Railway applications. Communications, signalling and processing systems. Software for railway control and protection systems

In unequal societies, the rich may benefit from shaping economic institutions in their favor. This paper analyzes the dynamics of institutional subversion by focusing on public protection of property rights. If this institution functions imperfectly, agents have incentives to invest in private protection of property rights. The ability to maintain private protection systems makes the rich natural opponents of public protection of property rights and precludes grass-roots demand to drive the development of the market-friendly institution. The economy becomes stuck in a bad equilibrium with low growth rates, high inequality of income, and wide-spread rent-seeking. The Russian oligarchs of the 1990s, who controlled large stakes of newly privatized property, provide motivation for this paper.

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Cloud-Based Multimedia Content Protection System

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207448 ◽

2020 ◽

pp. 164-169

Author(s):

B. Aparna ◽

S. Madhavi ◽

G. Mounika ◽

P. Avinash ◽

S. Chakravarthi

Keyword(s):

Large Scale ◽

Protection System ◽

Multimedia Content ◽

Computationally Efficient ◽

Content Protection ◽

Protection Systems ◽

Multimedia Content Protection ◽

High Scalability ◽

Cloud Infrastructures ◽

Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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