Machine Learning
Non uniformly illuminated Blender simulated and camera captured haze masks.
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The following folder contains the data simulated using the metholodgy described in the paper titled "Federated Learning based Base Station Selection using LiDAR Data"
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Data of cricket bowlers was recorded using wearable IMU sensors. Data were collected from the designed sensor units placed on the thigh and tibia of the front leg of each bowler using a strap. Recorded data include 3-axes accelerometer data and 4-dimensional quaternion data.
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For whole-testis cross-section analysis the output predictions from 1024x1024 images were merged and the whole-testis cross-section image was recompiled, with the annotated cellular and tubular objects, which were derived from cell and tubule models, respectively . This figure provides a high-resolution image of whole-testis cross-section analysis with cellular and tubular labels.
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We can create ANN-based and SVM-based flip-flop timing models with the help of SPICE simulations. This dataset contains training/test samples obtained using SPICE simulation for creating/testing these ML-based models.
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The C3I Synthetic Human Dataset provides 48 female and 84 male synthetic 3D humans in fbx format generated from iClone 7 Character creator “Realistic Human 100” toolkit with variations in ethnicity, gender, race, age, and clothing. For each of these, it further provides the full-body model with five different facial expressions – Neutral, Angry, Sad, Happy, and Scared. Along with the body models, it also open-sources a data generation pipeline written in python to bring those models into a 3D Computer Graphics tool called Blender.
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This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.
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UCI Wine quality
HK stock prices
Customer retail credit
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Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data.
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