6,409 research outputs found
Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression
In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream
Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms
Music streaming services are increasingly popular among younger generations
who seek social experiences through personal expression and sharing of
subjective feelings in comments. However, such emotional aspects are often
ignored by current platforms, which affects the listeners' ability to find
music that triggers specific personal feelings. To address this gap, this study
proposes a novel approach that leverages deep learning methods to capture
contextual keywords, sentiments, and induced mechanisms from song comments. The
study augments a current music app with two features, including the
presentation of tags that best represent song comments and a novel map metaphor
that reorganizes song comments based on chronological order, content, and
sentiment. The effectiveness of the proposed approach is validated through a
usage scenario and a user study that demonstrate its capability to improve the
user experience of exploring songs and browsing comments of interest. This
study contributes to the advancement of music streaming services by providing a
more personalized and emotionally rich music experience for younger
generations.Comment: In the Proceedings of ChinaVis 202
Text Analytics for Android Project
Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis,
automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article
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