One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99 and NSLKDD benchmark data sets were generated a decade ago. However, numerous current studies showed that for the current network threat environment, these data sets do not inclusively reflect network traffic and modern low footprint attacks.

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The dataset consists of 60285 character image files which has been randomly divided into 54239 (90%) images as training set 6046 (10%) images as test set. The collection of data samples was carried out in two phases. The first phase consists of distributing a tabular form and asking people to write the characters five times each. Filled-in forms were collected from around 200 different individuals in the age group 12-23 years. The second phase was the collection of handwritten sheets such as answer sheets and classroom notes from students in the same age group.

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The purpose of this challenge is to provide standardization of methods for assessing and benchmarking deep learning approaches to ultrasound image formation from ultrasound channel data that will live beyond the challenge.

Last Updated On: 
Mon, 07/19/2021 - 08:40
Citation Author(s): 
Muyinatu A. Lediju Bell, Jiaqi Huang, Alycen Wiacek, Ping Gong, Shigao Chen, Alessandro Ramalli, Piero Tortoli, Ben Luijten, Massimo Mischi, Ole Marius Hoel Rindal, Vincent Perrot, Hervé Liebgott, Xi Zhang, Jianwen Luo, Eniola Oluyemi, Emily Ambinder

The year 2018 was declared as "Turkey Tourism Year" in China. The purpose of this dataset, tourists prefer Turkey to be able to determine. The targeted audience was determined through TripAdvisor. Later, the travel histories of individuals were gathered in four different groups. These are the individuals’ travel histories to Europe (E), World (W) Countries and China (C) City/Province and all (EWC). Then, "One Zero Matrix (OZ)" and "Frequency Matrix (F)" were created for each group. Thus, the number of matrices belonging to four groups increased to eight.

 

Instructions: 

The operational steps of the study are given in Fig. According to this, firstly, the targeted audience was determined through TripAdvisor. Later, the travel histories of individuals were gathered in four different groups. These are the individuals’ travel histories to Europe (E), World (W) Countries and China (C) City/Province and all (EWC). Then, "One Zero Matrix (OZ)" and "Frequency Matrix (F)" were created for each group. Thus, the number of matrices belonging to four groups increased to eight.

 

For more information, please read the article.

 

Publication.

İbrahim Topal, Muhammed Kürşad Uçar, "Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists", IEEE Open Access.

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Water meter dataset. Contains 1244 water meter images. Assembled using a crowdsourcing platform Yandex.Toloka.

Instructions: 

The dataset consists of 1244 images.

File name consists of:

1) water meter id

2) water meter readings

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE

Instructions: 

 

 

Image name format : 

"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"

 

backgroundID: 

1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office

 

 

objectOrientationID: 

1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top

 

 

objectID:

 1-100

 

 

challengeType: 

No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise

challengeLevel: 

A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.

 

 

 

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

Instructions: 

The name format of the video files are as follows: “sequenceType_sequenceNumber_challengeSourceType_challengeType_challengeLevel.mp4”

·         sequenceType: 01 – Real data 02 – Unreal data

·         sequenceNumber: A number in between [01 – 49]

·         challengeSourceType: 00 – No challenge source (which means no challenge) 01 – After affect

·         challengeType: 00 – No challenge 01 – Decolorization 02 – Lens blur 03 – Codec error 04 – Darkening 05 – Dirty lens 06 – Exposure 07 – Gaussian blur 08 – Noise 09 – Rain 10 – Shadow 11 – Snow 12 – Haze

·         challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.

Test Sequences

We split the video sequences into 70% training set and 30% test set. The sequence numbers corresponding to test set are given below:

[01_04_x_x_x, 01_05_x_x_x, 01_06_x_x_x, 01_07_x_x_x, 01_08_x_x_x, 01_18_x_x_x, 01_19_x_x_x, 01_21_x_x_x, 01_24_x_x_x, 01_26_x_x_x, 01_31_x_x_x, 01_38_x_x_x, 01_39_x_x_x, 01_41_x_x_x, 01_47_x_x_x, 02_02_x_x_x, 02_04_x_x_x, 02_06_x_x_x, 02_09_x_x_x, 02_12_x_x_x, 02_13_x_x_x, 02_16_x_x_x, 02_17_x_x_x, 02_18_x_x_x, 02_20_x_x_x, 02_22_x_x_x, 02_28_x_x_x, 02_31_x_x_x, 02_32_x_x_x, 02_36_x_x_x]

The videos with all other sequence numbers are in the training set. Note that “x” above refers to the variations listed earlier.

The name format of the annotation files are as follows: “sequenceType_sequenceNumber.txt“

Challenge source type, challenge type, and challenge level do not affect the annotations. Therefore, the video sequences that start with the same sequence type and the sequence number have the same annotations.

·         sequenceType: 01 – Real data 02 – Unreal data

·         sequenceNumber: A number in between [01 – 49]

The format of each line in the annotation file (txt) should be: “frameNumber_signType_llx_lly_lrx_lry_ulx_uly_urx_ury”. You can see a visual coordinate system example in our GitHub page.

·         frameNumber: A number in between [001-300]

·         signType: 01 – speed_limit 02 – goods_vehicles 03 – no_overtaking 04 – no_stopping 05 – no_parking 06 – stop 07 – bicycle 08 – hump 09 – no_left 10 – no_right 11 – priority_to 12 – no_entry 13 – yield 14 – parking

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.

Instructions: 

The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"

  • sequenceType: 01 - Real data 02 - Unreal data

  • signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking

  • challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze

  • challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.

  • Index: A number shows different instances of traffic signs in the same conditions.

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1306 Views

This dataset includes all letters from Turkish Alphabet in two parts. In the first part, the dataset was categorized by letters, and the second part dataset was categorized by fonts. Both parts of dataset includes the features mentioned below.

  • 72, 20 AND 8 POINT LETTERS
  • UPPER AND LOWER CASES

The all characters in Turkish Alphabet are included (a, b, c, ç, d, e, f, g, ğ, h, ı, i, j, k, l, m, n, o, ö, p, r, s, ş, t, u, ü, v, y, z).

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This dataset contains the actual sensor and calculated process variables in a winder station in a paper mill. Several Process variables change in time with the change of the rewind diameter. I provided the process data for two sets, in future I will add more data. Advanced time series forcasting techniques can be used to estimate many process variables considering the rewind diameter as the time axis.

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