Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped fou...
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In this paper, an event-based tracker is presented. Inspired by recent advances in asynchronous processing of individual events, we develop a direct matching scheme that aligns spatial distributions of events at diffe...
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To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force ...
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There are a lot of technologies have been implemented in the modern cars these days, ranging from preventive braking system to driving assistance. It is believe that equipping car with modern technology would enhance ...
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YouTube is a popular online video-sharing and social media platform, hosting diverse content for all ages. The platform is gradually gaining ground against traditional media, with major turnover increasingly turning t...
YouTube is a popular online video-sharing and social media platform, hosting diverse content for all ages. The platform is gradually gaining ground against traditional media, with major turnover increasingly turning there due to videos and creators' influence, especially in younger audiences. Some of the videos may attain a large number of views often accompanied by many comments. Comments, often focus not only on the content of the video but also on several other aspects and topics. In this paper, we focus on the sentiment analysis of video comments, using a variety of methods we explore the correlation between user comments' sentiment polarity score and the popularity of the relevant video. Some adversities of machine learning or neural network model approach to the task include linguistic irregularities, typing errors, niche vocabulary, sarcastic and ambiguous content, or unrelated events influencing the commented themes. Unlike previous works [1], [2], our focus is centered on leveraging comment sentiment polarity for deriving predictions of the popularity and success of the respective videos. Our methods depend on lexicon-based sentiment analysis approaches like VADER and TextBlob but also on neural network architectures including BERT and RoBERTa. Our experimental analysis resulted in promising results, setting a baseline for this application theme.
In the current era of big data, the generation and preservation of time series data has seen widespread adoption in various sectors such as trade, healthcare, and industry. At the same time, data have become a prized ...
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The pandemic and the various natural disasters related to climate change force Universities to operate remotely during certain periods of time. Synchronous and asynchronous Learning Management System (LMS) platforms a...
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This study aims to propose a precise rigid-body face registration method that does not require an invasive marker attachment for surgical navigation devices. The non-invasive face registration involved the following s...
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All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows ...
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Verifying a new missile design requires a series of successful real-life tests on a firing range in various scenarios. Such an approach is extremely expensive and requires complex infrastructure and logistics. The cos...
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