Artificial Intelligence

The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park.

In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics.  Please cite the following original article where this work was initially presented:

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The C3I Synthetic Face Depth Dataset consists of 3D virtual human models and 2D rendered RGB and GT depth images in zipped version into two folders for male and female.

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Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks, especially multi-step attacks. In this work, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced.

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We created a 2563-image custom dragon fruit image dataset, with 1248 images of raw dragon fruits and 1315 photographs of ripe dragon fruits. The images were taken with the Nikon D5200 DSLR and OnePlus 6's Sony IMX 519 16 megapixel camera. The photographs taken with the DSLR camera had a resolution of 4000 by 6000 pixels, while those taken with the OnePlus6 had a resolution of 3456 by 4608 pixels. They were photographed in natural sunlight. The average temperature during that time was 28°C (84.2°F), with partly sunny skies, 65 percent humidity, and 17 km/h wind speeds.

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The data set has been consolidated for the task of Human Posture Apprehension. The data set consists of two postures namely -

  1. Sitting and,
  2. Standing,

There are images for each of the postures listed above. The images have a dimension of 53X160 to 1845×4608.

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The dialogue corpus is  described in the paper "Anticipating User Intentions in Customer Care Dialogue Systems" and contains a selection of human-chatbot Italian dialogues concerning customer-care requests.

In order to preserve the privacy and company data property, we removed the actual sentences and we present only the annotation described in the paper.

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An indoor positioning testbed was set up to collect location fingerprint dataset for multi-floor environments and an extensive fingerprint measurement campaign was carried out at the second and third floors of Wing B of the Faculty of Engineering (FOE), Multimedia University, on its campus in Cyberjaya, Malaysia. The total area of the evaluation site is approximately around 1112 m2.

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Two in-air signature databases were created. Forty participants voluntarily took part in each of the two databases’ construction. Some of them participated in both databases construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.

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With increasing research on solving Dynamic Optimization Problems (DOPs), many metaheuristic algorithms and their adaptations have been proposed to solve them. However, from currently existing research results, it is hard to evaluate the algorithm performance in a repeatable way for combinatorial DOPs due to the fact that each research work has created its own version of a dynamic problem dataset using stochastic methods. Up to date, there are no combinatorial DOP benchmarks with replicable qualities.

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Vehicle data set in low illumination environment processed with labelimg

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