## Characterizing Abhorrent Misinformative and Mistargeted Content on YouTube

YouTube is plagued by various types of problematic content: 1)disturbing videos targeting young children; 2) hateful and misogynistic content; and 3) pseudoscientific misinformation . By analyzing the Incel community on YouTube, we find that Incel activity is increasing over time and that platforms may play an activerole in steering users towards extreme content .…

## Educational Robotics in Online Distance Learning An Experience from Primary School

Temporary school closures caused by the Covid-19 pandemic posed new challenges for teachers and students worldwide . Online distance learning considerably limits the possibilities for students to interact with their peers and teachers . The devised activities are based on pen and paper approaches that arecomplemented by commonly used social media to facilitate communication and collaboration .…

## Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic imaging has the potential to revolutionise healthcare due to the valuable information on tissue physiology that is contained inmultispectral photoacoustic measurements . Clinical translation of the technology requires conversion of the high-dimensional acquired data intoclinically relevant and interpretable information .…

## Probing the Effect of Selection Bias on NN Generalization with a Thought Experiment

Learned networks in the domain of visual recognition and cognition impress inpart because they are trained with datasets many orders ofmagnitude smaller than the full population of possible images . Here, wetry a novel approach in the tradition of the Thought Experiment.…

## Covid 19 Detection from Chest X ray and Patient Metadata using Graph Convolutional Neural Networks

The novel corona virus (Covid-19) has introduced significant challenges dueto its rapid spreading nature through respiratory transmission . As a result,there is a huge demand for Artificial Intelligence (AI) based quick diseasediagnosis methods as an alternative to high demand tests such as PolymeraseChain Reaction (PCR) Chest X-ray (CXR) Image analysis is such cost-effectiveradiography technique due to resource availability and quick screening .…

## AnaXNet Anatomy Aware Multi label Finding Classification in Chest X ray

Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision . Most existing deeplearning models only look at the entire X-rays for classification, failing to utilize important anatomical information . In this paper, we propose a novel multi-label chest x-ray classification model that accurately classifies the image finding and also localizes the findings to their correct anatomical regions .…

## POCFormer A Lightweight Transformer Architecture for Detection of COVID 19 Using Point of Care Ultrasound

COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in atimely manner . We present an image-based solution that aimsat automating the testing process which allows for rapid mass testing to beconducted with or without a trained medical professional that can be applied torural environments and third world countries .…

## DPN SENet A self attention mechanism neural network for detection and diagnosis of COVID 19 from chest x ray images

The new type of coronavirus is also calledCOVID-19 . It began to spread at the end of 2019 and has now spread across the world . It has infected around 37 million people and claimed about 1 million lives . We propose a deep learning model that can helpradiologists and clinicians use chest X-rays to diagnose COV-19 cases and show the diagnostic features of pneumonia .…

## Superpixel based Domain Knowledge Infusion in Computer Vision

Superpixels are higher-order perceptual groups of pixels in an image, oftencarrying much more information than raw pixels . There is an inherent relationalstructure to the relationship among different superpixels of an image . Thisrelational information can convey some form of domain information about the image .…

## Semi supervised Topology Aware Segmentation of Tubular Structures from Live Imaging 3D Microscopy

Motivated by a challenging tubular network segmentation task, this papertackles two commonly encountered problems in biomedical imaging: Topologicalconsistency of the segmentation, and limited annotations . We propose atopological score which measures both topological and geometric consistency between the predicted and ground truth segmentations .…

## Enhancement Programming Skills and Transforming Knowledge of Programming through Neuroeducation Approaches

Programming digital devices and developing software is an important qualification, which contributes to employment opportunities . Despite this fact, there is a remarkable shortage in suitable human resources . In this context, research studies focus on issues of programming didactic,teaching models, programming paradigms, which are meant to enhance and optimizeprogrammers’ skills .…

## Robust partial Fourier reconstruction for diffusion weighted imaging using a recurrent convolutional neural network

A neural networkarchitecture is derived which alternates between data consistency operationsand regularization implemented by recurrent convolutions . The proposed method is trained on DW liver data of 60 volunteers and evaluated on retrospectively andprospectively sub-sampled data of different anatomies and resolutions .…

