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During the past three decades a large body of research has investigated the problem of specifying class intervals for choropleth maps. This work, however, has focused almost exclusively on placing observations in quasi-continuous data distributions into ordinal bins along the number line. All enumeration units that fall into each bin are then assigned an areal symbol that is used to create the choropleth map. The geographical characteristics of the data are only indirectly considered by such approaches to classification. In this article, we design, implement, and evaluate a new approach to classification that places class-interval selection into a multicriteria framework. In this framework, we consider not only number-line relationships, but also the area covered by each class, the fragmentation of the resulting classifications, and the degree to which they are spatially autocorrelated. This task is accomplished through the use of a genetic algorithm that creates optimal classifications with respect to multiple criteria. These results can be evaluated and a selection of one or more classifications can be made based on the goals of the cartographer. An interactive software tool to support classification decisions is also designed and described.

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Keyword spotting (KWS) is a major component of human-computer interaction for smart on-device terminals and service robots, the purpose of which is to maximize the detection accuracy while keeping footprint size small. In this paper, based on the powerful ability of DenseNet on extracting local feature-maps, we propose a new network architecture (DenseNet-BiLSTM) for KWS. In our DenseNet-BiLSTM, the DenseNet is primarily applied to obtain local features, while the BiLSTM is used to grab time series features. In general, the DenseNet is used in computer vision tasks, and it may corrupt contextual information for speech audios. In order to make DenseNet suitable for KWS, we propose a variant DenseNet, called DenseNet-Speech, which removes the pool on the time dimension in transition layers to preserve speech time series information. In addition, our DenseNet-Speech uses less dense blocks and filters to keep the model small, thereby reducing time consumption for mobile devices. The experimental results show that feature-maps from DenseNet-Speech maintain time series information well. Our method outperforms the state-of-the-art methods in terms of accuracy on Google Speech Commands dataset. DenseNet-BiLSTM is able to achieve the accuracy of 96.6% for the 20-commands recognition task with 223K trainable parameters.

Automated or semiautomated surveillance monitoring involves movement tracking and sensor handoff. In order to track moving objects over a large area, sensor coverage needs to overlap significantly. Overlapping coverage can be modeled using the concept of backup coverage, a location modeling approach that seeks to maximize primary and backup coverage simultaneously. This kind of sensor placement problem belongs to the class of NP-hard combinatorial optimization problems, so computational difficulty is expected when solving large problem instances, not to mention the need for dealing with multiple objectives. Beyond this, backup coverage for supporting sensor placement actually brings about confounding problem instances for branch-and-bound approaches because of the trade-off between primary and backup coverage. To address these difficulties, this paper develops a multiobjective evolutionary algorithm for the backup coverage problem to support sensor placement. The solutions of this algorithm are evaluated in terms of computational requirements and solution quality.