Artificial Intelligence
Visual storytelling refers to the manner of describing a set of images rather than a single image, also known as multi-image captioning. Visual Storytelling Task (VST) takes a set of images as input and aims to generate a coherent story relevant to the input images. In this dataset, we bridge the gap and present a new dataset for expressive and coherent story creation. We present the Sequential Storytelling Image Dataset (SSID), consisting of open-source video frames accompanied by story-like annotations.
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Seed quality has become increasingly important in seed management and operation. Seed germination testing is one of the crucial methods for seed quality assessment, as the development quality of seeds, including germination rate and growth speed of seedlings, is an important indicator of seed quality. The germination rate of soybeans is one of the criteria for identifying high-quality soybeans.
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This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm.
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The FMK (Finger Major Knuckle) dataset was proposed and created to support the experiments of identity verificatio of knuckles of middle and thumb fingers modalites. The images of this dataset were captured using the rear camera of an OPPO A12 smartphone. This dataset was created from 20 different subjects between the ages of 30 and 67. For each subject there are 3 images of major knuckle for the middle finger and 3 images of major knuckle for thumb finger.. The FMK dataset was proposed and constructed for testing and evaluation.
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We collated small molecule solubility data from an array of databases and literature. Some of these sources merely provided molecular names, lacking SMILES notation. We sourced the molecular SMILES and molecular weights from PubChem, DrugBank, https://www.wikiarabic.org/, and https://www.sigmaaldrich.com/US/en. In terms of data selection, Canonical SMILES was preferred over Isomeric SMILES in instances where both were available in different forms.
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Abstract—This paper presents a novel approach to optimizing resource allocation in Internet of Things (IoT) networks, focusing on enhancing energy efficiency (EE) while maintaining age of information (AoI) awareness through device-to-device (D2D) communication. Our proposed solution integrates simultaneous wireless information and power transfer (SWIPT) with energy harvesting (EH) techniques. Specifically, D2D users employ time switching (TS) to harvest energy from the environment, while IoT users utilize power splitting (PS) to obtain energy from base stations (BS).
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A short time ago, the study of contactless fingerprint authentication gained appeal among biometric researchers. Contactless fingerprint systems offer various advantages, such as ease of capture and affordability, over conventional fingerprint identification systems, which demand that the user's finger make direct contact with the sensor.
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The dataset is named Chinese rose disease dataset, including healthy leaves, black spot leaves, powdery mildew leaves and downy mildew leaves. All images in this dataset were collected from Nanyang City, Henan Province, China. And all images were collected under natural conditions in order to ensure the true execution of the images. To improve the image variety, we randomly enhance the images in the dataset by flipped, changed the brightness, added Salt and pepper noise and added Gaussian noise.
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This dataset consists of “.csv” files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack) targeting the RPL protocol, and these files are taken from Cooja (Contiki network simulator). It allows researchers to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks by mimicking real-world attack scenarios is essential to developing and testing protection mechanisms against such attacks.
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Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets.
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