## An examination of local strain fields evolution in ductile cast iron through micromechanical simulations based on 3D imaging

Microscopic digital volume correlation (DVC) and finite elementprecoalescence strain evaluations are compared for two nodular cast ironspecimens . Image segmentation-relateduncertainties are taken into account and observed to be negligible with respect to the differences between strain levels . Macroscopic strain levels are consistently predicted.…

## Teaching Continuity in Robotics Labs in the Age of Covid and Beyond

This paper argues that training of future Roboticists and Robotics Engineers requires the extensive direct work with real robots . Part of this vision was implemented at our institution during 2020 and has been in constant use since then . The exciting insight in the conclusion is that the work that wasencouraged and triggered by a pandemic seems to have very positive longer-termbenefits of increasing access to robotics education, increasing the ability of any institution to scale their robotics education greatly, and potentially reducing costs .…

## A parameter refinement method for Ptychography based on Deep Learning concepts

X-ray Ptychography is an advanced computational microscopy technique which is delivering exceptionally detailed quantitative imaging of biological andnanotechnology specimens . But coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability . A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychographic reconstruction .…

## Can Self Reported Symptoms Predict Daily COVID 19 Cases

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths . Online surveys have been shown to be an effectivemethod for data collection amidst the pandemic . The best model predicts daily cases with a meanabsolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state .…

## Coverage Path Planning for Spraying Drones

The COVID-19 pandemic is causing a devastating effect on the health of globalpopulation . Cleaning and disinfecting public areas has become an important task . This task is not restricted to disinfection, but it is also applied topainting or precision agriculture .…

## Divided We Rule Influencer Polarization on Twitter During Political Crises in India

Influencers are key to the nature and networks of information propagation on social media . We use Google’s Universal Sentence Encoder(USE) to encode tweets of 6k influencers and 26k Indian politicians during political crises in India . We find that while on COVID-19 there is a confluence ofinfluencers on the side of the government, on three other contentious issues around citizenship, Kashmir’s statehood, and farmers’ protests, it is mainly government-aligned fan accounts that amplify the incumbent’s positions .…

## Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks

Farmers assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weedcontrol, or harvesting . The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors .…

## Geometric Model Checking of Continuous Space

Spatial Logicof Closure Spaces, SLCS, extends Modal Logic with reachability connectivesthat, in turn, can be used for expressing interesting spatial properties . SLCS constitutes the kernel of asolid logical framework for reasoning about discrete space, such as graphs and digital images, interpreted as quasi discrete closure spaces .…

## The Behavior of Internet Traffic for Internet Services during COVID 19 Pandemic Scenario

SARS-CoV-2 virus known as COVID-19 has spreadrapidly around the world, forcing many governments to impose restrictive blocking or lockdown to combat the pandemic . People’s behavior, habits, and the way they started using the Internet changed significantly . As result, the characterization and traffic of communication networks were affected in some way .…

## Improved Simultaneous Multi Slice Functional MRI Using Self supervised Deep Learning

Recent deep learning (DL) techniques have gained interest for improving MRIreconstruction . However, these methods are typically trained in a supervisedmanner that necessitates fully-sampled reference data . Self-supervised learning that does not require fully sampled data has recently been proposed and has shown similar performance to supervised learning .…

## The Modulo Radon Transform Theory Algorithms and Applications

Recently, experiments have been reported where researchers were able to perform high dynamic range (HDR) tomography in a heuristic fashion, by fusing multiple tomographic projections . This approach to HDR tomography has been inspired by HDR photography and inherits the same disadvantages .…

## Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets A Multi task Approach

We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis . Co-training especially helps in improving the performance of the classification task . We achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset .…

## Weather impact on daily cases of COVID 19 in Saudi Arabia using machine learning

COVID-19 was announced by the World Health Organisation as a global pandemic . The severity of the disease spread is determined by various factors such as the countries’ health care capacity and the enforced lockdown . However, it is not clear if a country’s climate acts as a contributing factor toward the number of infected cases .…

