Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms A Comparative Study

Medical imaging has been employed to support medical diagnosis and treatment . It may also provide crucial information to surgeons to facilitate optimalsurgical preplanning and perioperative management . This paper addresses to propose the most efficient DL architectures for Liversegmentation by adapting and comparing state-of-the-art DL frameworks, studiedin different disciplines .…

D Net Siamese based Network with Mutual Attention for Volume Alignment

Alignment of contrast and non-contrast-enhanced imaging is essential for thequantification of changes in several biomedical applications . Existing deep learning-based methods for alignment require a commontemplate or are limited in rotation range . We present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CTscans that additionally does not require a prior standard template .…

E cheating Prevention Measures Detection of Cheating at Online Examinations Using Deep Learning Approach A Case Study

This study addresses the current issues in online assessments, which are particularly relevant during the Covid-19 pandemic . The intelligence agent monitors the behaviour of the students and hasthe ability to prevent and detect any malicious practices . It can be used toassign randomised multiple-choice questions in a course examination and beintegrated with online learning programs .…

A novel reconstruction technique for two dimensional Bragg scatter imaging

We introduce a new reconstruction technique for two-dimensional BraggScattering Tomography . Our method uses a combination of ideas frommultibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts . We then test our algorithm in avariety of Monte Carlo (MC) simulated examples of practical interest in airportbaggage screening and threat detection .…

Corrective Information Does Not Necessarily Curb Social Disruption

The authenticity ofinformation on a social networking service (SNS) is unknown, and falseinformation can be easily spread . Many studies have been conducted on methods to control the spread of misinformation on socialnetworking sites . This study models the impact of the reduction of misinformation and the diffusion of corrective information on society .…

Work Patterns of Software Engineers in the Forced Working From Home Mode

The COVID-19 outbreak has caused a major disruption worldwide . But what happened to companies developing digital services? Were they interrupted as much or at all? And how has the enforcedWorking-From-Home (WFH) mode impacted their ability to deliver software? We hear that some managers are concerned that their engineers are not working effectively from home, or even lack the motivation to work in general, that teams lose touch and that managers do not notice when things go wrong .…

Viral Visualizations How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Controversial understandings of the coronavirus pandemic have turned datavisualizations into a battleground . People who mistrust the scientific establishment often deploy the same rhetorics of data-drivendecision-making used by experts, but to advocate for radical policy changes . We document an epistemological gap that leads pro- and anti-mask groups to drawdrastically different inferences from similar data .…

CheXtransfer Performance and Parameter Efficiency of ImageNet Models for Chest X Ray Interpretation

Deep learning methods for chest X-ray interpretation typically rely onpretrained models developed for ImageNet . This paradigm assumes that betterImageNet-pretrained weights provide a performance boost over randominitialization . We find that,for models without pretraining, the choice of model family influencesperformance more than size within a family for medical imaging tasks .…

Digital Contact Tracing Large scale Geolocation Data as an Alternative to Bluetooth based Apps Failure

The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic . None of them has managed to attract the number of active users required to achieve an efficientoperation . This urges the research community to re-open the debate and explore new avenues that lead to efficient contact-Tracing solutions .…

From Gen Z Millennials to Babyboomers Portraits of Working from Home during the COVID 19 Pandemic

Since March 2020, companies nationwide have started work from home (WFH) dueto the rapid increase of COVID-19 confirmed cases in an attempt to help prevent the coronavirus from spreading and rescue the economy from the pandemic . We perform an ordinary least square regression to investigate therelationship between the sentiment about WFH and user characteristics including gender, age, ethnicity, median household income, and population density .…

Covid 19 classification with deep neural network and belief functions

Computed tomography (CT) image provides useful information for radiologiststo diagnose Covid-19 . However, visual analysis of CT scans is time-consuming . In this paper, we propose a belief function-based convolutionalneural network with semi-supervised training . Our results are more reliable andexplainable than those of traditional deep learning-based classification models.…

Classification of fNIRS Data Under Uncertainty A Bayesian Neural Network Approach

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form ofBrain-Computer Interface (BCI) It is used for the imaging of brainhemodynamics and has gained popularity due to the certain pros it poses overother similar technologies . Since hemodynamic responses arecontaminated by physiological noises, several methods have been implemented inthe past literature to classify the responses in focus from the unwanted ones .…

Artificial Intelligence for Emotion Semantic Trending and People Emotion Detection During COVID 19 Social Isolation

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined . Early detection of emotional feelings and their trends helpimplement timely intervention strategies . Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics .…

Impact of COVID 19 on Adoption of IoT in Different Sectors

COVID-19 has disrupted normal life and has enforced a substantial change inthe policies, priorities and activities of individuals, organisations andgovernments . These changes are proving to be a catalyst for technology andinnovation . In this paper, we discuss the pandemic’s impact on the adoption of the Internet of Things (IoT) in various broad sectors namely \sectors .…

