Artificial Intelligence
The goal of our research is to identify malicious advertisement URLs and to apply adversarial attack on ensembles. We extract lexical and web-scrapped features from using python code. And then 4 machine learning algorithms are applied for the classification process and then used the K-Means clustering for the visual understanding. We check the vulnerability of the models by the adversarial examples. We applied Zeroth Order Optimization adversarial attack on the models and compute the attack accuracy.
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The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
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Mindlin Plate data used for training DNN surrogate model for Uncertainty Quantification.
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Visual systems can improve transitions between different locomotion mode controllers (e.g., level-ground walking to stair ascent) by sensing the walking environment prior to physical interactions. Here we developed StairNet to support the development of vision-based stair recognition systems for robotic leg control. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.
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In blast furnace iron-making process (BFIP), there is a significant push to maintain a stable iron-making process and ensure process at maximum efficiency. While some control systems can compensate for multiple types of disturbances when faults occur, some significant process faults often require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnostic system to efficiently identify these faults so that operators can deal with them quickly.
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Retail Gaze, a dataset for remote gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles.
Each image captures the third-person view of the customer and shelves. Location of the gaze point, the Bounding box of the person's head, segmentation masks of the gazed at product areas are provided as annotations.
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