## Technical Report Virtual X ray imaging for higher order finite element results

A nested hierarchy of oriented bounding boxes is used for selecting candidate elements undergoing a ray-casting procedure . The exact intersection points of the ray with the finite element are not computed, instead the ray is discretized by a sequence of points .…

## Optimal Subgraph on Disturbed Network

During the pandemic of COVID-19, the demand of the transportation systems aredrastically changed both qualitatively and quantitatively and the network has become obsolete . A solution is optimal if it induces a minimal globaldelay . We model this problem as a Mixed Integer Linear Program before applying the model on a real-case application on the Lyon’s buses transportation network .…

## Modeling the geospatial evolution of COVID 19 using spatio temporal convolutional sequence to sequence neural networks

Europe was hit hard by the COVID-19 pandemic and Portugal was one of the mostaffected countries, having suffered three waves in the first twelve months . Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000inhabitants in excess of 1000 .…

## A 2 5D Vehicle Odometry Estimation for Vision Applications

This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world . It is based on a set of commonly deployed vehicularodometric sensors, with outputs available on automotive communication buses such as CAN or FlexRay .…

## Deep Polarization Imaging for 3D shape and SVBRDF Acquisition

We present a novel method for efficient acquisition of shape and spatiallyvarying reflectance of 3D objects using polarization cues . We provide our deep network with strong novel cues related to shape and reflectance in the form of a normalized Stokes map and an estimate of diffuse color .…

## Quadrature by Parity Asymptotic eXpansions QPAX for scattering by high aspect ratio particles

We study scattering by a high aspect ratio particle using boundary integralequation methods . This problem has important applications in nanophotonicsproblems, including sensing and plasmonic imaging . For this problem, we find that the boundary integral operator is nearly singulardue to the collapsing geometry from an ellipse to a line segment.…

## Model reduction in acoustic inversion by artificial neural network

In ultrasound tomography, the speed of sound inside an object is estimated based on acoustic measurements carried out by sensors surrounding the object . In this paper, a neural network -based approach is proposed, that can compensate for modeling errors caused by the approximate forward models .…

## Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID 19 Cases

Time series forecasting methods play critical role in estimating the spread of an epidemic . The coronavirus outbreak of December 2019 has already infectedmillions all over the world and continues to spread on . Hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the bestresult among the selected models with a reduced RMSE, which proved to be almost35.3% better than one of the most prevalent method of time-series prediction(ARIMA) The results demonstrated the efficacy of the hybrid implementation of the ARIMa-NARNN model over other forecasting methods such as Prophet, HoltWinters, LSTM, and the ARimA model .…

## Effects of the COVID 19 Pandemic on Learning and Teaching a Case Study from Higher Education

In December 2019, the first case of SARS-CoV-2 infection was identified inWuhan, China . Since that day, COVID-19 has spread worldwide, affecting 153million people . Education has managed to adapt to therequirements and barriers implied by the impossibility to teach students face-to-face as it was done before .…

## COVID Net CT S 3D Convolutional Neural Network Architectures for COVID 19 Severity Assessment using Chest CT Images

The health and socioeconomic difficulties caused by the COVID-19 pandemiccontinues to cause enormous tensions around the world . A critical step in the treatment andmanagement of COID-19 positive patients is severity assessment, which is challenging even for expert radiologists given the subtleties at differentstages of lung disease severity .…

## Textual Analysis of Communications in COVID 19 Infected Community on Social Media

During the COVID-19 pandemic, people started to discuss aboutpandemic-related topics on social media . On subreddit\textit{r/COVID19positive}, a number of topics are discussed or being shared . We found differences in linguisticcharacteristics across threedifferent categories of topics . We also classified posts into the different categories using SOTA pre-trained language models .…

## Consumer Demand Modeling During COVID 19 Pandemic

The current pandemic has introduced substantial uncertainty to traditionalmethods for demand planning . The contributions of this paper include a quantitativebehavior model of fear of COVID-19, impact of government interventions on consumer behavior, and impact of consumer behavior on consumer choice and hencedemand for goods .…

