Autonomous detection of occluded humans in sparsely populated area

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The data comes from the Qingdao Public Security Traffic Information Service Network, which provides the city's traffic index and is a comprehensive system. The traffic operation index is a comprehensive index for the theoretic evaluation of the overall operation of the road network. Compared with the traditional parameters such as vehicle speed and flow, it has the characteristics of being intuitive and simple.

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This dataset contains  job and their skills extracted from the job adverisments. 

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This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).

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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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This dataset contains 1050 multi-pattern multi-bin wafer bin maps (WBMs) synthesized from WM-811K binary WBM dataset and real world multi-bin WBMs using a trained pix2pix model.

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The Baseline set described in the submitted IEEE article  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.

The data set in .csv format contains the columns timestamp, user_id, session_id, acc_var, hr, rmssd, sdnn, st, eda, eda_freq, bm, location, and concentration. The time step is 5 seconds.

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One to three columns of data are X, Y, Z coordinates respectively

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One to three columns of data are X, Y and Z coordinates respectively.

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Mindlin Plate data used for training DNN surrogate model for Uncertainty Quantification.

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