13,055 research outputs found
Item-based Variational Auto-encoder for Fair Music Recommendation
No full textWe present our solution for the EvalRS DataChallenge. The EvalRS
DataChallenge aims to build a more realistic recommender system considering
accuracy, fairness, and diversity in evaluation. Our proposed system is based
on an ensemble between an item-based variational auto-encoder (VAE) and a
Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the
bias in popularity, we use an item-based VAE for each popularity group with an
additional fairness regularization. To make a reasonable recommendation even
the predictions are inaccurate, we combine the recommended list of BPRMF and
that of item-based VAE. Through the experiments, we demonstrate that the
item-based VAE with fairness regularization significantly reduces popularity
bias compared to the user-based VAE. The ensemble between the item-based VAE
and BPRMF makes the top-1 item similar to the ground truth even the predictions
are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as
a novel evaluation metric based on our reflections from the extensive
experiments.Comment: 6pages, CIKM 2022 Data challeng
Author Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
No full textThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
Predicting Mind-Wandering with Facial Videos in Online Lectures
No full textThe importance of online education has been brought to the forefront due to COVID. Understanding students' attentional states are crucial for lecturers, but this could be more difficult in online settings than in physical classrooms. Existing methods that gauge online students' attention status typically require specialized sensors such as eye-trackers and thus are not easily deployable to every student in real-world settings. To tackle this problem, we utilize facial video from student webcams for attention state prediction in online lectures. We conduct an experiment in the wild with 37 participants, resulting in a dataset consisting of 15 hours of lecture-taking students' facial recordings with corresponding 1,100 attentional state probings. We present PAFE (Predicting Attention with Facial Expression), a facial-video-based framework for attentional state prediction that focuses on the vision-based representation of traditional physiological mind-wandering features related to partial drowsiness, emotion, and gaze. Our model only requires a single camera and outperforms gaze-only baselines
DBLP-derived labeled data for author name disambiguation
No full textThis is a DBLP-derived labeled data originally created by Dr. C. Lee Giles at Penn State University and filtered for duplicate removal and error correction by Dr. Jinseok Kim at University of Michigan. For more details, see references below.1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9Each row refers to an author name instance with following feature information separated by tab.author name: full name string extracted from DBLPunique author id: labels assigned manually by Dr. C. Lee Giles's teampaper id: assigned by Dr. Jinseok Kimauthor list: names of authors in the byline of the paperyear: publication yearvenue: conference or journal namestitle: stopwords removed and stemmed by the Porter's stemmerIf you want to use this dataset, please consider to cite papers below.For the original dataset: Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two Supervised Learning Approaches for Name Disambiguation in Author Citations. JCDL 2004: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, 296-305. doi:10.1145/996350.996419For the filtered dataset: 1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 or2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9</div
Khoo Kay Kim, professor of Malaysian history : a biobibliometric study
No full textPresents an analysis of the publication productivity, authorship pattern, channels of communication, journal preference and language preference of Professor Dato' Khoo Kay Kim, Professor of Malaysian History in the University of Malaya, Kuala Lumpur. The results of this biobibliometric study indicate that he can be a role model for future Malaysian historians to emulate his various achievements especially in the field of history education
Anxious, Dismal, Giddy, Aggressive: Seth Kim-Cohen interviewed by Mark Peter Wright for Ear Room.
No full textA conversation with author Seth Kim-Cohen
Kim Gordon - no icon
No full textAs cofounder of legendary rock band Sonic Youth, best-selling author, and celebrated artist, Kim Gordon is one of the most singular and influential figures of the modern era. This personally curated scrapbook is an edgy and evocative portrait of Gordon s life, art, and style. Spanning from her childhood on Californian surf beaches in the 60s and 70s to New York s downtown art and music scene in the 80s and 90s where Sonic Youth was born. Through unpublished personal photographs, magazine and newspaper clippings, fashion editorials, and advertising campaigns, interspersed with Gordon s song lyrics, writings, artworks, private objects, and ephemera, this book demonstrates how Kim Gordon has been a role model for generations of women and me
Network analysis approach to study hospitals' prescription patterns focused on the impact of new healthcare policy
No full textUnderstanding hospitals' relationships is critical to the analysis of public healthcare environment. There have been many attempts to analyze medical environment at a personal level. Recently, at an organizational level, there has been some advance in research into examining a relationship between hospitals. However, the formation of linkages is restricted to explicit and direct interactions. In contrast, we focused on implicit information flows between hospitals. This study also analyzes large scale hospital networks based on prescribing similarity. The sample dataset we used is the trustworthy representative of actual population in Korea. We assessed the impact of Drug Utilization Review (DUR) on hospital network characteristics. We examined National Inpatient Sample (NIS) dataset for before-DUR year (2010) and after-DUR year (2011). Various network metrics and performance measures are calculated for the two years. Generated hospital networks of the two years were significantly different in terms of both network metrics and performance measures, except for a riskiness measure. In network clustering result, Spearman's correlation coefficients indicated that network metrics can be used to evaluate hospitals having extreme prescription patterns. We anticipate our novel approach allows us to better understand public healthcare environment
Overview of Recent Progress in Fire Suppression
No full textthis document is published in / Une version de ce document se trouve dans : Invited Keynote Lecture at the 2 nd NRIFD Symposium, Proceedings, Tokyo, Japan, July 17-19, 2002, pp. 1-13 www.nrc.ca/irc/ircpubs NRCC-45690 Title: OVERVIEW OF RECENT PROGRESS IN FIRE SUPPRESSION TECHNOLOGY Author(s): Andrew KIM Corresponding (first) author: Andrew Kim Academic degree: Ph.
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