Artificial Intelligence
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19”, Proceedings of the 25th International Conference on Human-Computer Interaction (HCII 2023), Copenhagen, Denmark, July 23-28, 2023 (Accepted for Publication)
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
We present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1) Well-trimmed longtemporal clips. Each video contains a complete shot, lasting around 5 seconds on average. 2) Dense annotation. The pixel-level annotations are provided at a high frame rate of 15 f/s. 3) High resolution.
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Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.
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Passive missiles, such as short-range or within-visual-range air-to-air missiles (SRAAMs or WVRAAMs) and man-portable air defense systems (MANPADS), which do not emit radio frequencies (RF) and thus evade detection by an aircraft’s Radar Warning Receiver (RWR). Current passive missile detection systems, primarily relying on Midwave Infrared (MWIR) exhibit limitations regarding high false alarm rates and extensive processing requirements.
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This data provides price and tweet data for Bitcoin from February 21, 2021 to May 10, 2022.
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The dataset consists of a large collection of images of about 11,000 different species of birds, with a total of 5 million images. This dataset represents a valuable resource for researchers, conservationists, and bird enthusiasts alike, allowing for a more comprehensive understanding of the diversity and distribution of avian species around the world. The data could be used for a wide range of applications, including species identification, biodiversity monitoring, and ecological research.
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Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working aged adults. For clinical datasets, i have conducted the pilot study at SRM Medical College Hospital and Research Centre, Chennai for diabetic patients. Then we collected the retinal images from patients for further evaluation, All images were graded by experienced ophthalmologist for model training and testing purpose.
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This dataset was created by following these steps. First, online reviews of HMD VR devices are collected and refined. Second, variables are deduced from previous studies, and then appropriate keyword candidates for the deduced variables are selected. Topic modeling is conducted to examine whether the deduced variables sufficiently represent all the reviews, and other variables are added if necessary.
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