COEL: A Web-based Chemistry Simulation Framework

The chemical reaction network (CRN) is a widely used formalism to describe macroscopic behavior of chemical systems. Available tools for CRN modelling and simulation require local access, installation, and often involve local file storage, which is susceptible to loss, lacks searchable structure, and does not support concurrency. Furthermore, simulations are often single-threaded, and user interfaces are non-trivial to use. Therefore there are significant hurdles to conducting efficient and collaborative chemical research. In this paper, we introduce a new enterprise chemistry simulation framework, COEL, which addresses these issues. COEL is the first web-based framework of its kind. A visually pleasing and intuitive user interface, simulations that run on a large computational grid, reliable database storage, and transactional services make COEL ideal for collaborative research and education. COEL's most prominent features include ODE-based simulations of chemical reaction networks and multicompartment reaction networks, with rich options for user interactions with those networks. COEL provides DNA-strand displacement transformations and visualization (and is to our knowledge the first CRN framework to do so), GA optimization of rate constants, expression validation, an application-wide plotting engine, and SBML/Octave/Matlab export. We also present an overview of the underlying software and technologies employed and describe the main architectural decisions driving our development. COEL is available at http://coel-sim.org for selected research teams only. We plan to provide a part of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Assessment of Generic Skills through an Organizational Learning Process Model

This contribution has been published in this repository with the permission of the publisher. This contribution was presented in WEBIST 2018 (http://www.webist.org/?y=2018) and has been published by SCITEPRESS in http://www.scitepress.org/PublicationsDetail.aspx?ID=y9Yt0eHt02o=&.The performance in generic skills is increasingly important for organizations to succeed in the current competitive environment. However, assessing the level of performance in generic skills of the members of an organization is a challenging task, subject to both subjectivity and scalability issues. Organizations usually lay their organizational learning processes on a Knowledge Management System (KMS). This work presents a process model to support managers of KMSs in the assessment of their individuals’ generic skills. The process model was deployed through an extended version of a learning management system. It was connected with different information system tools specifically developed to enrich its features. A case study with Computer Science final-year students working in a software system was conducted following an authentic learning approach, showing promising results.Visaigle Project (grant TIN2017-85797-R)

Login Authentication with Facial Gesture Recognition

Facial recognition has proven to be very useful and versatile, from Facebook photo tagging and Snapchat filters to modeling fluid dynamics and designing for augmented reality. However, facial recognition has only been used for user login services in conjunction with expensive and restrictive hardware technologies, such as in smart phone devices like the iPhone x. This project aims to apply machine learning techniques to reliably distinguish user accounts with only common cameras to make facial recognition logins more accessible to website and software developers. To show the feasibility of this idea, we created a web API that recognizes a users face to log them in to their account, and we will create a simple website to test the reliability of our system. In this paper, we discuss our database-centric architecture model, use cases and activity diagrams, technologies we used for the website, API, and machine learning algorithms. We also provide the screenshots of our system, the user manual, and our future plan