geoTangle : Interactive Design of Geodesic Tangle Patterns on Surfaces

Tangles are complex patterns, which are often used to decorate the surface of real-world artisanal objects. They consist of arrangements of simple shapes organized into nested hierarchies, obtained by recursively splitting regions to add progressively finer details. In this article, we show that 3D digital shapes can be decorated with tangles by working interactively in the intrinsic metric of the surface. Our tangles are generated by the recursive application of only four operators, which are derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface without relying on parametrization. We reach interactive speed on meshes of a few million triangles by relying on an efficient approximate graph-based geodesic solver.

Tracking Cloud Forests With Cloud Technology and Random Forests

Hotspots of endemic biodiversity, tropical cloud forests teem with ecosystem services such as drinking water, food, building materials, and carbon sequestration. Unfortunately, already threatened by climate change, the cloud forests in our study area are being further endangered during the Covid pandemic. These forests in northern Ecuador are being razed by city dwellers building country homes to escape the Covid virus, as well as by illegal miners desperate for money. Between August 2019 and July 2021, our study area of 52 square kilometers lost 1.17% of its tree cover. We base this estimate on simulations from the predictive model we built using Artificial Intelligence, satellite images, and cloud technology. When simulating tree cover, this model achieved an accuracy between 96 and 100 percent. To train the model, we developed a visual and interactive application to rapidly annotate satellite image pixels with land use and land cover classes. We codified our algorithms in an R package—loRax—that researchers, environmental organizations, and governmental agencies can readily deploy to monitor forest loss all over the world.

A Skin Cancer Detection Interactive Application Based on CNN and NLP

Skin cancer is the most common cancer with several different types. According to current estimations, one in five Americans will develop skin cancer in their lifetime. Therefore, early diagnosis and treatment of it is of crucial significance. Several advanced image processing methods have been applied to predict skin cancer. However, few researchers utilize those methods to build an interactive application. In this work, we implemented an interactive skin cancer diagnosis website, combining the convolutional neural network (CNN) and natural language processing (NLP) technology. The neural network model uses four convolutional layers and dense layers respectively to improve the accuracy. Two max-pooling layers were used to reduce redundant information. To address the severe overfitting problem, we chose to utilize the batch normalization along with dropout layers. Based on our results, 0.9935 in accuracy and 0.0225 loss is realized for training data, and accuracy of 0.8393 and 0.6648 loss for testing data. Natural language processing (NLP) was used to implement a chatbot for interaction with users. We crawled skin cancer related questions and answers from Quora and used them to train our chatbot. Lastly, we combined CNN and NLP to build an interactive skin cancer diagnosis website. VUE.js and Django were used to build the front-end and back-end of our website. These results offer a guideline for combining artificial intelligence with not only medicine but also interactive network, which enables people to get medical care more easily.

lcsm: An R package and tutorial on latent change score modelling

Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling.

Experience of using the interactive application "AcademiХ3D" in the context of the COVID-19 pandemic in the educational process of students of the medical faculty.

An analysis of the experience of using the interactive application "Academics 3D" in the context of the COVID-19 pandemic in teaching students of the medical faculty is presented. Virtual technologies make it possible to optimally distribute the time limit and stages of mastering practical skills in the professional training of doctors. The analysis showed the significance, prospects and ways of increasing the efficiency of using simulation technologies in the process of training doctors.

Translation of Brain Activity Patterns of a user into Commands using Electroencephalography (EEG)

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). The BCI system uses oscillatory Electroencephalography (EEG) signals, recorded using specific mental activity, as input and provides a control option by its output. A brain-computer interface uses electrophysiological signals to control the remote devices. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. The computer processes the EEG signals and uses it in order to accomplish tasks such as communication and environmental control.