Artificial Intelligence
To study the driver's behavior in real traffic situations, we conducted experiments using an instrumented vehicle, which comprises:
(i) a camera, installed above the vehicle's side window and oriented toward the driver, and (ii) a Mobile Digital Video Recorder (MDVR).
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The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks.
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To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .
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Data used for evaluation of the Chameleon system. We use it to evaluate capabilities of sensor fusion system that is able to adapot to multiple envrionemnts and monitor activity states within a room. The data set is divided by the two deployments and includes inforamtion for both of the sensors used to test the system. We include two weeks worth of data along with training and testing accuracy results.
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The dataset is pre-processed from two datasets: UCI credit card dataset (https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients) and ULB credit card dataset (https://www.kaggle.com/mlg-ulb/creditcardfraud).
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Surveillance video captured by Multi-intensity infrared illuminator.
GT(ground-truths) :bounding boxes of 'person' in channel 2,4 and 6 by following the Pascal VOC format.
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This dataset presents a collection of coordinates that belongs to paths generated with a 3D disjstkra algorithm,in diferents enviroments,with a grid size equal to one. The output is a six dimension vector that represents the action taken by the agent (z+,z-,y+,y-,x+,x-) based on his pose, sensors readings and the target.
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The iSAID-Reduce100 is a reduced version of the DOTA dataset for instance segmentation task, including 1400 training samples and 1362 validation samples. The images are captured from multiple sensors and cropped to (512, 512).
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