Artificial Intelligence

Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones.

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Visual saliency prediction has been extensively studied in the context of standard dynamic range (SDR) display. Recently, high dynamic range (HDR) display has become popular, since HDR videos can provide the viewers more realistic visual experience than SDR ones. However, current studies on visual saliency of HDR videos, also called HDR saliency, are very few. Therefore, we establish an SDR-HDR Video pair Saliency Dataset (SDR-HDR-VSD) for saliency prediction on both SDR and HDR videos.

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QiandaoEar22 is a high-quality noise dataset designed for identifying specific ships among multiple underwater acoustic targets using ship-radiated noise. This dataset includes 9 hours and 28 minutes of real-world ship-radiated noise data and 21 hours and 58 minutes of background noise data.

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This database contains Synthetic High-Voltage Power Line Insulator Images.

There are two sets of images: one for image segmentation and another for image classification.

The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.

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LGG Segmentation Dataset

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This a Lightning arrester point cloud dataset, using TXT documents to save, each file format is (8192, 7), 8192 means each file has 8192 points, where 1-3 columns are spatial dimensions, 4-6 columns are color information, and the last column is the label information of lightning arrester parts segmentation. It can be used to finished pointcloud segmention task.

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While picking robots aim to address this, the complex growth environment poses challenges in identifying and locating fruits due to factors like light and leaf occlusion. This study focuses on designing a recognition and localization method tailored to the natural growth conditions of melons and fruits, aiming to provide precise positional information for effective harvesting. Leveraging GTR-Net and binocular stereo vision, the proposed technology integrates a lightweight backbone network with Ghost bottleneck and TCSPG modules.

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The following are three publicly available datasets for experiments related to federated learning or machine learning.

Availability of Data and Materials: The datasets used to support the findings of this study are publicly available on Internet as follow:

MNIST: http://yann.lecun.com/exdb/mnist/;

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Popularity of smartphones also popularized, reading content using smartphones. Reading using smartphones quite differs from reading using desktop system. Mouse and Keyboard are the peripherals associated with the reading in desktop systems. Study of the handling of such devices has led to provide implicit feedback of the content read. Similar study in smartphones to get implicit feedback remains to be a huge gap. Reading using smartphones involves screen gestures like pinch to zoom, tap, scroll, orientation change and screen capture.

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The SA dataset is a small- to medium-sized dataset that we collected and produced in 2023 for anemone image generation, primarily from EUVP, Flicker, and YouTube, and we consider the availability of the SA dataset to be of particular significance given the scarcity of marine life image datasets. Currently, we have not publicly released the SA dataset, but will do so in the near future.

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