Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. Network disruption may occur due to unexpected variations in environmental conditions such as weather, wind, and natural or man-made surroundings, and the influence of the defect is quite severe. Prediction of such undesirable events at a low cost can boost 5G communication reliability, massive network capacity, and decreased latency. This research work makes use of novel preprocessing and feature engineering techniques, followed by a trained decision tree model to predict the occurrence of Radio Link Failure (RLF). This system is designed to predict RLF for not just the next day, but also any of the next 5 days. This prediction supports reliance and increasing demand for good Internet connectivity. In order to achieve accurate RLF prediction, comprehensive data has been used which undergoes preprocessing. To account for the influence of surrounding weather conditions on radio links, the proposed system makes use of information from the past i.e., previous RLFs, and the information from the future i.e., the weather forecast from the weather station around the radio link station. The decision tree model was trained with the integration of feature engineering. A macro-averaged F1-score of 70% and 77% were obtained for RLF prediction for the next day and RLF prediction for the next 5 days, respectively. The results show improvement in performance after the incorporation of feature engineering in the pipeline. Further, an additional metric termed G-Mean is introduced in the paper. Owing to the high imbalance in the dataset, this metric was found to provide a more realistic representation of the results. The G-Mean score was found to be 98.69% and 92.89% for RLF prediction for the next day and RLF prediction for the next 5 days, respectively.

No abstract

Long documents like contracts, financial documents, etc., are often tedious to read through. Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging. These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document. In this work, we describe DYNAMICTOC, a dynamic table of contentbased navigator, to aid in the task of non-linear, persona-based document consumption. DY-NAMICTOC highlights sections of interest in the document as per the aspects relevant to different personas. DYNAMICTOC is augmented with short questions to assist the users in understanding underlying content. This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs. Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DYNAMICTOC.

No abstract