This paper addresses the problem of indoor radio environment mapping (REM), which has a range of applications in wireless networks. We develop a novel deep learning framework called Trans-REM for solving the indoor REM problem. Unlike most works in this domain, which use convolutional neural networks (CNN), we propose a novel hybrid architecture that combines the complementary strengths of CNNs and transformers. In Trans-REM, a CNN is used for local spatial feature extraction, and a transformer is used for capturing the global context. Additionally, we develop new techniques to generate two auxiliary input images, namely, a line-of-sight image and an antenna radiation pattern image, that assist Trans-REM in learning effectively for indoor environments. Numerical evaluations using a comprehensive dataset show that Trans-REM achieves superior prediction accuracy in complex indoor environments and outperforms state-of-the-art REM methods by at least 15% in terms of mean squared error.

Etiquettes are an essential ingredient of dayto-day interactions among people.Moreover, etiquettes are region-specific, and etiquettes in one region might contradict those in other regions.In this paper, we propose EtiCor, an Etiquettes Corpus, having texts about social norms from five different regions across the globe.The corpus provides a test bed for evaluating LLMs for knowledge and understanding of region-specific etiquettes.Additionally, we propose the task of Etiquette Sensitivity.We experiment with state-of-the-art LLMs (Delphi, Falcon40B, and GPT-3.5).Initial results indicate that LLMs, mostly fail to understand etiquettes from regions from non-Western world. * Equal Contributions 1In this paper, we use the term etiquette and social norm inter-changeably Wikipedia DataPercentage

Reduction of the image colors, which is also called color quantization (CQ), has been the focus of recent research interest. It is as an integral part of various digital image related areas such as compression, segmentation etc.. Neural networks play a significant role in either assisting conventional color quantization techniques or providing standalone solutions for color quantization. In the present work three new algorithms have been proposed using modified forward only counterpropogation network (MFOCPN). These algorithms introduce, 1) sub-clustering in Kohonen layer for enhancing the clustering process, 2) a new entropy metric based initialization of the Kohonen layer for efficient color-map design and faster convergence of network. Further, the two approaches are merged, to yield third algorithm to achieve better results. The proposed algorithms have been tested on standard test image.

In this paper, learning algorithm for a single multiplicative spiking neuron (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is found that a single MSN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and function-approximation are illustrated. It has been observed that the inclusion of few more biological phenomenon in artificial neural networks can make them more prevailing.