Assessing the participatory design of a project-based course on computer network applications

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.Peer ReviewedPostprint (published version

Multimedia big data computing for in-depth event analysis

While the most part of ”big data” systems target text-based analytics, multimedia data, which makes up about 2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and challenges. Multimedia Big Data Computing is the new topic that focus on all aspects of distributed computing systems that enable massive scale image and video analytics. During the course of this paper we describe BPEM (Big Picture Event Monitor), a Multimedia Big Data Computing framework that operates over streams of digital photos generated by online communities, and enables monitoring the relationship between real world events and social media user reaction in real-time. As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.Peer ReviewedPostprint (author's final draft

Lester: rotoscope animation through video object segmentation and tracking

This article introduces Lester, a novel method to automatically synthesize retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment Anything Model (SAM) and the resulting masks are tracked through subsequent frames with DeAOT, a method of hierarchical propagation for semi-supervised video object segmentation. The geometry of the masks’ contours is simplified with the Douglas–Peucker algorithm. Finally, facial traits, pixelation and a basic rim light effect can be optionally added. The results show that the method exhibits an excellent temporal consistency and can correctly process videos with different poses and appearances, dynamic shots, partial shots and diverse backgrounds. The proposed method provides a more simple and deterministic approach than diffusion models based video-to-video translation pipelines, which suffer from temporal consistency problems and do not cope well with pixelated and schematic outputs. The method is also more feasible than techniques based on 3D human pose estimation, which require custom handcrafted 3D models and are very limited with respect to the type of scenes they can process.This research is partially funded by the Spanish Ministry of Science and Innovation under contract PID2019-107255GB, and by the SGR programme 2021-SGR-00478 of the Catalan Government.Peer ReviewedPostprint (published version

Pictonaut: movie cartoonization using 3D human pose estimation and GANs

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.This work is partially supported by the Spanish Ministry of Science and Innovation under contract PID2019-107255GB, and by the SGR programme 2017-SGR-1414 of the Catalan Government.Peer ReviewedPostprint (published version

Towards the cloudification of the social networks analytics

In the last years, with the increase of the available data from social networks and the rise of big data technologies, social data has emerged as one of the most profitable market for companies to increase their benefits. Besides, social computation scientists see such data as a vast ocean of information to study modern human societies. Nowadays, enterprises and researchers are developing their own mining tools in house, or they are outsourcing their social media mining needs to specialised companies with its consequent economical cost. In this paper, we present the first cloud computing service to facilitate the deployment of social media analytics applications to allow data practitioners to use social mining tools as a service. The main advantage of this service is the possibility to run different queries at the same time and combine their results in real time. Additionally, we also introduce twearch, a prototype to develop twitter mining algorithms as services in the cloud.Peer ReviewedPostprint (author’s final draft

Modelo en red de los contenidos mediáticos en la era de los dispositivos inteligentes

The changes in the information technologies of the last two decades, represented by the democratization of the Internet and the availability of portable and intelligent handhelds, is associated with new business, communication and communicative models. The high service capacities of today's smartphones have enabled a two-way dynamic processing of great amounts of content. The users gather in virtual communities and become both consumers and producers of information: prosumers. These contents come from or are allocated in the cloud or other platforms, and spread rapidly through the net. In this paper, the evolution of information fluxes is analyzed in three phases of the Information Society or Net Society, and the theory around user-generated content and media convergence is discussed. Each phase is represented in a cyclical model articulating data dealing, the new roles of users and the impact of technology. On the basis of these models, news discourses and content may be anticipated for various purposes. A flow model based on algorithms determined by the personal preferences of users is finally proposed.Los cambios en las tecnologías de la información en las últimas décadas, representados por la democratización de Internet y la disponibilidad de las computadoras portátiles y dispositivos inteligentes están asociados con nuevos negocios y modelos comunicativos. La capacidad de los actuales teléfonos inteligentes facilita el procesamiento bidireccional y dinámico de grandes cantidades de contenidos. Los usuarios se agrupan en comunidades virtuales y se convierten en consumidores y productores: prosumidores. Estos contenidos se encuentran en la nube, en los medios de comunicación (plataformas), y se propagan rápidamente a través de la red. En este artículo se analiza la evolución de estos flujos de mensajes en el marco de las teorías que rodean el contenido generado por el usuario y la convergencia de medios. Se analiza también la naturaleza cíclica de la secuencia de datos, la evolución de los usuarios y la dependencia tecnológica para desarrollar y distribuir nuevos productos discursivos Se propone finalmente un modelo de flujos basado en algoritmos determinados por las preferencias personales de los usuarios.Peer ReviewedPostprint (author's final draft

Artificial neural networks as emerging tools for earthquake detection

As seismic networks continue to spread and monitoring sensors become more ef¿cient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identi¿cation, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recentadvancesarerelatedtotheapplicationofArti¿cial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.Peer ReviewedPostprint (published version

Dynamic configuration of partitioning in spark applications

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.Peer ReviewedPostprint (author's final draft

Distributed training of deep neural networks with spark: The MareNostrum experience

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.Peer ReviewedPostprint (author's final draft

Automated curation of brand-related social media images with deep learning

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. 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. When appropriate, we also apply object detection, usually to discover images containing logos. 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. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.Peer ReviewedPostprint (author's final draft