Children with visual impairments often struggle to fully participate in group activities due to limited access to visual cues. They have difficulty perceiving what is happening, when, and how to act-leading to children with and without visual impairments being frustrated with the group activity, reducing mutual interactions. To address this, we created Touchibo, a tactile storyteller robot acting in a multisensory setting, encouraging touch-based interactions. Touchibo provides an inclusive space for group interaction as touch is a highly accessible modality in a mixed-visual ability context. In a study involving 107 children (37 with visual impairments), we compared Touchibo to an audio-only storyteller. Results indicate that Touchibo significantly improved children's individual and group participation perception, sparking touch-based interactions and the storyteller was more likable and helpful. Our study highlights touch-based robots' potential to enrich children's social interactions by prompting interpersonal touch, particularly in mixed-visual ability settings.

The Internet is increasingly considered an essential and integral part of people's lives. With its massification and the constant increase of the bandwidth offered to the consumers, services like Over-The-Top (OTT) have grown in recent years - e.g. YouTube and Netflix. OTT services refer to the delivery of audio, video and other data over the Internet without network operators' control. This trend leads to Quality-of-Experience (QoE) challenges in the presence of large delays or quality breaks in the visualization of a video stream. To overcome these issues, new approaches are needed to improve the content delivery reception to the users, and consequently, the services' QoE. This paper proposes a novel prefetching mechanism aiming to reduce the content delivery response, bringing the content as close as possible to consumers before being explicitly requested. This approach is based on the distribution of OTT contents in wireless networks, that can be integrated in network equipment: it is able to predict the type and quality of content that consumers connected to - or nearby - an access point may request, and cache it before being requested, thereby improving their quality perception of the service. Given the lack of information in the literature on management and control of proxy caches for embedded systems, the first step is to analyze and compare different proxy cache solutions - Nginx and Squid - and then evaluate two developed prefetching algorithms in mobile scenarios, given the characteristics of wireless networks. The results show that the proposed content-aware prefetching mechanism is capable of boosting the network's performance, from a technical perspective - higher hit-ratios, reduced data transfers and request latency - and from a user perspective, with significant improvements in the Mean Opinion Score (MOS) QoE metric.

The migration of popular Catch-up TV services to modern Over-The-Top (OTT) multimedia delivery infrastructures creates a wide set of scalability challenges which are commonly addressed using Content Delivery Networks (CDNs) relying on caching nodes close to users. The use of general-purpose caching nodes, tailored for generic web content, is far from optimal as it does not consider the particularities of Catch-up TV content, namely its dynamic popularity behavior, superstar effects, and relevance decay, as shown in existing scientific literature. Since caches are limited in size and are relatively small when compared to the whole catalog of available Catch-up TV content, which may contain tens of thousands of TV programs, it is crucial to make the most out of the available resources. To address these issues, this paper proposes a novel content-aware cache replacement algorithm, Most Popularly Used (MPU), capable of taking advantage of content demand forecasts built using machine learning models, to significantly outperform traditional cache replacement policies, such as Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO), and approach the optimal theoretical hit-ratio limits. MPU leverages millions of Catch-up TV request logs to validate its results under realistic conditions.

As the interest on Network Virtualization continues to grow, so does the awareness of the many technical obstacles to transpose before the envisioned virtualized network environment may become a reality. A significant obstacle lies on the efficient assignment of virtual resources into physical ones. Performing the so-called mapping of a virtual network into a substrate network is a computationally intensive task, due to the dual optimization required for nodes and links placement. The purpose of this paper is to tackle this problem taking into consideration real-life scenarios of network operators, where the limitations imposed by the heterogeneity of the virtual and substrate networks must be accounted for. To that end, this paper proposes a heuristic algorithm for virtual resources mapping in the physical infrastructure that supports the heterogeneity of networks, in both links and nodes. The mapping heuristic was evaluated both through simulation and in a real experimental virtualization platform. Through the simulation results, it is shown that the mapping approach is able to embed a high percentage of the Virtual Network (VNet) requests respecting all links and node constraints. With respect to the experimental results, the proposed algorithm was shown to be fast, requiring a mapping time in the order of low tens of milliseconds, and linearly scalable with the increase in the number of existing VNets.

In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.

Stress and syllabification are essential attributes for several components in text-to speech (TTS) systems. They are responsible for improving grapheme-to-phoneme conversion rules and for enhancing the synthetic intelligibility, since stress and syllable are key units in prosody prediction. This paper presents three linguistically rule-based automatic algorithms for Catalan text-to-speech conversion: a word stress marker, an orthographic syllabification algorithm and a phonological syllabification algorithm. The systems were implemented and tested. The results gave rise to the following word accuracy rates: 100% for the stress marker algorithm, 99.7% for the orthographic syllabification algorithm and 99.8% for the phonological syllabification algorithm.