Emerging technologies enforce strict requirements on future wireless networks such as massive connectivity that cannot be supported with scheduled access. Contention based Non-Orthogonal Multiple Access is a novel technique to overcome strict massive connectivity requirements by efficient use of wireless resources. However, most of the solutions proposed in this direction assumes different loads which would degrade the performance significantly if they would not hold. To stress these assumptions a resource efficiency metric is defined and state of the art solutions are evaluated for varying load regarding this metric. It is shown that the resource efficiency problem in the state of the art can be improved with multiplicity estimation, and hence, we propose Multiplicity estimating Random Access protocol, that adapts to the dynamic loads. This adaptation is evaluated through analytical calculation against the state of the art and it is shown that resource efficiency against with a slight decrease in the metric any load from 1 up to > 10 3 users is supported. In addition, we show how this protocol can be dimensioned and integrated to contention based NOMA.
Increasing volume of user-generated human-centric video content and their applications, such as video retrieval and browsing, require compact representations that are addressed by the video summarization literature. Current supervised studies formulate video summarization as a sequence-to-sequence learning problem and the existing solutions often neglect the surge of human-centric view, which inherently contains affective content. In this study, we investigate the affective-information enriched supervised video summarization task for human-centric videos. First, we train a visual input-driven state-of-the-art continuous emotion recognition model (CER-NET) on the RECOLA dataset to estimate emotional attributes. Then, we integrate the estimated emotional attributes and the high-level representations from the CER-NET with the visual information to define the proposed affective video summarization architectures (AVSUM). In addition, we investigate the use of attention to improve the AVSUM architectures and propose two new architectures based on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We conduct video summarization experiments on the TvSum database. The proposed AVSUM-GRU architecture with an early fusion of high level GRU embeddings and the temporal attention based TA-AVSUM architecture attain competitive video summarization performances by bringing strong performance improvements for the human-centric videos compared to the state-of-the-art in terms of F-score and self-defined face recall metrics.
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