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 .…

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 .…

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 .…

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 .…

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 .…

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 .…

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 .…

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 .…

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 .…

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 .…

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 .…