This is a dataset of client-server Round Trip Time delays of an actual cloud gaming tournament run on the infrastructure of the cloud gaming company Swarmio Inc. The dataset can be used for designing algorithms and tuning models for user-server allocation and server selection. To collect the dataset, tournament players were connected to Swarmio servers and delay measurements were taken in real time and actual networking conditions.
For the main dataset, the 189 players and the 9 servers were distributed among 4 different regions: North America, South America, Europe, East Asia. The 9 servers were located in the following cities with their acronyms in the dataset:
Each of the 189 players were able to connect to each of the 9 servers. The following data is registered for each player:
In the dataset file main-dataset.json, every record contains the network delay measurements from a particular player to each of the 9 servers. It should be noted that the URLs and the IP addresses of the servers are provided in a separate file main-dataset-servers.json.
The user ID is a unique 32-character identifier that is generated for each player; for example, 5193b0e1-2412-4338-ac8d-6f519049aa77. The time of access is based on the Unix timestamp which is counted in seconds January 1, 1970; for example, 1528484445170. Longitude and latitude are based on the geo-location of the player; for example, "longitude": "121.0409", "latitude": "14.5832". The Access Support Network is the ISP network in which the player is registered, for example Rogers Communications Canada Inc, Philippine Long Distance Telephone Company, AT&T Services Inc., tec.
Each measurement consisted of sending 11 packets from the player to the server, and the following measurements were obtained (all in ms):
It should be noted that out of the 9 servers, only the 1st server (“nl”) was used for testing the connection, and that can be noted from the field “testing” having the value of “1”. Therefore, the value of “stats” for the first server will have no measurements.
For the secondary dataset, we set up 11 different servers: 1 server owned by Swarmio Media in Toronto and 10 servers using the AWS cloud in the following locations:
The same script as the main dataset was run in the Swarmio client software of 67 players. This time, each server sent 8 packets to each player, and only the average delay was recorded and stored.
The secondary dataset consists of the JSON file secondary-dataset.json, where the keys are the names of the servers, and the values contain a list of the delays to the 67 players. The players IPs are provided in order in a separate file secondary-dataset-users.json. It is also possible to reuse the code that was used to retrieve the measurements in the file HostsUsersRTT.py . The IP addresses of the 11 servers can also be accessed in the file secondary-dataset-servers.json where the key of the record will have the name of the server; for example “N Virginia”, and the value will have the IP address of the server
In contrast to the main dataset, the secondary dataset contains only the delay between the servers and the players whereas the main dataset has more information such as the geo-location and the ISP. This makes the secondary dataset more suitable for testing and verification due to having a single label with only 2 features (IP addresses and city names), while the main dataset contains more features and measurements suitable for training and inference.
EEG brain recordings of ADHD and non-ADHD individuals during gameplay of a brain controlled game, recorded with an EMOTIV EEG headset. It can be used to design and test methods to detect individuals with ADHD.
For details, please see:
Alaa Eddin Alchalabi, S. Shirmohammadi, A. N. Eddin and M. Elsharnouby, “FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game”, IEEE Transactions on Instrumentation and Measurement, Vol. 67, No. 7, July 2018, pp. 1512-1520.
Images of various foods, taken with different cameras and different lighting conditions. Images can be used to design and test Computer Vision techniques that can recognize foods and estimate their calories and nutrition.
Please note that in its full view, the human thumb in each image is approximately 5 cm by 1.2 cm.
For more information, please see:
P. Pouladzadeh, A. Yassine, and S. Shirmohammadi, “FooDD: Food Detection Dataset for Calorie Measurement Using Food Images”, in New Trends in Image Analysis and Processing - ICIAP 2015 Workshops, V. Murino, E. Puppo, D. Sona, M. Cristani, and C. Sansone, Lecture Notes in Computer Science, Springer, Volume 9281, 2015, ISBN: 978-3-319-23221-8, pp 441-448. DOI: 10.1007/978-3-319-23222-5_54
A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next:
You can use all videos for research. Also, you can display the screenshots of some (not all) videos in your own publications. Please check the Allow Researchers to use picture in their paper column in the table to see if you can use a screenshot of a particular video or not. If for a particular video that column is “no”, you are NOT allowed to display pictures from that specific video in your own publications.
The videos are unlabeled, since it is very easy to see the yawning sequences. For more details, please see:
S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD: A Yawning Detection Dataset”, Proc. ACM Multimedia Systems, Singapore, March 19 -21 2014, pp. 24-28. DOI: 10.1145/2557642.2563678
This is an eye tracking dataset of 84 computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. The dataset consists of 135 raw videos (YUV) at 720p and 30 fps with eye tracking data for both eyes (left and right). Male and female players were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands.
- AVI offset represents the frame from which data gathering has been started.
- The 1st frame of each YUV file is the 901st frame of its corresponding AVI file.
- For detailed info and instructions, please see:
Hamed Ahmadi, Saman Zad Tootaghaj, Sajad Mowlaei, Mahmoud Reza Hashemi, and Shervin Shirmohammadi, “GSET Somi: A Game-Specific Eye Tracking Dataset for Somi”, Proc. ACM Multimedia Systems, Klagenfurt am Wörthersee, Austria, May 10-13 2016, 6 pages. DOI: 10.1145/2910017.2910616