The ngram CC classifier performed similarly to manually developed CC classifiers and has advantages of rapid automated creation and updating, and may be used independent of language or dialect.

e growing demand for high-speed networks is increasing the use of high-frequency electromagnetic waves in wireless networks, including in microwave backhaul links and 5G. e relative higher frequency provides a high bandwidth, but it is very sensitive to obstructions and interference. Hence, when positioning a transmi erreceiver pair, the line-of-sight between them should be free of obstacles. Furthermore, the Fresnel zone around the line-of-sight should be clear of obstructions, to guarantee e ective transmission. When deploying microwave backhaul links or a cellular network there is a need to select the locations of the antennas accordingly. To help network planners, we developed an interactive tool that allows users to position antennas in di erent locations over a 3D model of the world. Users can interactively change antenna locations and other parameters, to examine clearance of Fresnel zones. In this paper we illustrate the interactive tool and the ability to test clearance in real-time, to support interactive network planning. CCS CONCEPTS •Networks →Network components; •Information systems →Spatial-temporal systems;

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Spatiotemporal streams are prone to data quality issues such as missing, duplicated and delayed data—when data generating sensors malfunction, data transmissions experience problems, or when data are stored or processed improperly. However, many important real-time applications rely on the continuous availability of stream values, e.g., to monitor traffic flow, resource usage, weather phenomena, and so on. Other non real-time applications that support continuous or offline historical analytics also require high quality data to avoid producing misleading output such as false positives, erroneous conclusions, and decisions. In this article, we study the problem of smoothing streams produced by an overlay of sensors. We present nonparametric (data-driven, distribution free) statistical methods to provide an uninterrupted stream of high-quality spatiotemporal data to real-time applications, even when the raw stream suffers data quality issues, such as noise or missing values. Our novel family of robust methods computes smoothed values (SVs) that could be used as proxies for data of questionable quality. The methods make use of a partition of the monitored area into cells to compute SVs based on historical data and the deviation from normalcy in neighboring spatial cells in a way that outperforms standard regression or interpolation. Our methods use incremental computation for efficiency, and they differ in how the deviations are normalized, e.g., with respect to zeroth-order, first-order, and second-order moments. We use three real data sets to run a suite of experiments and empirically demonstrate the superiority of the method that uses normalization with respect to variability.