Ricerc@Sapienza

The Zone of Proximal Development (ZPD) theorized by Lev Vygotskij can be considered as one of the most interesting insights on the value of collaborative learning: it specifies the cognitive distance reachable by a student in learning activities, given that some support by teachers, and peers, is available. Group handling is certainly one of the main aspects to be reproduced in an online learning system. This paper aims to offer a twofold contribution.

Questions with answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them, which can be mitigated by doing peer-assessment. In OpenAnswer we modeled peer-assessment as a Bayesian network connecting the sub-networks representing any participating student to the corresponding answers of her graded peers.

A short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of utmost importance for the planning of facilities, optimization of day-to-day operations, and an effective management of the available resources.

In this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand.

In this section, we compare the prediction performance achieved by the recurrent neural network architectures presented in the previous sections on both the synthetic tasks and the real-world datasets. For each architecture, we report the optimal configuration of its hyperparameters for the task at hand, and the best learning strategy adopted for training the model weights. We perform several independent evaluation of the prediction results due to the stochastic initialization of the internal model weights.

In this chapter, we consider three different real-world datasets, which contain real-valued time series of measurements of electricity and telephonic activity load. For each dataset, we set up a short-term load forecast problem of 24 hours ahead prediction. Two of the datasets under analysis include time series of measurements of exogenous variables, which are used to provide additional context to the network and thus to improve the accuracy of the prediction.

In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly considered in the literature to evaluate the performance of a predictive model.

In this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure. First, we present the most basic version of recurrent neural networks, called Elman recurrent neural network. Then, we introduce two popular gated architectures, which are long short-term memory and the gated recurrent units.

In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks and explain the general properties that are common to several existing architectures. We introduce the basis of their training procedure, the backpropagation through time, as a general way to propagate and distribute the prediction error to previous states of the network. The learning procedure consists of updating the model parameters by minimizing a suitable loss function, which includes the error achieved on the target task and, usually, also one or more regularization terms.