Introduction
Welcome to the new challenging event-based multi-object tracking dataset (DSEC-MOT) repository. Our goal is to provide a challenging and diverse event-based MOT dataset with various real-world scenarios to facilitate the objective and comphrehensive evaluation of event-based multi-object tracking algorithms. This dataset, built upon DSEC, contains a variety of traffic entities and complex scenarios, aiming to address the current lack of event-based MOT datasets.
This dataset consists of 'circles' (or 'friends lists') from Facebook. Facebook data was collected from survey participants using this Facebook app. The dataset includes node features (profiles), circles, and ego networks.
This dataset consists of 'circles' (or 'friends lists') from Facebook. Facebook data was collected from survey participants using this Facebook app. The dataset includes node features (profiles), circles, and ego networks.
The attachment "serial_data_dvl_20240729.txt" presents the DVL sensor data collected during the experiment in this paper, including information such as timestamps, transducer data, and velocity. The line beginning with "wru" indicates the status and information of the four transducers. The four data points following "wrx" represent, in sequence, the time interval since the last data collection (in milliseconds), x-axis velocity, y-axis velocity, and z-axis velocity. The four numbers following "wrt" represent the distances from the four transducers to the seabed.
The TripAdvisor online airline review dataset, spanning from 2016 to 2023, provides a comprehensive collection of passenger feedback on airline services during the COVID-19 pandemic. This dataset includes user-generated reviews that capture sentiments, preferences, and concerns, allowing for an in-depth analysis of shifting customer priorities in response to pandemic-related disruptions. By examining these reviews, the dataset facilitates the study of evolving passenger expectations, changes in service perceptions, and the airline industry's adaptive strategies.
This dataset can be used for vulnerability detection. This repository is devised to explain vulEmbedding,
First "altKlasörTaraTahmin.R" file is for searching code files to generate suitable numeric matrix,
createKeywordMatrix.R is for generating keyword matrix, thereby checking vulnerabilities,
sphericalLabeling.R is for generating spherical labeling.
Final you can run deepnetVersion2.R to produce vulnerability prediction.
If you are not concered with generating data from source code,
