Artificial Intelligence
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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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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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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Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models.
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299 Views
This dataset is a sequential recommendation dataset that includes three sub-datasets: Beauty, Toys, and Yelp, specifically designed for research and development in recommendation systems. All datasets have been pre-processed, allowing users to directly input them into the main program for use. These datasets are ready for experiments involving user-item interactions and can be used to train and evaluate recommendation algorithms. The command to run the datasets is: .
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The Multi-Server Multi-User computation offloading dataset is a dataset based on the scenario of multi-server multi-user binary computing offloading. It is characterized by the connection status between users and edge servers, user task information, and server computational resource information. The solution aims to minimize the total cost of power consumption and latency of all tasks. The labels are the offloading decisions of user tasks and the computational resource allocation of edge servers. The features and labels of this dataset are graph-structured.
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Acoustic non-line-of-sight vehicle detection dataset. Complete multi-channel audio of vehicles passing through the intersection was captured at multiple intersections. It can be used for acoustic non-line-of-sight vehicle detection. The direction in which the vehicle entered the intersection, and the moments when the vehicle entered and left the line of sight were recorded in the file names, and the audio categories can be classified based on that moment. All audio files were stored in five folders to facilitate five-fold cross-validation.
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