Social Distancing Using AI and Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 1816-1822
Author(s):  
Mr. Kiran Mudaraddi
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Open Source ◽  
Suggested Method ◽  
Video Frame ◽  
Social Distancing ◽  
The Social ◽  
The Impact

The paper presents a deep learning-based methodology for detecting social distancing in order to assess the distance between people in order to mitigate the impact of the coronavirus pandemic. The input was a video frame from the camera, and the open-source object detection was pre-trained. The outcome demonstrates that the suggested method is capable of determining the social distancing measures between many participants in a video.

scholarly journals What can scholars learn from Open Source software communities during pandemics

2020 ◽  
Author(s):  
Jonathan Tennant ◽  
Tom Crick
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Scholarly Communication ◽  
Social Demand ◽  
Research Communities ◽  
The Social ◽  
Current Outbreak ◽  
The Impact ◽  
Global Research

When the SARS-CoV-2 outbreak began on January 31, 2020, no-one could have anticipated the impact that it would have on our scholarly communication and publishing systems. That is, perhaps, unless you work on open source software. Right now, global research communities are united to collaborate on solving the threat of the pandemic, sharing resources and knowledge more efficiently and effective than ever before, a process broadly described as ‘open scholarship’ (Dunleavy, 2020). This is essentially akin to how free and open source software (FOSS) communities have been operating now for decades (Willinsky, 2005). Recently, we participated in a “massively open online paper”, or MOOP, that explored the intersections between FOSS and open scholarship (Tennant, Agarwal, et al., 2020). Here, we want to summarise our key findings from that project, and place them in the context of the current outbreak. Critically, this pandemic shows us that many of the pervasive and systemic issues surrounding the evaluation, valuation, use and operationalisation of “openness” in scholarship can be extremely easily bypassed when the social demand and urgency is there, thus showing that the primary barriers towards open scholarship are inherently political and not technical.

scholarly journals A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19

2021 ◽  
Author(s):  
Carl Bonander ◽  
Debora Stranges ◽  
Johanna Gustavsson ◽  
Matilda Almgren ◽  
Malin Inghammar ◽  
...  
Keyword(s):  
Older Adults ◽  
Regression Discontinuity ◽  
Model Specification ◽  
Regression Discontinuity Design ◽  
Sharp Drop ◽  
Social Distancing ◽  
Disease Outcomes ◽  
The Social ◽  
National Register Data ◽  
The Impact

Objectives: To study the impact of non-mandatory, age-specific social distancing recommendations for older adults (70+ years) in Sweden on isolation behaviors and disease outcomes during the first wave of the COVID-19 pandemic. Methods: Our study relies on self-reported isolation data from COVID Symptom Study Sweden (n = 96,053) and national register data on COVID-19 hospitalizations, deaths, and confirmed cases. We use a regression discontinuity design to account for confounding factors, exploiting the fact that exposure to the recommendation was a discontinuous function of age. Results: By comparing individuals just above to those just below the age limit for the policy, our analyses revealed a sharp drop in the weekly number of visits to crowded places at the 70-year-threshold (-13%). Severe COVID-19 cases (hospitalizations or deaths) also dropped abruptly by 16% at the 70-year-threshold. Our data suggest that the age-specific recommendations prevented approximately 1,800 to 2,700 severe COVID-19 cases, depending on model specification. Conclusion: The non-mandatory, age-specific recommendations helped control the COVID-19 pandemic in Sweden.

The Impact of Open Source Development on the Social Construction of Intellectual Property

2008 ◽  
pp. 3777-3805
Author(s):  
Bernd Carsten Stahl
Keyword(s):  
Intellectual Property ◽  
Social Construction ◽  
Open Source ◽  
Open Source Software ◽  
Natural Rights ◽  
The Social ◽  
Software Changes ◽  
Information And Communication ◽  
The Impact ◽  
Open Source Development

This chapter discusses the impact that open source software has on our perception and use of intellectual property. The theoretical foundation of the paper is constructionist in that it holds intellectual property to be a social construction that is created and legitimized by narratives. In a first step, the chapter recounts the narratives that are usually found in the literature to justify the creation and protection of intellectual property. The two most important streams of narratives are the utilitarian and the natural rights arguments. In a second step, the paper proceeds to the impact that the use of information and communication technology (ICT) has on the narratives of intellectual property. From there, the chapter progresses to a discussion of the impact of open source software on these narratives. It will be argued that open source software changes our perception of intellectual property because it offers evidence that some of the classical narratives are simplistic. At the same time it will become clear that open source is not a frontal assault on intellectual property because it is partly based on ownership of intellectual artefacts. The conclusion discusses how this change of narratives caused by open source software may reflect on our institutions, laws, and regulations of intellectual property.

