Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Distributed Adaptive Video Streaming using Inter-Server Data Distribution and Agent-based Adaptive Load Balancing
    (Institute of Electrical and Electronics Engineers Inc., 2020) Bhowmik, M.; Raghunandan, A.; Rudra, B.
    As the number and hours of videos available within an organisation increases, as well as it's demand, the need for fast video streaming applications arises. Cloud based services are not cost effective and are not an ideal choice for storing the ever-increasing video data that is usually stored and used only within a particular organisation, like a University. Hence, this paper proposes a web based system design to store and stream videos at a small-scale within an organisation. To improve the video viewing experience for the user, the system is flexible to handle sudden changes, like increase in number of requests. The system requires the use of a cluster of servers to deliver the content as a single server cannot handle the load as number of requests increases. This requires effective load distribution among the servers. This paper proposes a way to design this system for adaptive video streaming. This system is highly scalable and can handle high loads, i.e. a higher number of users connecting to the application simultaneously. This paper proposes an algorithm called inter-server load balancing algorithm with Adaptive Agent-based load balancing to solve this problem. The algorithms also incorporates dynamic video resolution delivery techniques to ensure smooth viewing experience in the whole user experience irrespective of the network speed and bandwidth. © 2020 IEEE.
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    Transparency in Content and Source Moderation
    (Springer, 2023) C, A.R.; D, C.S.; D V, P.; Chandavarkar, B.R.
    Content moderation is defined as the process of screening and monitoring user-generated content online. To provide a safe environment for both users and brands, platforms must moderate content to ensure that it falls within pre-established guidelines of acceptable behavior specific to the platform and its audience. Many social media companies employ thousands of employees or volunteers to moderate content manually. These moderators discuss the nature of any questionable posts off-site and remove them if they are deemed inappropriate. Certain platforms also employ automated moderation of content through machine learning models. However, many of them often do not give users any or accurate reasons when their posts are taken down. This lack of transparency in moderation can cause users to believe that their posts were evaluated in a biased manner. To increase users’ trust in the unbiased nature of a platform and still allow for extensive and robust content moderation, we propose a novel algorithm in this chapter. An adaptive machine learning model is used as the initial moderation layer, and then users are allowed to moderate posts through a trust-based social network algorithm. Since machine learning models can gradually improve their performance through feedback and feedback is given in a self-policing fashion, the system enforces both accuracy and transparency for content moderation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.