysis deals with decision problems in which multiple criteria need to be considered. The criteria might be measured on different scales so that comparability is difficult. One approach to help the user to organize the problem and to reflect on his or her assessment on the decision is Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH). Here the user needs to provide qualitative judgment about differences of attractiveness regarding pairs of options. MACBETH was implemented in the M-MACBETH software using the additive aggregation model. The present article introduces the software tool “AniFair” which combines the MACBETH approach with the Choquet integral as an aggregation function, because the Choquet integral enables the modeling of interaction between criteria. With the Choquet integral, the user can define constraints on the relative importance of criteria (Shapley value) and the interaction between criteria. In contrast to M-MACBETH, with every instance of “AniFair” the user is made available at least two aggregation level. “AniFair” provides Graphical User Interfaces for the entering of information. The software tool is introduced via an example from the Welfare Quality Assessment protocol for pigs. With this, “AniFair” is applied to real data that were collected from thirteen farms in Northern Germany by an animal welfare expert. The “AniFair” results enabled a division of the farms into five groups of comparable performance concerning the welfare principle “Good feeding”. Hereby, the results differed in how much the interaction between criteria contributed to the Choquet integral values. The shares varied from 5% to 55%. With this, the vulnerability of aggregation results towards relative importance of and interaction between criteria was stressed, as changes in the ranking due to the definition of constraints could be shown. All results were exported to human readable txt or csv files for further analyses, and advice could be given to the farmers on how to improve their welfare situation.
文摘Prototyping is one of the core activities of User-Centered Design (UCD) processes and an integral component of Human-Computer Interaction (HCI) research. For many years, prototyping was synonym of paper-based mockups and only more recently we can say that dedicated tools for supporting prototyping activities really reach the market. In this paper, we propose to analyze the evolution of prototyping tools for supporting the development process of interactive systems. For that, this paper presents a review of the literature. We analyze the tools proposed by academic community as a proof of concepts and/or support to research activities. Moreover, we also analyze prototyping tools that are available in the market. We report our observation in terms of features that appear over time and constitute milestones for understating the evolution of concerns related to the development and use of prototyping tools. This survey covers publications published since 1988 in some of the main HCI conferences and 118 commercial tools available on the web. The results enable a brief comparison of characteristics present in both academic and commercial tools, how they have evolved, and what are the gaps that can provide insights for future research and development.
文摘新类型新版本的手机应用数量与日俱增,使得传统的人工测试方法已经无法负荷,因此需要研究人员提出更加有效的自动化测试方法。在自动化测试的过程中,Android应用程序的GUI(Graphical User Interface),即图形用户界面,发挥着极其重要的作用,GUI自动化测试凭借其出色的测试覆盖率和故障检测性能,成为研究人员的重点研究对象。文中对当前GUI自动化测试的相关研究进行梳理和总结,选取其中有代表性、普遍性的自动化测试框架进行详细剖析,从测试策略、探索策略、错误报告、是否支持重放、测试环境、支持的事件类型、是否使用APP源码、是否开源、系统事件识别方法几个方面来对挑选出的自动化测试工具进行相应的分类、分析与对比。同时选取部分有代表性的自动化测试框架进行对比实验,以探究测试效率以及各自的优缺点。最后提出当前研究所面临的挑战以及未来的发展前景。
基金This research was supported by the KISTI Program(No.K-20-L02-C05-S01)the EDISON Program through the National Research Foundation of Korea(NRF)(No.NRF-2011-0020576).A grant was also awarded by the Ministry of Science and ICT(MSIT)under the Program for Returners for R&D.
文摘With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing the number of deep-learning framework users.However,to design a deep neural network,a considerable understanding of the framework is required.To solve this problem,a GUI(Graphical User Interface)-based DNN(Deep Neural Network)design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However,the existing GUI-based DNN design tool has certain limitations such as poor usability,framework dependency,and difficulty encountered in changing GUI components.In this study,a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover,the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency,we applied ONNX(Open Neural Network Exchange),which is an exchange standard for neural networks,and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally,to address the difficulty encountered in changing GUI components,we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.
