ViFoDAC is a collection of Authentic videos and Forged videos. The dataset has a total of 16 Authentic videos and 16 Forged videos. The Authentic videos are camera recorded whereas the Forged videos are edited using Adobe Premiere Pro and Wondershare Filmora software. The dataset can be used to train and optimise video identification models. This dataset can be used for the Research and Development of fake video classification. 

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Most present wireless power transfer (WPT) systems are designed for 400-V EVs and its compatibility for a higher battery pack voltage is barely studied. To adapt for different WPT charging scenarios, this paper proposes a resonant inductor integrated-transformer (RIIT) based receiver. The design guideline, power losses, and power transfer capacity of the proposed system are presented. The proposed receiver is compact, low-cost, reliable, easy-to-implement, and compatible for WPT systems with different battery pack voltages without any significant change of system parameters.

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This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

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 The crime rate is increasing at a high rate in India. Terrorist attacks like Mumbai 26/11, Pulwama attack, Pune German Beckary attacks have created terrific fear amongst Indian Society. Video analytics plays a significant role in detecting and predicting such suspicious human activities using deep learning models It will help in reducing the increasing crime rate by preventing treacherous actions. Video analytics analyzes the video content and adds brains to eyes i.e. analytics to the camera. It extracts contents from the video by monitoring the video in real-time.

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A promising technique to realize augmented reality on future light-weight glasses is to offload computationally extensive rendering tasks to the cloud. This however places considerable demands on the network as well as the air interface with respect to latency, reliability and throughput. For evaluation of these architectures and for traffic modelling, a dataset is provided, which contains realistic payloads of cloud-rendered augmented reality in form of video files.

Instructions: 

Provided are the raw video files after rendering with a resolution of 7200x6360 pixels. For low-latency encoding libx264 ffmpeg version 4.2.4 was used with flags -preset ultrafast -tune zerolatency at a target bitrate of 8Mbit/s. The streaming is taking place with ffmpeg and the custom nut output muxer. The resulting packetized output is sent to a UDP port on localhost. The encoded video files as well as the captured traffic traces are provided.  

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The data contains the video presentation of the platform and a supplementary document for the modified IEEE 9 bus system used in simulation.

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The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions. 

Instructions: 

The directory scenes/ contains the videos in mp4 format with actions of interest performed in context of other players present in the scene. The files are arranged in subdirectories according to the action class of the action of interest. The directory actions/ contains the videos of performances of actions by single players isolated from the videos in scenes directory. The files are arranged in subdirectories according to the performed action class. Files are named so that the beginning of the name matches the original video from which the action is extracted. The directory player_detections/ contains the object detections for each frame in the videos.

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Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spec- troscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments.

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The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models.

Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices.

We collect human opinions for each video with a series of carefully designed subjective experiments.

Instructions: 

The Waterloo Quality-of-Experience IV database consists of 1,350 streaming videos (generated from 5 source videos x 2 encoders x 9 network traces x 5 ABR algorithms x 3 viewing devices). The 5 ABR algorithms include RB, BB, FastMPC, Pensieve, and RDOS.

The waterloo_sqoe4_feature.zip contains all meta data such as chunk level bitrate, rebuffering duration, spatial resolution, and MOS.

The waterloo_sqoe4_server_video.zip contains the dash videos on the server.

The waterloo_sqoe4_full.zip contains all the streaming videos in mp4.

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