There are various types of digital content protection techniques available in the market. Effectively securing the content is the main challenge as we have various advance tricks & methods available that can easily hack our premium content. So, delivering the content like OTT & Digital TV services to end-user makes advance data security a very interesting topic. This paper focuses on a key concept to use software-based encryption for Broadcast data security. This paper covers complete information about live data processing through broadcast equipment. The main point is securing the content through softwarebased encryption that will reduce the additional hardware needed to encrypt & decrypt the live content. The simplified approach of securing the content via open-source tools & platform makes the entire work interesting & cost-effective.
It is already reported in the literature that the performance of a machine learning algorithm is greatly impacted by performing proper Hyper-Parameter optimization. One of the ways to perform Hyper-Parameter optimization is by manual search, but that is time-consuming. Some of the common approaches for performing Hyper-Parameter optimization are Grid search Random search and Bayesian optimization using Hyperopt. In this paper, we propose a brand new approach for hyperparameter improvement i.e. Randomized-Hyperopt and then tune the hyperparameters of the XGBoost i.e. the Extreme Gradient Boosting algorithm on ten datasets by applying Random search, Randomized-Hyperopt, Hyperopt and Grid Search. The performances of each of these four techniques were compared by taking both the prediction accuracy and the execution time into consideration. We conclude that the Randomized-Hyperopt method is the best performer when compared to that of the other three conventional methods for hyper-parameter optimization of XGBoost.
The objective of developing a recommender system is to aid users by recommending products that might be of interest to them. In this, the collaborative filtering technique is one of the widely used methods where similarities are calculated among users/items to provide personalized recommendation. In order to calculate the similarity, various similarity measures are used. Most of these similarity methods do not perform satisfactorily in the presence of cold start users. A user is considered cold start if he/she has rated less than twenty items. In case of such users, the minimum available ratings data has to be utilized to recommend items. To resolve this problem, we propose a new similarity measure based on both City Block (CB) and Jaccard measure (CBJ). The co-rated items are considered by City Block while Jaccard measure considers the common items for similarity computation. Thus, when both of these measures are combined, they consider all the co-rated and common items. The main advantage of using CBJ is the reduced computational complexity involved in finding the similarity as compared to other similarity methods. To validate CBJ, we conduct experiments on Film Trust and MiniFilm data sets. The recommendation results on Film trust data set having 872 cold start users out of1508 users and MiniFilm data set having all the 55 cold start users reveal that the proposed CBJ method outperforms other existing methods.
