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The paper presents an elegant approach for designing linear phase low pass digital FIR filter using swarm and evolutionary algorithms. Classical gradient based approaches are not efficient enough for accurate design and thus evolutionary approach is considered to be a better choice. In this paper a hybrid of Genetic Algorithm and Particle Swarm Optimization algorithm with varying neighbourhood topology, namely Genetic Lbest Particle Swarm Optimization with Dynamically Varying Neighbourhood (GLPSO DVN) is used to find the filter coefficients. In this work two objective functions (error metrics) are minimized. The first one is based on stop and pass band ripple and the second one studies the mean square error between the ideal and actual designed filter. The hybrid algorithm is found to produce fitter candidate solution than the classical Lbest PSO. The results are compared with the results obtained by solving the same problem using Lbest PSO (LPSO). It is also observed that GLPSO DVN gives better results than LPSO and as well LPSO DVN.

We present a multi-modal dialog system to assist online shoppers in visually browsing through large catalogs. Visual browsing is different from visual search in that it allows the user to explore the wide range of products in a catalog, beyond the exact search matches. We focus on a slightly asymmetric version of the complete multi-modal dialog where the system can understand both text and image queries, but responds only in images. We formulate our problem of "showing k best images to a user" based on the dialog context so far, as sampling from a Gaussian Mixture Model in a high dimensional joint multi-modal embedding space, that embed both the text and the image queries. Our system remembers the context of the dialog and uses an exploration-exploitation paradigm to assist in visual browsing. We train and evaluate the system on a multi-modal dialog dataset that we generate from large catalog data. Our experiments are promising and show that the agent is capable of learning and can display relevant results with an average cosine similarity of 0.85 to the ground truth. Our preliminary human evaluation also corroborates the fact that such a multi-modal dialog system for visual browsing is well-received and is capable of engaging human users.