Machine Learning

Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth.

EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.

First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.

Suggested uses of this dataset include performing supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with unlabeled activities of daily living data.

Description:

This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by Huawei 2 smart watches worn by healthy subjects. Subjects peform 20 repetitions of each exercise for each shoulder.

CMSO CFAR NN classifier

This India-specific COVID-19 tweets dataset has been curated using the large-scale Coronavirus (COVID-19) Tweets Dataset. This dataset contains tweets originating from India during the first week of each of the four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.

Announcements: 

PT7 Web is an annotated Portuguese language Corpus built from samples collected from Sep 2018 to Mar 2020 from seven Portuguese-speaking countries: Angola, Brazil, Portugal, Cape Verde, Guinea-Bissau, Macao e Mozambique. The records were filtered from Common Crawl — a public domain petabyte-scale dataset of webpages in many languages, mixed together in temporal snapshots of the web, monthly available [1]. The Brazilian pages were labeled as the positive class and the others as the negative class (non-Brazillian Portuguese).

Parallel sentences in English and French, with mathematical expressions tokenized. The French sentences were extracted from course notes on error-correcting codes authored by Dr. Monica Nevins, University of Ottawa.