This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

For efficient network management, it is important to monitor and analyse the data, particularly big data applications based on time series, in terms of trends and correlations, to predict network problems and be able to react preventively. Machine learning techniques can help but, given the amount and complexity of the algorithms and metrics available, the use of these techniques is laborious and requires specialized knowledge. This paper proposes a framework for distributed real-time time series forecasting with the goal to make predictions for various dynamic systems simultaneously and provide straightforward horizontal scaling, increased modularity, high robustness and a simple interface for users. Moreover, the proposed framework also enables the creation of ensemble algorithms, combining the results of multiple predictors, without changes on each individual predictor component. To demonstrate the functionalities of our framework, we show how simultaneous predictions can be made about the number of Internet sessions, using a real data stream from users of the buses in the Porto city.

Consumer demand for high-quality video over wireless networks is increasing at fast pace. The resulting technical challenges are particularly stringent in crowded spaces, where the density of users far exceeds the ability to deploy cellular base stations or WiFi infrastructure in a cost effective way. To address this problem, we present a reliable and scalable live streaming solution based on wireless multicast with real-time network coding. At the core of our approach is a timely delivery scheme that uses a minimum amount of feedback from the receivers to generate coded repair packets that are simultaneously useful to a large number of users. Our protocol, which we implemented and tested in a real-world wireless testbed, differs from traditional wireless unicast and multicast schemes in that (a) the feedback messages of the users are treated jointly and (b) the repair mechanism considers both the playout deadlines of individual packets and the list of packets already received by the clients. In comparison with a standard video approach that sends an MPEG-2 encoded stream over 802.11 unicast links, our solution offers real-time guarantees for all users commensurate with the link quality and an 11x improvement in terms of bandwidth usage. A commercial version of the proposed solution shows a strong increase in the number of clients that can access video streams simultaneously over a single WiFi hotspot.

EDGeS is an European funded Framework Program 7 project that aims to connect desktop and service grids together. While in a desktop grid, personal computers pull jobs when they are idle, in service grids there is a scheduler that pushes jobs to available resources. The work in EDGeS goes well beyond conceptual solutions to bridge these grids together: it reaches as far as actual implementation, standardization, deployment, application porting and training. One of the work packages of this project concerns monitoring the overall EDGeS infrastructure. Currently, this infrastructure includes two types of desktop grids, BOINC and XtremWeb, the EGEE service grid, and a couple of bridges to connect them. In this paper, we describe the monitoring effort in EDGeS: our technical approaches, the goals we achieved, and the plans for future work.