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  1. Free, publicly-accessible full text available November 1, 2022
  2. Collaborations that require information sharing and mutual trust between companies, suppliers, and clients can be tough, particularly in the remote era. But blockchain’s distributed ledger — and its use of smart contracts — can simplify the process, creating a common, reliable record of transactions and avoiding costly disputes. In doing so, blockchain changes how deals are made: Partner selection is made simpler, as establishing trust is less important; agreement formation is more important, because protocols are hard to alter once put in place; and execution is made easier, because outcomes can be automated. Blockchain isn’t a magic bullet — it works much better in some situations than others — but it can fundamentally change how collaborations work.
  3. Bosansky, B. ; Gonzalez, C. ; Rass, S. ; Sinha, A. (Ed.)
  4. The prediction of human shifts of attention is a widely-studied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer's goals, has received far less attention, even though visual search constitutes much of a person's everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 images were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results ofmore »several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.« less
  5. null (Ed.)