Artificial Intelligence
The accurate identification of miRNA-disease associations plays a crucial role in biomedical research and clinical applications. However, most research focuses on the existence of the association, without conducting further exploration. In this study, we propose a novel statistical meta-path contrastive learning-based approach (SMCLMDA), which aims to accurately identify the multidimensional relationships(up/down-regulation and causal/non-causal) between miRNAs and diseases.
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The proposed method is rigorously evaluated against several state-of-the-art algorithms, including ISAC, ITD3, IPPO, and IDDPG, to ensure a comprehensive performance analysis. The experimental data, which is publicly available [here], provides detailed insights into the training and evaluation processes of each algorithm.
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Dataset Description
This dataset is designed for analyzing and predicting comeback victories in Multiplayer Online Battle Arena (MOBA) games. It is derived from match data where an objective bounty mechanism was active, providing features that highlight differences between teams with and without the bounty advantage. The dataset is ideal for machine learning tasks, such as binary classification and feature importance analysis, and it enables researchers and analysts to explore factors influencing comeback scenarios in competitive gaming.
Dataset Contents:
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现有的多视图 3D 人体姿态估计 方法在很大程度上依赖于精确的外在校准，而 显著限制了它们在不受控制的情况下的实际部署 环境。为了解决这一限制，我们提出了一种无外在参数的多视图 3D 人体姿态估计 （EFMP） 框架，其中包含三个技术贡献。第一 提出了一种局部全局姿态嵌入 （LGPE） 方案 同时捕获细粒度的关节依赖关系 同时建立交叉视图对应关系。其次，开发了 SpatialView Joint Transformer （SVJFormer） 架构 具有三个专用组件：（1） 特征转换 调制 （FTM） 为 不同的标记来模拟异构关系模式;(2) 先验知识增强 （PKE） 系统地整合 人类运动学约束和多视图几何先验 通过结构拓扑编码进行注意力计算; （3） 空间视图联合注意力 （SVJA） 实现解耦 空间视图注意力计算，然后进行联合分布建模，以捕获分层空间视图依赖关系。 第三种是基于骨骼重投影的 Multi-view Aggregation 引入 （BPMA） 机制以整合多个 3D 输出为单个更高质量的 3D 姿势，用于实际应用。对 3 个基准测试的广泛实验表明 我们的方法实现了最先进的性能，同时 保持紧凑的模型大小。
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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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