The COVID-19 pandemic is a worldwide crisis with impacts that are both devastating and inequitable as effects often fall hardest on communities that are already suffering from economic, social, and political dispariti...
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ISBN:
(纸本)9781728162515
The COVID-19 pandemic is a worldwide crisis with impacts that are both devastating and inequitable as effects often fall hardest on communities that are already suffering from economic, social, and political disparities. Interpretable machine learning (IML) offers the possibility for detailed understanding of this and similar disease outbreaks, allowing subject matter experts to explore the data more thoroughly and find patterns and connections that might otherwise remain hidden. As an active area of research in artificial intelligence, IML has great significance yet numerous technical challenges to overcome. In this paper, we focus on approximating epidemic curves using an interpretable artificial neural network. This is a first step toward a flexible and interpretable modeling framework that we plan to use to study impacts of various demographic, socioeconomic, and other factors on disease outbreaks. We tap into a substantial but little-known collection of IML studies in nonlinear function approximation from engineering mechanics, where domain knowledge including visually observable features of the data is systematically sorted and directly utilized in the initialization of sigmoidal neural networks leading to training success and good generalization. After an introductory review of existing work, we present a feasibility study on approximating a particular epidemic curve leading to a promising result.
In the general domain of telecommunications and networking, there is a relatively new area of green communications techniques that has been attracting the attention of many researchers. It is focused on making communi...
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ISBN:
(纸本)9781728112442
In the general domain of telecommunications and networking, there is a relatively new area of green communications techniques that has been attracting the attention of many researchers. It is focused on making communications and network systems more friendly to the environment. In this paper, we aim to control the demand side of a wireless communications system and deploy techniques for shaping it to reduce the load on the network and to improve the capacity. This is due to the fact that wireless infrastructure, such as base stations, switching centers already consume about 0.5% of the global electric power that cause the carbon emissions, which translates into a carbon footprint of 34g of CO2 for 1 Mbit of transmitted data. We consider a semi-automated decision system that implements a user-in-the-loop (UIL) approach that has been described in the literature in recent years, where in addition we deploy the particle swarm optimization (PSO) algorithm in conjunction with two types of user traffic classes, namely, the Best Effort users and the Guaranteed Bit Rate users to enhance the efficient utilization of the overall system resources. Simulation results validate that these suggested UIL solutions techniques can improve the user capacity of the overall system when compared with the traditional uncontrolled system. Hence, this will have a positive impact on the interactions of communications systems with the environment, ultimately achieving the end purpose of the green mobile data communications.
Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications;however a clear discrepancy between current installations and research-level designs can be easily notic...
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Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications;however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deployment.
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