Artificial Intelligence
This dataset is utilized for adversarial camouflage generation. We collect vehicle datasets in the CARLA simulation environment under 16 weather conditions. These weather conditions are generated by combining four sun altitude angles (-90°, 10°, 45°, 90°) with four fog densities (0, 25, 50, 90). Within each weather scenario, we randomly choose 16 locations for texture generation. Camera transformation values are randomly selected within specified intervals at each car location.
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This dataset offers a comprehensive mix of financial, demographic, temporal, and external factor data to help predict credit delinquency. It includes key information such as loan terms, credit balances, and effective interest rates, along with client details like salary, marital status, and profession.
In addition to tracking historical credit behavior and overdue days at different time points, the dataset incorporates critical external factors, including climate change, social unrest, and global crises like COVID-19, which may influence payment delays and financial behavior.
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A dataset has been created by recoloring three existing datasets: NeRF Synthetic, LLFF, and Mip 360. The recoloring was performed to provide ground truth for validating recoloring applications. NeRF Synthetic was recolored using Blender, while LLFF and Mip 360 were processed in Photoshop. For each scene in the datasets, 11 images were recolored, ensuring consistency across the datasets.
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The HMDD dataset, which includes a total of 10,235 dia
HMDD 数据集，总共包括 10,235 个 dialogues, combines three types of conversation data: Human
logues 结合了三种类型的对话数据：人类Human (both Agents A and B are humans), Human-AI (Agent
人类（代理 A 和 B 都是人类）、人类-人工智能（代理A is human, and Agent B is AI), and AI-Human (Agent A is
A 是人类，代理 B 是 AI），AI 人类（代理 A 是AI, and Agent B is human). AI 和代理 B 是人类）。
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A number of major aspects of creative English language teaching are reviewed in this. What makes this research interesting is the integration of technology, mostly via artificial intelligence (AI) and mobile based learning, which give new ways to improve the student engagement and learning results.
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117 Views
This dataset presents one-shot measurements of a multimode fiber subjected to displacement over a range of 18mm, with a fine resolution of 0.01mm. The data captures the intricate light patterns transmitted through the fiber at each displacement position, providing a detailed view of the fiber's behavior under varying conditions.
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Well logs are interpreted/processed to estimate the in-situ reservoir properties (petrophysical, geomechanical, and geochemical), which is essential for reservoir modeling, reserve estimation, and production forecasting. The modeling is often based on multi-mineral physics or empirical formulae. When sufficient amount of training data is available, machine learning solution provides an alternative approach to estimate those reservoir properties based on well log data and is usually with less turn-around time and human involvements.
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We develope a novel TCM hallucination detection dataset, Hallu-TCM, sine no prior work has attempted this task in TM. We selected 1,260 TCM exam questions including 16 TCM subjects, input them into GPT-4, and collected their feedback. In the first level, we utilize Qwen-Max interface to annotate feedback multiple times with the binary label. If Qwen-Max consistently provided the same label across annotations, we adopted that label. For contentious cases, we recruited higher-degree research students who can understand and solve complex questions, including three Ph.D.
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167 Views
Metaverse Network Traffic dataset consists of comprehensive applications from Virtual, Augmented, and Mixed Realities. Dataset is captured in an intelligent platform built using Oculus Quest 2, traffic manager, and cloud rendering device using Virtual Desktop Streamer. The Dataset is captured in packet capture (.pcap) format. The extracted version in the form of comma-seperated value (.csv) file is also provided. However, .pcap file will provide more flexibility. Dataset is captured for 60, 90, and 120 Hz frames per seconds (FPS) configurations.
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This repository contains the datasets produced using different data generation strategies to train data driven models (e.g., decision trees, gradient tree boosting, and deep neural networks), and to evaluate their performances. The data generation strategies are described, and the results are presented in the conference paper: "Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids" J. Cuenca, E. Aldea, E. Le Guern-Dall'o, R. Féraud, G. Camilleri, and A. Blavette. IEEE ISGT EU 2024, Dubrovnik, Croatia, Oct 2024.
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