Web Science and Digital Libraries Research Group

As researchers, we acquire new developments in our fields through reading scientific literature. Even though we assume the reading and cognitive behaviors to be stochastic, recent studies reveal otherwise. Over the recent periods, we ( NirdsLab , WS-DL ) have been studying these patterns of researchers using eye-tracking and published a series of studies highlighting our findings. This blog will summarize our studies, highlighting our discoveries, realizations, and future directions.   For the discussion, we consider three peer-reviewed poster publications. Analyzing the Effect of Reading Patterns using Eye Tracking Measures ( DOI , JCDL 2020 , Best Poster ) Analyzing Unconstrained Reading Patterns of Digital Documents Using Eye Tracking ( DOI , JCDL 2021 , Best Poster ) Multidisciplinary Reading Patterns of Digital Documents ( DOI , ETRA 2022 , In Press) We will briefly review each publication, highlighting key features, novelties, and limitations. Then we identify potential resear

Image Source Introduction Clinicians and researchers continue to look at more and more at ways to utilize Electroencephalography (EEG)   data to support the diagnosis and treatment of various physical and mental health conditions.  However, not long after being  involved in EEG related research , you will soon realize that there are numerous formats available for EEG data recordings.  The lack of EEG data format consistency continues to present challenges in the transmission, exchange, and analysis of such data amongst researchers and impedes  advancements in brain-computer interface (BCI) and  biomedical research . At a minimum, EEG data consistency would reduce confusion amongst researchers and clinicians who rely on data sharing.  However, the benefits of EEG data standardization also include  the reduction or elimination of  expensive, time-consuming, and labor-intensive  data conversions to other formats.  This is very significant as it  supports interdisciplinary, multidisciplin

Tables and figures are an essential part of a well-written scientific paper. Scientific papers use tables to present the bulk of the detailed information such as results and their associations. Many of the basic concepts, process flows, key natural trends, and key discoveries are presented in the figures. In this blog, I present a simple but effective rule-based approach using regular expressions (RegEx) for extracting table and figure references from the text in scientific papers.  What does the table or figure reference mean? In scientific papers, the tables and figures are referred to in body text to support the claims. Below are some examples where tables and figures are referred to in body text. As seen in Table 3 , there are 3 cross-listed top 10 features identified by both ANOVA-F and MI (in blue text). Figure 4 shows that evaluation results using the core features exhibit significantly different performances. Overview of the rule-based approach Prior to using the rule-based me

Figure: Virtual cover for ACM IUI’22 conference The premier showcase of outstanding research and development on intelligent user interfaces was hosted by the University of Helsinki, Finland. The 27th edition of the annual ACM Intelligent User Interface (IUI) conference was virtually held on March 21-25, 2022. There were 386 registered participants and 62 papers presented worldwide. For those who could not attend the conference, pre-recorded videos are available on the ACM SIGCHI YouTube channel, and the conference proceedings are available online. This year, I was fortunate that one of our papers was accepted and represented our research group, Web Science and Digital Libraries Research Group (WSDL) , at this conference. My paper, " InSupport: Proxy Interface for Enabling Efficient Non-Visual Interaction with Web Data Records " was co-authored by Dr. Sampath Jayarathna and Dr. Vikas Ashok from Old Dominion University, USA, and Hae-Na Lee from Stony Brook University, USA.