designing haptics is a difficult task especially when the user attempts to design a sensation from scratch. In the fields of visual and audio design, designers often use a large library of examples for inspiration, su...
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designing haptics is a difficult task especially when the user attempts to design a sensation from scratch. In the fields of visual and audio design, designers often use a large library of examples for inspiration, supported by intelligent systems like recommender systems. In this work, we contribute a corpus of 10 000 mid-air haptic designs (500 hand-designed sensations augmented 20x to create 10 000), and we use it to investigate a novel method for both novice and experienced hapticians to use these examples in mid-air haptic design. The RecHap design tool uses a neural-network based recommendation system that suggests pre-existing examples by sampling various regions of an encoded latent space. The tool also provides a graphical user interface for designers to visualize the sensation in 3D view, select previous designs, and bookmark favourites, all while feeling designs in real-time. We conducted a user study with 12 participants suggesting that the tool enables people to quickly explore design ideas and experience them immediately. The design suggestions encouraged collaboration, expression, exploration, and enjoyment, which improved creativity support.
Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV ...
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Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there;and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.
Recent Artificial Intelligence studies have achieved substantial improvements in practical tasks by using extensive amounts of data. We assume that a substantial part of the data to guide artificial design technologie...
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Recent Artificial Intelligence studies have achieved substantial improvements in practical tasks by using extensive amounts of data. We assume that a substantial part of the data to guide artificial design technologies resides in existing designexamples. Developing ways to use this data may enable improvements in intelligent design tools, with the hope that these may provide more effective design workflows and more productive design practices. Such improvements may result in more in-depth evaluations of potentials and alternatives for design situations;hence better planning for the spatial environment. Various approaches have been developed to use representations of architectural examples for artificially tackling architectural design tasks. This study presents a review of the historical development of these approaches, with an overall aim to investigate where and how designexamples have been used for practical computational design applications. The review encompasses traditional and recent Shape Grammar and Procedural Modeling studies, Case-baseddesign, Similarity-based Evaluation and design, and recent studies on the architectural uses of Machine Vision, Semantic Modeling, Machine Learning, and Classification. The emphasis of the review is on the studies that aim at designing or generating new designexamples, particularly for building layouts, facades, envelopes, and massing. For a comparative evaluation of the current capabilities of the examined lineages of studies, we propose a minimum set of design capabilities, and assess each study through this framework. This reveals the overall patterns of already covered requirements. The review shows that initial hand-operated SGs gave way to automatic generation, which in turn developed into automated SG extraction, through increasing levels of computational capabilities. Case baseddesign has been neglected;however, it can be reinvigorated through novel Al techniques. On the other hand, Similarity-based Evaluation
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