Social distancing in a social therapy environment

2020 ◽  
Vol 41 (3/4) ◽  
pp. 113-127
Author(s):  
Geraldine Ann Akerman ◽  
Emily Jones ◽  
Harry Talbot ◽  
Gemma Grahame-Wright
Keyword(s):  
Social Isolation ◽  
Current Practice ◽  
Social Therapy ◽  
Content Type ◽  
Social Distancing ◽  
Emotional Environment ◽  
The Social ◽  
The Uk ◽  
The Impact

Purpose This paper aims to describe the impact of the COVID-19 pandemic on a prison-based therapeutic community (TC). Design/methodology/approach The paper takes the form of a case study where the authors reflect on their current practice, using the findings of research on social isolation and the overarching TC principles to explore the effect of the pandemic on the TC at HMP Grendon. The authors consider how the residents and staff adjusted to the change as the parameters changed when the social distancing rules were imposed and how they adapted to the prolonged break to therapy. Sections in the paper were written by a resident and an operational member of staff. The authors conclude with their thoughts on how to manage the consequences the lockdown has brought and start to think about what returning to “normality” might mean. Findings The paper describes the adjustments made by the residents and staff as the UK Government imposed the lockdown. The authors, including a resident and an operational member of staff comment on the psychological and practical impact these adjustments had. The thought is given to the idea of “recovery”, returning to “normality” and how this study can be best managed once restrictions are lifted. Research limitations/implications At the time of writing, there are no confirmed cases of COVID-19 at HMP Grendon. The measures and commitment from all staff and residents in the prison to keep the prison environment safe may in part account for this. This paper explores the effects of lockdown on the emotional environment in a TC and highlights the consequences that social isolation can have on any individual. To the authors’ knowledge, there is currently no research undertaken on the impact of lockdown/social isolation on a TC. This research would be useful, as the authors postulate from reflections on current practice that the effects of the lockdown will be greater in a social therapy environment. Originality/value HMP Grendon started in 1962, as this time there have been no significant events that have meant the suspension of therapy for such a sustained period. It is, therefore, important that the impact of such is considered and reflected upon.

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020 ◽  
Vol 12 (18) ◽  
pp. 3053 ◽  
Author(s):  
Thorsten Hoeser ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Land Surface ◽  
Large Scale ◽  
Earth Observation ◽  
Observation Data ◽  
Fine Grained ◽  
The Impact ◽  
Object Dynamics

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

scholarly journals Implementation of Tensor Flow for Real-time Object Detection

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2342-2345
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Open Source ◽  
Real Time ◽  
Mobile Web ◽  
Ongoing Process

Tensor Flow is an open-source Machine Learning library for research and creation. Tensor Flow offers APIs for beginners and specialists to create for work desktop, mobile, web, and cloud. The best utilizations of Google's Tensor flow are the best applications for deep learning . Deep Learning is extraordinary at example acknowledgment/machine recognition, and it's being connected to pictures, video, sound, voice, content and time arrangement information. It groups and bunch information like that with now and again superhuman precision. This can be actualized for the acknowledgment of the diverse items, for example, Ball, Cat, Bottle, Car and so forth. It can utilize Android as its stage with to utilize the cell phone's camera to prepare the informational indexes and perceive diverse items in ongoing process.

scholarly journals Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
F. Nyabadza ◽  
F. Chirove ◽  
C. W. Chukwu ◽  
M. V. Visaya
Keyword(s):  
South Africa ◽  
Deterministic Model ◽  
Cumulative Number ◽  
The Novel ◽  
Social Distancing ◽  
The Social ◽  
Potential Impact ◽  
Policy Direction ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

scholarly journals Evaluating Classical Airplane Boarding Methods Considering COVID-19 Flying Restrictions

