Study on the Competency Model Construction for Industrial Designers under Artificial Intelligence Technology

As artificial intelligence (AI) technology is gradually put into application in the design industry, higher requirements have been imposed on the competence of industrial designers in the market. This study explored the application of AI technology in the design field and its future development direction on the basis of literature research in order to figure out the competency factors of industrial designers under the background of AI technology. Expert research and interview were used to obtain expert reliability, followed by a questionnaire survey of designers and data analysis to verify the effectiveness of the competency model, based on which the competency model of industrial designers in the AI context has been finalized. The result shows that it is necessary for designers to be capable of integrating and reserving cross-domain knowledge due to high demands on the degree of recognition to innovation ability, knowledge, and professional capability, and on the innovation of both design thinking and design method. Under the background of AI, industrial designers are required to understand the basic background knowledge of AI, and to have the ability to finish information sorting and provide assistance in designing applying AI software, and the ability to cooperate with AI engineers. This competency model will not only promote the development of AI technology in the industrial design trade by imposing new demands on industrial designers but also point out the direction for the cultivation of design talents in institutions of higher learning

The Relationship Between Teacher Transformational Leadership and Students’ Motivation to Learn in Higher Education

This quantitative study sought to examine whether there existed relationships between teacher transformational leadership and students’ motivation to learn. In aggregate, 171 undergraduates recruited from a public Chinese university participated in the study through a random sampling. The participants were administered two instruments: the Multi-factor Leadership Questionnaire (MLQ) 5X-short to measure students’ perceptions of the teacher transformational leadership in the educational context and the Motivated Strategies for Learning Questionnaires (MSLQ) to measure students’ motivation to learn. The data collected were analyzed by Pearson’s correlation and multiple regressions using the Statistical Package for Social Sciences (SPSS). The results from the multiple regression analyses further verified the findings in the previous literature that teacher transformational leadership could contribute to the students’ increased motivation to learn. It is recommended in the study that professional development be the best practice to facilitate the problems and all the college teachers should attend the professional development about transformational leadership behaviors, and implement the newly-acquired knowledge and skills to elevate students’ motivation to learn

An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm