Pass2vec: Analyzing soccer players’ passing style using deep learning

The aim of this research was to analyze the player’s pass style with enhanced accuracy using the deep learning technique. We proposed Pass2vec, a passing style descriptor that can characterize each player’s passing style by combining detailed information on passes. Pass data was extracted from the ball event data from five European football leagues in the 2017–2018 season, which was divided into training and test set. The information on location, length, and direction of passes was combined using Convolutional Autoencoder. As a result, pass vectors were generated for each player. We verified the method with the player retrieval task, which successfully retrieved 76.5% of all players in the top-20 with the descriptor and the result outperformed previous methods. Also, player similarity analysis confirmed the resemblance of players passes on three representative cases, showing the actual application and practical use of the method. The results prove that this novel method for characterizing player’s styles with improved accuracy will enable us to understand passing better for player training and recruitment.11Nssciscopu

PatternFinder 2.0: Usability Test and Redesign of a Patient History Search System

Managing patients ’ data occupies around 20 % of annual nation wide medical expense. There are many reasons for this issue and one of them is lack of visual query languages for temporal data. Many visualization systems exist already, however they posses weak query mechanisms. Using a system that already has extensive query mechanism into multipl

ModelCraft: capturing freehand annotations and edits on physical 3D models

With the availability of affordable new desktop fabrication techniques such as 3D printing and laser cutting, physical models are used increasingly often during the architectural and industrial design cycle. Models can easily be annotated to capture comments, edits and other forms of feedback. Unfortunately, these annotations remain in the physical world and cannot be easily transferred back to the digital world. Here we present a simple solution to this problem based on a tracking pattern printed on the surface of each model. Our solution is inexpensive, requires no tracking infrastructure or per object calibration, and can be used in the field without a computer nearby. It lets users not only capture annotations, but also edit the model using a simple yet versatile command system. Once captured, annotations and edits are merged into the original CAD models. There they can be easily edited or further refined. We present the design of a SolidWorks plug-in implementing this concept, and report initial feedback from potential users using our prototype. We also present how this prototype could be extended seamlessly to a fully functional system using current 3D printing technology. ACM CLASSIFICATION: H5.2 [Information interfaces an