Artificial Intelligence
Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks.
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The dataset contains the navigation measurements obtained in the indoor experiment field. The volunteers move on the whole 4th floor of the Building D of Dong Jiu Teaching classes at Huazhong University of Science and Technology. Meanwhile, the experimental area consists of a total area of 717 m 2. These datasets were used and can be used to test and validate the radio map database updating-based localization positioning algorithm through the RSSI signals space.
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1381 Views
Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.
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2062 Views
The file contains the source code of the mission generator MG1 together with the materials and results of the experiments. This data is associated with a paper in which we focus on an experimental protocol for the comparison of fully-automatic design methods of control software for robot swarms.
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数据集收集于 2009 年至 2010 年，通常用于预测学生的表现和个性化。在学生信息方面，此数据集包含 525，535 行学生答案，包括 4，217 名学生和 124 种技能。
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165 Views
(Work in progress)
This dataset contains the augmented images and the images & segmentation maps for seven handwashing steps, six of which are prescirbed WHO handwashing steps.
This work is based on a sample handwashing video dataset uploaded by Kaggle user real-timeAR.
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336 Views
MCAD contains 5 seen classed and 68939 cell samples, and the unseen class contains 6 categories and 32679 cell samples. Seen class samples are collected from fecal microscopic images, while unseen class samples are collected from leucorrhea microscopic images.
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174 Views
This is a subset of the ASHRAE Global Comfort Database that we used in our study to prove that Deep learning methods performs better than shallow methods predicting the thermal sensation.
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89 Views
This paper describes the creation of the Massive Arabic Speech Corpus (MASC). MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition. In addition to MASC, a pre-trained 3-gram language model and a pre-trained automatic speech recognition model are also developed and made available to interested researchers.
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9326 Views
Owing to increased biosecurity and industrial demands, the poultry houses in Taiwan are generally nonopen and closed types, with automatic environmental control and sensor equipment gradually being installed in such houses. Environmental sensors and poultry health monitoring systems are necessary to improve poultry feeding efficiency and safety. In this work, we developed a goose surface temperature monitoring system based on deep learning using visible image and integrated with infrared thermal image.
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222 Views