05-08-2022, 10:44 AM
Let's start by defining some basic concepts related to video-conferencing security. Since the information discussed in these video conferences could be sensitive, data storage needs to be secure and separate from all other networks. Many companies use subscription video-conferencing services that store all video-conferencing data in special locked-down, off-site facilities. Data transmission is the most vulnerable area of video-conferencing security since the data must travel over so many public and private networks to reach its destination. Encryption and network security are the keys to protecting data transmission during a video conference. The level of encryption depends on the sensitivity of the data. For most non-military organizations, the built-in encryption that comes with the video-conferencing product or service is sufficient. The numbers refer to the length of the encryption key, 56 bits or 128 bits. A 128-bit key is nearly impossible to crack because it contains 3.4 X 1038 possible variations. For government and military data transmission, NSA regulations require that organizations install special encryption boxes to protect classified data. Thus, these methods require more inference time even with theoretical computational efficiency and lead to sub-optimal results. However, the key to video recognition is aggregating features across different frames. Most of these methods rely on the assumption that several salient frames are equally important to an effective video representation for video recognition, which may introduce temporal redundancy and lack specific consideration for temporal modeling. A promising alternative direction to reduce the computational complexity of analyzing video content, without sacrifice of recognition accuracy, is representing videos with one clip in a single step. 3D-CNNs methods reveal the superiority owing to its spatio-temporal information extraction. However, traditional clip-level sampling requires to average the predictions of multiple clips, and clips containing visual redundancy will pollute the final results. Inspired by that, we design an efficient video recognition framework that compresses trimmed/untrimmed videos into a single clip by evaluating a clip-based reward on a per-video basis in one turn. para que sirve el paracetamol
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