The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people’s night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.
Abstract-Software composition aims to provide mechanisms for systematic construction based on well-defined software units. Various software composition mechanisms have been defined in the literature for different kinds of software units. In componentbased development, it is desirable to have software units and composition mechanisms that support automated, systematic construction. In this paper, we first survey existing definitions of composition units and the corresponding composition mechanisms, and then use the survey to propose a taxonomy that identifies good candidates for composition units and composition mechanisms for component-based development.
Abstract. Design patterns are typically defined informally, albeit in a standard format, and have to be programmed by the software designer into each new application. Thus although patterns support solution reuse, in practice this does not translate into code reuse. In this paper we argue that to achieve code reuse, patterns should be defined and used in the context of software component models. We show how in such a model, behavioural patterns can be defined as composition operators which can be stored in a repository, alongside components, thus enabling code reuse.
Building large and complex systems in one step (the 'big bang' approach) is a very challenging task, given that humans can only deal with a limited measure of complexity at a time. A more practical approach would be to build such systems incrementally, i.e. iteratively increment an incomplete version of the system under construction until the system is completed. In software engineering, there are such approaches, but they are generally top-down, and not component-based. In this paper we present a componentbased approach, which is bottom-up, and demonstrate its feasibility by applying it to the CoCoME example.
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