Scared into Action How Partisanship and Fear are Associated with Reactions to Public Health Directives

Differences in political ideology are increasingly appearing as an impediment to successful bipartisan communication from local leadership . We find that conservatives adhere to health directives when they express more fear of the virus . We analyze both official and citizen communications and find that press releases from local and federalgovernment, along with the number of confirmed COVID-19 cases, lead to anincrease in expressions of fear on Twitter .…

VIDA A simulation model of domestic VIolence in times of social DistAncing

The COVID-19 pandemic led Brazil to recommend and, at times, imposes social distancing, with the partial closure of economic activities, schools,and restrictions on events and public services . Preliminary evidence shows thatintense coexistence increases domestic violence, while social distancingmeasures may have prevented access to public services and networks,information, and help .…

Model Generalization on COVID 19 Fake News Detection

We aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) The world is facing unprecedented infodemic with the proliferation of both fake and real information . We suggest the importance of model generalization ability in thistask to step forward to tackle the problem in online socialmedia platforms .…

Identification of COVID 19 related Fake News via Neural Stacking

The solution was within 1.5% of the best performing solution . The proposed solution employs a heterogeneousrepresentation ensemble, adapted for the classification task via an additionalneural classification head comprised of multiple hidden layers . The solution is freely available and the solution is free to download and use it as a guide to identifying Fake News in the future .…

Constraint 2021 Machine Learning Models for COVID 19 Fake News Detection Shared Task

In this system paper we present our contribution to the Constraint 2021COVID-19 Fake News Detection Shared Task . We find our best performing system to be based on alinear SVM, which obtains a weighted average F1 score of 95.19% on test data, lands a place in the middle of the leaderboard (place 80 of 167) In terms of pre-processing, we experiment with various steps like stop word removal, stemming/lemmatization,link removal and more.…

Where you live matters a spatial analysis of COVID 19 mortality

The COVID-19 pandemic has caused ~ 2 million fatalities . Within Mexico City,there is a clear south, north divide with higher mortality in the northernmunicipalities . Deceased patients in these northern municipalities have the highest pre-existing health conditions . The use of hexagonal cartograms is a better approach for spatial mapping of the data in Mexico as it addresses bias in area size and population .…

Remote Pulse Estimation in the Presence of Face Masks

Remote photoplethysmography (rPPG) is a known family of techniques formonitoring blood volume changes from a camera . We foundthat occlusions from face masks affect face video-based rPPG as the meanabsolute error of blood volume estimation is nearly doubled when the face is partially occluded by protective masks .…

The audiovisual resource as a pedagogical tools in times of covid 19 An empirical analysis of its efficiency

The global pandemic caused by the COVID virus led universities to a change inthe way they teach classes, moving to a distance mode . 13 videos were analyzed, which had 16,340 views, coming from at least 1,486 viewers . It was obtained that the visualizations depend on the proximity to the test dates and that although the visualizationtime has a curve that accompanies the duration of the videos, it is limited and the average number of visualizations is 10 minutes and 4 seconds .…

Explaining the Black box Smoothly A Counterfactual Approach

We propose a BlackBox that is explicitly developed for medical imaging applications . Classical approaches assessing feature importance do not explain how a particular anatomical region is relevant to the outcome . We design the loss function to ensure thatessential and potentially relevant details, such as support devices, are preserved in the counterfactually generated images .…

A National Research Agenda for Intelligent Infrastructure 2021 Update

Federal investments in intelligent infrastructure will increase safety and resilience, improve efficiencies and civic services, andbroaden employment opportunities and job growth nationwide . The technologiesthat comprise intelligent infrastructure can also provide keys to solving some of the most vexing challenges we face today, including confronting futurepandemics and natural disasters, achieving sustainability and energy efficiency goals .…

Monocular Depth Estimation for Soft Visuotactile Sensors

Fluid-filled soft visuotactile sensors alleviate key challenges for robust manipulation . They enable reliable grasps along with the ability to obtain high-resolution sensory feedback on contact geometry and forces . We investigate the application of state-of-the-art monocular depthestimation to infer dense internal (tactile) depth maps directly from theinternal single small IR imaging sensor .…

Blockchain platform for COVID 19 vaccine supply management

In the context of the COVID-19 pandemic, the rapid roll-out of a vaccine and implementation of a worldwide immunization campaign is critical . We present such system implementation in which .blockchain technology is used for assuring data integrity and immutability incase of beneficiary registration for vaccination, eliminating identity theft and impersonations .…

Minimizing L1 over L2 norms on the gradient

Recent works have demonstrated that L1/L2 is better than the classictotal variation (the L1 norm on the gradient) to enforce the sparsity of the image gradient . In this paper, we study the L1-L2 minimization for imaging applications . Experimentally, we demonstrate visible improvements of L1 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CTreconstruction .…