Amidst rapid and constant technological change, keeping IT higher education curricula up to date is becoming increasingly challenging. For a decade, the course”Project on Information Technologies” within the undergraduate computer science studies offered by a major technical university has been pursuing an effort of continuous curriculum adaptation and active learning practices based on an open project-based learning (PBL) methodology. This article researches the implications of employing an open-ended PBL approach, with a specific focus on the alignment of acquired skills with the trends in the professional landscape. Our analysis has identified a strong correlation between the technologies utilized in the projects and the contemporary technological trends in the areas of global technological focus, programming languages, server-side technologies, database management systems, and DevOps-related tools. For the study, we analyzed empirical data gathered from over 100 projects involving more than 400 students who enrolled in this reference course, belonging to the last year of the IT higher education programme in the last 10 years. The results suggest the open project-based design as a teaching means in the student’s study plan for fostering the student the learning of prevailing current practical technologies.

Abstract This article describes Pictonaut, a novel method to automatically synthetise animated shots from motion picture footage. Its results are editable (backgrounds, characters, lighting, etc.) with conventional 3D software, and they have the finish of professional 2D animation. Rather than addressing the challenge solely as an image translation problem, a hybrid approach combining multi-person 3D human pose estimation and GANs is taken. Sub-sampled video frames are processed with OpenPose and SMPLify-X to obtain the 3D parameters of the pose (body, hands and face expression) of all depicted characters. The captured parameters are retargeted into manually selected 3D models, cel shaded to mimic the style of a 2D cartoon. The results of sub-sampled frames are interpolated to generate a complete and smooth motion for all the characters. The background is cartoonized with a GAN. Qualitative evaluation shows that the approach is feasible, and a small dataset of synthetised shots obtained from real movie scenes is provided.

New teaching methodologies which foster student involvement, such as project-based learning, are nowadays part of the study curriculum of many engineering schools. Project-based learning courses, however, often build upon other previously taught technical courses, where the technical content for the project to be developed is studied. That type of course design focuses on building the transversal capabilities of students, and the technical challenges of the project are the mean to acquire these non-technical skills. In this paper, we present and assess a project-based course on computer network applications of a computer science school, which has been designed to improve within the same course both the transversal and technical skills of the students. The proposition of interest is that the course not only aims to train the students’ transversal skills by a group work project, but also to practise new technical topics and technologies. We argue that the key element of the proposed course design is that each student project group defines with the instructor the project they would like to develop in the course. We present first the design of the course and then an assessment with questionnaires, which were conducted over two semesters with the students enrolled in the course. The obtained results indicate that the students achieved both technical and transversal skills, while the instructors need to be flexible to adapt to diverse technical topics of the proposed projects.

Deployment of a distributed deep learning technology stack on a large parallel system is a very complex process, involving the integration and configuration of several layers of both, general-purpose and custom software. The details of such kind of deployments are rarely described in the literature. This paper presents the experiences observed during the deployment of a technology stack to enable deep learning workloads on MareNostrum, a petascale supercomputer. The components of a layered architecture, based on the usage of Apache Spark, are described and the performance and scalability of the resulting system is evaluated. This is followed by a discussion about the impact of different configurations including parallelism, storage and networking alternatives, and other aspects related to the execution of deep learning workloads on a traditional HPC setup. The derived conclusions should be useful to guide similarly complex deployments in the future.

Spark has become one of the main options for large-scale analytics running on top of shared-nothing clusters. This work aims to make a deep dive into the parallelism configuration and shed light on the behavior of parallel spark jobs. It is motivated by the fact that running a Spark application on all the available processors does not necessarily imply lower running time, while may entail waste of resources. We first propose analytical models for expressing the running time as a function of the number of machines employed. We then take another step, namely to present novel algorithms for configuring dynamic partitioning with a view to minimizing resource consumption without sacrificing running time beyond a user-defined limit. The problem we target is NP-hard. To tackle it, we propose a greedy approach after introducing the notions of dependency graphs and of the benefit from modifying the degree of partitioning at a stage; complementarily, we investigate a randomized approach. Our polynomial solutions are capable of judiciously use the resources that are potentially at user's disposal and strike interesting trade-offs between running time and resource consumption. Their efficiency is thoroughly investigated through experiments based on real execution data.

In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy.

In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalabilityof the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.