The paper proposes a preliminary design of crosslayer quality of service applied to congestion control in future Internet. This is an alternative solution to QoS-aware routing, whenever the infrastructure operator cannot add new resources, and/or when re-routing is not possible. Dedicated software, running in each node, collects a list of local parameters such as available transfer rate and one-way delay between all neighbors. Then this real-time status information is distributed to all the innetwork management enabled nodes that are allowed to be reached. Due to the statistics regarding individual link traffic, a minimal network coding scheme, triggered by cross-layer quality of service, is temporarily activated. This system presents an enhanced distributed routing that preserves the performances of the running services, despite the congestion which cannot be eliminated.
With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15,000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data.
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