Artificial Intelligence
The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
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518 Views
With the increasing uncertainties introduced by intermittent renewable energy sources, as a critical decision-making tool for power system operations, security-constrained unit commitment (SCUC) provides an efficient solution for economically and robustly responding to the changes in the power system operating state. In this study, a graph reinforcement learning (GRL)-based approach is proposed to address the day-ahead SCUC problem, incorporating alternating current (AC) power flow constraints.
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49 Views
A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC).
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26 Views
This dataset contains a collection of Twitter rumours and non-rumours posted during breaking news. The five breaking news provided with the dataset are as follows: * Charlie Hebdo: 458 rumours (22.0%) and 1,621 non-rumours (78.0%).* Ferguson: 284 rumours (24.8%) and 859 non-rumours (75.2%).* Germanwings Crash: 238 rumours (50.7%) and 231 non-rumours (49.3%).* Ottawa Shooting: 470 rumours (52.8%) and 420 non-rumours (47.2%).* Sydney Siege: 522 rumours (42.8%) and 699 non-rumours (57.2%).
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53 Views
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems.
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206 Views
Code search is essential for code reuse, allowing developers to efficiently locate relevant code snippets. Traditional encoder-based models, however, face challenges with poor generalization and input length limitations. In contrast, decoder-only large language models (LLMs), with their larger size, extensive pre-training, and ability to handle longer inputs, present a promising solution to these issues. However, their effectiveness in code search has not been fully explored.
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114 Views
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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32 Views
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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145 Views
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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50 Views