18,509 research outputs found

    Automatic generation of level maps with the do what's possible representation

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.Final Accepted Versio

    Reimagine Recovery: A Playbook for an Equitable Future

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    We cannot allow ourselves to resume what was; we must reimagine what can be. True recovery requires us to acknowledge the unjust structures and policies that, in many ways, led to and compounded the devastation of the pandemic. It calls for us to examine our obsession with the idea of rapid growth at all costs and establish a shared understanding of inclusive, sustainable growth that results in equal opportunity—and equitable outcomes. It demands us to recognize our global web of mutuality and come together to collectively address the problems ahead with humility and reciprocity. And it challenges us to realize a bold, hopeful reimagination of our social, economic, political and governance systems, with equity and interdependence at their core.Reimagine Recovery: A Playbook for an Equitable Future offers a detailed vision of such recovery, beginning in the places we work and live and extending to our largest global stages. Like much of our work at the Ford Foundation, the playbook asks: What's possible when everyone can fully participate in society and has the opportunity to shape their lives? What's possible when we follow the lead of leaders and organizations building solutions for—and with—historically excluded communities? What's possible when we shift our old ways of operating and include equity in our execution of every policy and cultivation of every movement

    Automated metadata annotation: What is and is not possible with machine learning

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    Automated metadata annotation is only as good as training dataset, or rules that are available for the domain. It's important to learn what type of data content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what's popular and what's available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.Peer ReviewedPostprint (published version

    MTRNet: A Generic Scene Text Eraser

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    Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting. To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging large-scale synthetic dataset, initially proposed for text detection in real scenes. What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets.Comment: Presented at ICDAR2019 Conferenc
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