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Abstract. Transcription of User Interface (UI) elements hand drawings
to the computer code is a tedious and repetitive task. Therefore, a need arose to create a system capable of automating such process. This paper describes a deep learning-based method for hand-drawn user interface elements detection and localization. The proposed method scored 1st place in the ImageCLEFdrawnUI competition while achieving an overall precision of 0.9708. The final method is based on Faster R-CNN object detector framework with ResNet-50 backbone architecture trained with advanced regularization techniques. The code has been made available at: https://github.com/picekl/ImageCLEF2020-DrawnUI.

"We address the problem of offline handwritten diagram recognition. Recently, it has been shown that diagram symbols can be directly recognized with deep learning object detectors. However, object detectors are not able to recognize the diagram structure. We propose Arrow R-CNN, the first deep learning system for joint symbol and structure recognition in handwritten diagrams. Arrow R-CNN extends the Faster R-CNN object detector with an arrow head and tail keypoint predictor and a diagram-aware postprocessing method. We propose a network architecture and data augmentation methods targeted at small diagram datasets. Our diagram-aware postprocessing method addresses the insufficiencies of standard Faster R-CNN postprocessing. It reconstructs a diagram from a set of symbol detections and arrow keypoints. Arrow R-CNN improves state-of-the-art substantially: on a scanned flowchart dataset, we increase the rate of recognized diagrams from 37.7 to 78.6%."

"A guide for AI entrepreneurs on how to prepare a dataset for a machine learning project."

Obtaining Information From Technical Drawings Using TensorFlow, Keras-OCR and OpenCV

"If you’re responsible for a budget that pays programmers or other knowledge workers, you probably care a great deal about getting good va..."

"Very rarely is exactly one design system created to serve exactly one product that expresses exactly one design language. Nearly all the design systems we've worked on require a high degree of flexibility in order to properly serve our clients' needs. Some of this flexibility is achieved by variabil"

"A list of Figma plugins to efficiently manage your Design System."

"It’s peculiar that an organization (OpenAI) founded to worry about things like monstrously inhuman obsessive paperclip maximizers would choose to personify their text generator.
I mean, I mean, I know they worry about an AI so smart that it convinces us meat puppets to work to destroy ourselves. But they chose to frame *their* AI in a way everyone knows (or should know) makes humans less critical, more gullible. …"

Automated dialogue or conversational systems are anthropomorphised by developers and personified by users. While a degree of anthropomorphism may be inevitable due to the choiceof medium, conscious and unconscious design
choices can guide users to personify such systems to varying degrees. Encouraging users to relate to automated systems as if they were human can lead to high risk scenarios caused by over-reliance on their outputs. As a result, natural language processing researchers have investigated the factors that induce personification and develop resources to mitigate such effects. However, these efforts are fragmented, and many aspects of anthropomorphism have yet to be explored. In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise, including reinforcing gender stereotypes and notions of acceptable language. We recommend that future efforts towards developing dialogue systems take particular care in their design, development, release, and description; and attend to the many linguistic cues that can elicit personification by users.