SRI Blog

-By Xinghua Zheng1, Ming Tang1, Hankz Hankui Zhuo1*, Kevin X. Wen Paper Link Abstract Bike Sharing Systems (BSSs) have been adopted in many major cities of the world due to traffic congestion and carbon emissions. Although there have been approaches to exploiting either bike trailers via crowdsourcing or carrier vehicles to reposition bikes in the “right” stations in the “right” time, they do not jointly consider the usage of both bike trailers and carrier vehicles. In this paper, we aim to take advantage of both bike trailers and carrier vehicles to reduce the loss of demand with regard to the crowdsourcing of bike trailers and the fuel cost of carrier vehicles. In the experiment, we exhibit that our approach outperforms baselines in several datasets from bike sharing companies. Bike-sharing systems (BSSs) typically have a set of base stations that are strategically placed throughout a city and each station has a fixed number of docks, e.g., Capital Bike-share, Blue

- By Wiesław Kopec´, Kinga Skorupska, Piotr Gago, Krzysztof Marasek  Polish-Japanese Academy of Information Technology Paper Link Courtesy DZone   Abstract One of the more prominent trends within Industry 4.0 is the drive to employ Robotic Process Automation (RPA), especially as one of the elements of the Lean approach.     The full implementation of RPA is riddled with challenges relating both to the reality of everyday business operations, from SMEs to SSCs and beyond, and the social effects of the changing job market. To successfully address these points there is a need to develop a solution that would adjust to the existing business operations and at the same time lower the negative social impact of the automation process. To achieve these goals we propose a hybrid, human-centred approach to the development of software robots. This design and  implementation method combines the Living Lab approach with empowerment through participatory design to kick-start the

-By Sheng-Yu Wang Oliver Wang Andrew Owens Richard Zhang Alexei A. Efros  UC Berkeley1 Adobe Research Paper Link   Git Code Abstract Most malicious photo manipulations are created using standard image editing tools, such as Adobe. We present a method for detecting one very popular Photoshop manipulation image warping applied to human faces – using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to “undo” a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations In an era when digitally edited visual content is ubiquitous, the public is justifiably eager to know whether the images they see on TV, in glossy magazines and on the Internet are,