• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1310-1319
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• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.259-262
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• 2015

Due to the labor-intensiveness, high investment cost, long execution time, frequent change orders, and many stakeholders in a BIM project, a BIM manager is bound to face a lot of risks to make decisions in cost managing process. Since the productivity of a BIM team will affect the execution cost, this study investigates a simple method of assessing the productivity of a BIM team using the working timesheet records of the team. In this research, the productivity of a BIM team is defined as the effective working floor area (in square meters) of BIM uses per labor-time (in man-months) spent by the team. After the applicability of this method is tested by regression analysis using data from 5 real BIM projects in the construction phase, it is found that the simple productivity definition adopted in the method, although easy-to-implement, does not produce a statistically constant productivity value. More research is therefore needed in the future to devise better indicator(s) for assessing the productivity of a BIM team.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.681-693
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• 2020

This study sees a critical gap in the previous body of research, which it seeks to fill; the disclosure of the unemployment ratio correlation has only been measured by the level of economic growth. This study is to add investment variables and government expenditure variables that objectively aim to measure the level of effectiveness in handling the unemployment ratio, which is then a measurement of the effectiveness of unemployment. Economic growth is measured by its impact on income inequality through empirical, conceptual relationships as a critical review and economic strategy for the future. The research uses secondary data on Indonesian macro and microeconomics since 2003-2018, then testing uses a quantitative approach to correlation, regression, and scatterplot. The results of this study show correlations between variables, and volatiles on the graphs show a similar trend. In other words, variables are bound together and support each other. The strategy of prioritizing the scale of government expenditure and investment to reach the target is the primary concern, so that the economic cycle can be optimal and equipped to face the possibility of an economic recession in the future. Many factors cause complex income inequality, though investment does not show a correlation to income inequality.