Web Science and Digital Libraries Research Group

ParsCit is a citation parser developed by a joint effort of Pennsylvania State University and National University of Singapore. Over the past ten years, it is been compared with many other citation parsing tools and is still widely used. Although Neural ParsCit has been developed, the implementation is still not as easy to use as ParsCit. In particular, PDFMEF encapsules ParsCit as the default citation parser. However, many people found that installing ParsCit is not very straightforward. This is partially because it is written in perl and the instructions on the ParsCit website are not 100% accurate. In this blog post, I describe the installation procedures of ParsCit on a Ubuntu 16.04.6 LTS desktop. Installation on CentOS should be similar. The instructions do not cover Windows. The following steps assume we install ParsCit under /home/username/github. Download the source code from https://github.com/knmnyn/ParsCit and unzip it. $ unzip ParsCit-master.zip  Install c++ compiler

ACM SIGIR 2019 was held in Paris, France July 21-25, 2019 in the conference center of the Cite des sciences et de l'industrie . Attendees were treated to great talks, delicious food, sunny skies, and warm weather. The final day of the conference was historic - Paris' hottest day on record (42.6 C, 108.7 F).   There were over 1000 attendees, including 623 for tutorials, 704 for workshops, and 918 for the main conference. The acceptance rate for full papers was a low 19.7%, with 84/426 submissions accepted. Short papers were presented as posters, set up during the coffee breaks, which allowed for nice interactions among participants and authors. ( Conference schedule - contains links to videos of many of the talks ) Several previously-published ACM TOIS journal papers were invited for presentation as posters or oral presentations. We were honored to be invited to present our 2017 ACM TOIS paper, " Comparing the Archival Rate of Arabic, English, Danish, and Korean La

First of all, I would like to give credit to Matt Gray for going through the major hassle in figuring out the prerequisites and for the awesome documentation provided on how to Install and Use Lab Streaming Layer on Windows. In this blog, I will guide you how to install open source  Lab Stream Layer (LSL)  and stream data (eye tracking example using PupilLabs eye tracker) to  NeuroPype Academic edition . Though a basic version of LSL is available along with   NeuroPype, you will still need to complete following prerequisites before installing LSL. You can find installation instructions for LSL at  https://github.com/sccn/labstreaminglayer/blob/master/doc/BUILD.md . The intention of this blog is to provide an easier and more streamlined step-by-step guide for installing LSL and NeuroPype. LSL is low-level technology for exchange of time series between programs and computers. Figure:   LSL core components Source: ftp://sccn.ucsd.edu/pub/bcilab/lectures/Demo_1_The_Lab_Stream