The documentation of software libraries is an essential resource for learning how to use the library. Bad documentation may demotivate a developer from using the library or may result in incorrect usage of the library. Therefore, as developers select which libraries to use and learn, it would be beneficial to know the quality of the available documentation. In this paper, we follow a systematic process to create an automatic documentation quality evaluation tool. We identify several documentation quality aspects from the literature and design metrics that measure these aspects. We design a documentation quality overview visualization to visualize and present these metrics, and receive intermediate feedback through a focused interview study. Based on the received feedback, we implement a prototype for a web service that can evaluate a given documentation page for Java, JavaScript, and Python libraries. We use this web service to conduct a survey with 26 developers where we evaluate the usefulness of our metrics as well as whether they reflect developers’ experiences when using this library. Our results show that participants rated most of our metrics highly, with Text Readability, and Code Readability (of examples) receiving the highest ratings. We also found several libraries where our evaluation reflected developers’ experiences using the library, indicating the accuracy of our metrics.

We present a novel system for detecting and capturing paper documents on a tabletop using a 4K video camera mounted overhead on pan-tilt servos. Our automated system first finds paper documents on a cluttered tabletop based on a text probability map, and then takes a sequence of high-resolution frames of the located document to reconstruct a high quality and fronto-parallel document page image. The quality of the resulting images enables OCR processing on the whole page. We performed a preliminary evaluation on a small set of 10 document pages and our proposed system achieved 98% accuracy with the open source Tesseract OCR engine.

New technologies arise in a number of ways. They may come from advances in scientific research, through new combinations of existing technologies, or by simply imagining what might be possible in the future. This video describes the evolution of Tabletop Telepresence, a system for remote collaboration through desktop videoconferencing combined with a digital desk. Tabletop Telepresence began as a collection of camera, projector, videoconferencing and user interaction technologies. Working together; artists and research scientists combined these technologies into a means of sharing paper documents between remote desktops, interacting with those documents, requesting services (such as translation), and communicating through a videoconference.

Determining the aesthetics of photographs has recently become a research topic of considerable interest. In this project, we focus on constructing meaningful features to model the aesthetic quality of photos with faces. Utilizing face information, color, composition features, as well as novel saliency-based spatial features, we construct an aesthetic inference model, which is more accurate than a state-of-the-art method. Further, we show that this model can be improved by applying different sets of features for single-face and multiple-face photos. Third, we demonstrate by combining low-level generic features with handcrafted features, that the model can be made to achieve even lower error rates.