Deep Learning towards Expertise Development in a Visualization-based Learning Environment

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What do faculties specializing in brain and neural sciences think about, and how do they approach, brain-friendly teaching-learning in Iran?

Objective: to investigate the perspectives and experiences of the faculties specializing in brain and neural sciences regarding brain-friendly teaching-learning in Iran. Methods: 17 faculties from 5 universities were selected by purposive sampling (2018). In-depth semi-structured interviews with directed content analysis were used. Results: 31 sub-subcategories, 10 subcategories, and 4 categories were formed according to the “General teaching model”. “Mentorship” was a newly added category. Conclusions: A neuro-educational approach that consider the roles of the learner’s brain uniqueness, executive function facilitation, and the valence system are important to learning. Such learning can be facilitated through cognitive load considerations, repetition, deep questioning, visualization, feedback, and reflection. The contextualized, problem-oriented, social, multi-sensory, experiential, spaced learning, and brain-friendly evaluation must be considered. Mentorship is important for coaching and emotional facilitation

A Visual Programming Paradigm for Abstract Deep Learning Model Development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods

Envisioning Futures of Design Education: An Exploratory Workshop with Design Educator

The demand for innovation in the creative economy has seen the adoption and adaptation of design thinking and design methods into domains outside design, such as business management, education, healthcare, and engineering. Design thinking and methodologies are now considered useful for identifying, framing and solving complex, often wicked social, technological, economic and public policy problems. As the practice of design undergoes change, design education is also expected to adjust to prepare future designers to have dramatically different demands made upon their general abilities and bases of knowledge than have design career paths from years past. Future designers will have to develop skills and be able to construct and utilize knowledge that allows them to make meaningful contributions to collaborative efforts involving experts from disciplines outside design. Exactly how future designers should be prepared to do this has sparked a good deal of conjecture and debate in the professional and academic design communities. This report proposes that the process of creating future scenarios that more broadly explore and expand the role, or roles, for design and designers in the world’s increasingly interwoven and interdependent societies can help uncover core needs and envision framework(s) for design education. This approach informed the creation of a workshop held at the Design Research Society conference in Brighton, UK in June of 2016, where six design educators shared four future scenarios that served as catalysts for conversations about the future of design education. Each scenario presented a specific future design education context. One scenario described the progression of design education as a core component of K-12 curricula; another scenario situated design at the core of a network of globally-linked local Universities; the third scenario highlighted the expanding role of designers over time; and the final scenario described a distance design education context that made learning relevant and “close” to an individual learner’s areas of interest. Forty participants in teams of up to six were asked to collaboratively visualize a possible future vision of design education based on one of these four scenarios and supported by a toolkit consisting of a set of trigger cards (with images and text), along with markers, glue and flipcharts. The collaborative visions that were jointly created as posters using the toolkit and then presented by the teams to all the workshop participants and facilitators are offered here as a case study. Although inspired by different scenarios, their collectively envisioned futures of what design education should facilitate displayed some key similarities. Some of those were: Future design education curricula will focus on developing collaborative approaches within which faculty and students are co-learners; These curricula will bring together ways of learning and knowing that stem from multiple disciplines; and Learning in and about the natural environment will be a key goal (the specifics of how that would be accomplished were not elaborated upon.) In addition, the need for transdisciplinarity was expressed across the collaborative visions created by each of the teams, but the manner that participants chose to express their ideas about this varied. Some envisioned that design would evolve by drawing on other disciplinary knowledge, and others envisioned that design would gradually integrate with other disciplines

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy