Artificial Intelligence
Comprehensive dataset (5000 spectra) of simulated grating biosensor reflections in Excel format. Generated via Lumerical FDTD, it includes 11 parameters (thickness, RI, peak wavelength, FWHM, reflectance, etc.). It is ideal for data visualization, sensor response exploration, and AI/ML benchmarking. The full dataset in Excel format is coming soon! Follow this repository to be notified when it's released. In the meantime, feel free to browse the README for more information about the project.
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DLSF is the first dedicated dataset for Text-Image Synchronization Forgery (TISF) in multimodal media. The source data for this dataset is scraped from the Chinese news aggregation platform, Toutiao. This dataset includes extensive text, image, and audio-video data from news articles involving politicians and celebrities, featuring samples of both entity-level and attribute-level TISF. It provides comprehensive annotations, including labels for text-image authenticity, types of TISF, image forgery regions, and text forgery tokens.
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This is a dataset containing images of cotton leaves with Verticillium wilt, brown spot, aphids, and healthy leaves.The dataset initially consisted of original images of brown spot disease (330 images), verticillium wilt (213 images), healthy leaves (383 images), and aphids (473 images). To balance class distributions and improve model performance, data augmentation techniques such as flipping and scaling were applied.
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The manual generation of access control policies from an organization’s high-level requirement specifications is a laborious and error-prone process. Mistakes in this manual policy generation process cause access control failures that may lead to data breaches. As a solution, previous research pro- posed automated access control policy generation frameworks. However, existing approaches suffer from several limitations, such as the inability to handle complex access requirements due to the lack of domain adaptation, making them highly unreliable.
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The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.
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This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.
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The rapid evolution of visual data demands compression technologies that balance theoretical expressiveness with practical deployment constraints. Current learning-based approaches face dual challenges: non-differentiable quantization operations that hinder end-to-end optimization, and rigid architectural components limiting adaptability to diverse content characteristics. This paper introduces a novel neural compression framework that integrates principles from Kolmogorov-Arnold Networks (KANs) with dynamic quantization mechanisms.
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The fitness landscape of optimization problems significantly impacts the performance of metaheuristic optimization algorithms. While no algorithm performs well on all problems, identifying the most suitable one for a specific problem can be achieved by extracting features from the landscape. However, these features are often extracted before optimization, disregarding valuable knowledge collected during the optimization process. Moreover, existing algorithm selection methods rely on a single algorithm, limiting flexibility and missing potentially better options.
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Iron Ore Powder Dataset: The mineral powder dataset is utilized to predict the moisture content present in the mineral powder. Images of the mineral powder at different moisture levels exhibit variations in texture, which are influenced by the moisture content and significantly affect the surface morphology of the powder. Specifically, this variation is evident in the changes in texture and surface features.
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Smart Home Automation (SHA) has significantly improved homes’ convenience, comfort, security, and safety. It has gained widespread use due to its intelligent monitoring and quick response capabilities. The current state of SHA enables effective monitoring and motion detection. However, false notifications remain a significant challenge, as they can cause unnecessary alarms in intrusion detection systems. To address this, we propose an intelligent model for a smart home security system that uses computer vision techniques to detect trespasser movement near the boundary wall.
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