The use of technological resources in education has lead to positive changes in the elaboration of new methodologies, in this context technologies such as the Digital Interactive Whiteboard (DIW) can act by facilitating Learning. The mere presence of the DIW does not guarantee benefits for the student's learning process, that raises doubts about whether or not the resources available are used in a satisfactory manner. In this research it was possible to verify that there are few tools available for the DIW context, and many of them have problems of usability and content quality. Thus, a form of facilitate the content elaboration for the DIW is the use of Authoring Tools (ATs). In order to verify whether or not the use of ATs promotes better use of the DIW, an AT (entitled AtauDIW) was developed to assist the use of DIWs.
Lazaridou., et al 2017 proposed a framework for language learning that relies on multi-agent communication. The agents in the framework were setup in a referential game where they communicated about many images. In this paper, we propose an experiment where agents develop a private language for referring to specified sentences given a set of sentences. The challenge is for the agents to learn a method of distinguishing differences between sentences and to develop a shared language to be able to refer to particular sentences by those distinguishing features. We will evaluate the agents' ability to accurately identify and differentiate the sentences. In addition, we will identify patterns in the methods that the agents develop to refer to the different types of sentences.Keywords: Reinforcement learning, multi-agent coordination
This research paper aims at exploiting efficient ways of implementing the N-Body problem. The N-Body problem, in the field of physics, predicts the movements and planets and their gravitational interactions. In this paper, the efficient execution of heavy computational work through usage of different cores in CPU and GPU is looked into; achieved by integrating the OpenMP parallelization API and the Nvidia CUDA into the code. The paper also aims at performance analysis of various algorithms used to solve the same problem. This research not only aids as an alternative to complex simulations but also for bigger data that requires work distribution and computationally expensive procedures.
Gene regulatory networks have an important role to study the behaviour of genes. By analysing
these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by
changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid
modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene
regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according
to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its
qualitative dynamical behaviour. René
Thomas discrete modelling of gene regulatory network (GRN) is a
well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible
targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using
another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given
Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions
also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to
pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these
time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN.
We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the
model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs.
As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This
process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra
features i.e. CTL editor, parameters filtering and input/output files management.
GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification,
K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter
filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours
and days of manual computation