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Theoretical Computer Science in Italy: The Early Years
No full textIn this article, we provide an overview of the early developments of theoretical computer science research in Italy in the Sixties and early Seventies. In the same years, the community of researchers working in this domain were organizing to gain an identity among the more traditional disciplines and to obtain a recognition for the "new science" that was taking its first steps. This led in Italy to the creation of Group of Researchers in Theoretical Informatics, in parallel to the institution of European Association for Theoretical Computer Science at European level. In this article, we characterize the Italian contribution to theoretical computer science research in those early years by describing the landscape of activities developed in Italian Universities and research centers in the late sixties and early seventies, on the basis of a census run in 1971 by the newly born GRIT
Data augmentation using background replacement for automated sorting of littered waste
Full text linkThe introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments
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