## Poroelastic near field inverse scattering

A multiphysics data analytic platform is established for imaging poroelasticinterfaces of finite permeability (e.g., hydraulic fractures) from elasticwaveforms and/or acoustic pore pressure measurements . The direct problem is formulated via the Biot equations in thefrequency domain where a network of discontinuities is illuminated by a set of total body forces and fluid volumetric sources .…

## Child Robot Interaction Studies During COVID 19 Pandemic

The coronavirus disease (COVID-19) pandemic affected our lives deeply, just like everyone else, justlike everyone else . The children also suffered from the restrictions due to the restrictions . The precautions due to COVirus disease also introduced new constraints in the social robotics research .…

## Ranking the information content of distance measures

Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and units of measure . When assessing the similarity between data points, one can build various distance measures using subsets of these features . Using the fewest features but still retainingsufficient information about the system is crucial in many statistical learningapproaches, particularly when data are sparse .…

## Event driven timeseries analysis and the comparison of public reactions on COVID 19

The rapid spread of COVID-19 has already affected human lives throughout the globe . Governments of different countries have taken various measures, but how they affected people lives is not clear . In this study, a rule-based and amachine-learning based models are applied to answer the above question using public tweets from Japan, USA, UK, and Australia .…

## Enhancing Safety of Students with Mobile Air Filtration during School Reopening from COVID 19

The paper discusses how robots enable occupant-safe continuous protection for students when schools reopen . Fixed air filters are not used as a key pandemic prevention method for public indoor spaces . Mobile robot is able to serve up to 14 students per cycle while reducing the worst-case pathogen dosage by 20%, and with higher robustnesscompared to a static filter .…

## Will the Winner Take All Competing Influences in Social Networks Under Information Overload

Influence competition finds its significance in many applications, such as marketing, politics and public events like COVID-19 . Existing work tends tobelieve that the stronger influence will always win and dominate nearly thewhole network, i.e., “winner takes all” However, this finding somewhatcontradicts with our common sense that many competing products are actually exist, e.g.,…

## Questioning causality on sex gender and COVID 19 and identifying bias in large scale data driven analyses the Bias Priority Recommendations and Bias Catalog for Pandemics

The BiasCatalog for Pandemics (BCP) provides definitions and emphasize realisticexamples of bias in general, and within the COVID-19 pandemic context . The objective is to anticipate and avoid disparate impact and discrimination, by consideringcausality, explainability, bias and techniques to mitigate the latter .…

## Inner ear Augmented Metal Artifact Reduction with Simulation based 3D Generative Adversarial Networks

Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in CT . A vastbody of methods have been proposed to tackle this issue, but their performance is usually insufficient when imaging tiny implants . We propose a 3D metal artifact reduction algorithm based on a generative adversarial neural network .…

## Auto Response Generation in Online Medical Chat Services

Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients . The benefits of telehealth have been even more apparent sincethe beginning of the COVID-19 crisis, as people have become less inclined tovisit doctors in person during the pandemic .…

## FedDPGAN Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID 19 Pneumonia

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia . However, the above methods have a critical challenge: dataprivacy . GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers .…

## Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

The consideration of predictive uncertainty in medical imaging with deeplearning is of utmost importance . We apply $\sigma$ scaling with a single scalarvalue; a simple, yet effective calibration method for both types ofuncertainty . The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-artconvolutional network architectures .…

## Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Classical approaches in cluster analysis are typically based on a featurespace analysis . Many applications lead to datasets with additionalspatial information and ground truth with spatially coherent classes . We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent postprocessing step or included into the clustering algorithm .…

## Transformers to Fight the COVID 19 Infodemic

The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19 . False informationdetection has thus become a surging research topic in recent months . NLP4IF-2021 shared task on fighting the COVI-19 infodemic has been organised to strengthen the research in false information detection where theparticipants are asked to predict seven different binary labels regarding false information in a tweet .…