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1087 ◽  
Author(s):  
Liviu-Adrian Cotfas ◽  
Camelia Delcea ◽  
R. John Milne ◽  
Mostafa Salari
Keyword(s):  
Health Risk ◽  
Health Risks ◽  
Social Distance ◽  
The Novel ◽  
Zone Method ◽  
Economic Productivity ◽  
Social Distancing ◽  
The Social ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus (SARS-CoV-2) has imposed the need for a series of social distancing restrictions worldwide to mitigate the scourge of the COVID-19 pandemic. This applies to many domains, including airplane boarding and seat assignments. As airlines are considering their passengers’ safety during the pandemic, boarding methods should be evaluated both in terms of social distancing norms and the resulting efficiency for the airlines. The present paper analyzes the impact of a series of restrictions that have been imposed or mooted worldwide on the boarding methods used by the airlines, featuring the use of jet-bridges and one-door boarding. To compare the efficacy of classical airplane boarding methods with respect to new social distancing norms, five metrics were used to evaluate their performance. One metric is the time to complete the boarding of the airplane. The other four metrics concern passenger health and reflect the potential exposure to the virus from other passengers through the air and surfaces (e.g., headrests and luggage) touched by passengers. We use the simulation platform in NetLogo to test six common boarding methods under various conditions. The back-to-front by row boarding method results in the longest time to complete boarding but has the advantage of providing the lowest health risk for two metrics. Those two metrics are based on passengers potentially infecting those passengers previously seated in the rows they traverse. Interestingly, those two risks are reduced for most boarding methods when the social distance between adjacent passengers advancing down the aisle is increased, thus indicating an unanticipated benefit stemming from this form of social distancing. The modified reverse pyramid by half zone method provides the shortest time to the completing boarding of the airplane and—along with the WilMA boarding method—provides the lowest health risk stemming from potential infection resulting from seat interferences. Airlines have the difficult task of making tradeoffs between economic productivity and the resulting impact on various health risks.

Improving multi-class Boosting-based object detection

2020 ◽  
Vol 28 (1) ◽  
pp. 81-96
Author(s):  
José Miguel Buenaposada ◽  
Luis Baumela
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Data Augmentation ◽  
Detection Performance ◽  
Significant Progress ◽  
Training Techniques ◽  
Multi Scale ◽  
Bounding Box ◽  
Open Issue ◽  
The Impact

In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the context of multi-class Boosting-based object detection. In our experiments we show that use of these training advancements significantly improves the object detection performance.

scholarly journals Finding an optimal distance of social distancing for COVID 19

2021 ◽  
Vol 3 (3) ◽  
pp. 206-220
Author(s):  
J Samuel Manoharan
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Disease Transmission ◽  
Research Work ◽  
Computational Method ◽  
Infection Prevention And Control ◽  
Social Distancing ◽  
Control Approach ◽  
True Detection ◽  
The Impact

Social distancing is a non-pharmaceutical infection prevention and control approach that is now being utilized in the COVID-19 scenario to avoid or restrict the transmission of illness in a community. As a consequence, the disease transmission, as well as the morbidity and mortality associated with it are reduced. The deadly coronavirus will circulate if the distance between the two persons in each site is used. However, coronavirus exposure must be avoided at all costs. The distance varies due to different nations' political rules and the conditions of their medical embassy. The WHO established a social distance of 1 to 2 metres as the standard. This research work has developed a computational method for estimating the impact of coronavirus based on various social distancing metrics. Generally, in COVID – 19 situations, social distance ranging from long to extremely long can be a good strategy. The adoption of extremely small social distance is a harmful approach to the pandemic. This calculation can be done by using deep learning based on crowd image identification. The proposed work has been utilized to find the optimal social distancing for COVID – 19 and it is identified as 1.89 meter. The purpose of the proposed experiment is to compare the different types of deep learning based image recognition algorithms in a crowded environment. The performance can be measured with various metrics such as accuracy, precision, recall, and true detection rate.

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