Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.
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Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, A dataset containing images of indoor scenes and annotations of instance segmentation is developed in this research.
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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 -
- Sitting and,
- 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. Each participant signs in the air five signatures and imitates five signatures of five other participants.
The proposed protocol for data acquisition consists of two ways for the two in-air signature databases respectively. In the first database, the participant sign in the air directly in front of the camera. In the second database, the participants sign in the air using a transparent glass plate between them and the camera.
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