Abstract
Internet-of-Things (IoT) systems have spread among different application domains, from home automation to industrial manufacturing processes. The rushed development by competing vendors to meet the market demand of IoT solutions, the lack of interoperability standards, and the overall lack of a defined set of best practices have resulted in a highly complex, heterogeneous, and frangible ecosystem. Several works have been pushing towards visual programming solutions to abstract the underlying complexity and help humans reason about it. As these solutions begin to meet widespread adoption, their building blocks usually do not consider reliability issues. Node-RED, being one of the most popular tools, also lacks such mechanisms, either built-in or via extensions. In this work we present SHEN (Self-Healing Extensions for Node-RED) which provides 17 nodes that collectively enable the implementation of self-healing strategies within this visual framework. We proceed to demonstrate the feasibility and effectiveness of the approach using real devices and fault injection techniques.

Abstract
New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

Abstract
This paper presents a literature review of the importance of virtual technology for rehabilitation for people with ASD (Autism Spectrum Disorder). ASD is diagnosed as a neurological disability characterized by a range of physical and mental disorders and whose first symptoms appear during early childhood. People with autism deal with issues with social communication, behavior, and attention skills. As a sensitive disturbance, adapted technology allows to re-learn skills stimulating procedures about how to proceed, communicate or behave without difficulties in unexpected environments. The use of technology in educational contexts, home or at school, helps prevent and teach younger people with ASD. Considering different technologies as more appropriate methods, Virtual Reality (VR) applications and personalized environments provide better simulation and comfortable environments. As the main advantage of VR, complete immersion and interactive experience promotes constant learning for people with autism. This systematic review details the benefits of VR studies and compares the benefits of different interactive technologies according to the deficits of several individuals. The use of technology versus the traditional path on therapies helps obtain better and faster results over time. Finally, it explains how VR can be recognized as a tool to help develop cognitive, verbal and nonverbal skills and recognizes technology as a good ally to face fears or reactions by ASD people.

Abstract

Abstract
Narrative Extraction from text is a complex task that starts by identifying a set of narrative elements (actors, events, times), and the semantic links between them (temporal, referential, semantic roles). The outcome is a structure or set of structures which can then be represented graphically, thus opening room for further and alternative exploration of the plot. Such visualization can also be useful during the on-going annotation process. Manual annotation of narratives can be a complex effort and the possibility offered by the Brat annotation tool of annotating directly on the text does not seem sufficiently helpful. In this paper, we propose Brat2Viz, a tool and a pipeline that displays visualization of narrative information annotated in Brat. Brat2Viz reads the annotation file of Brat, produces an intermediate representation in the declarative language DRS (Discourse Representation Structure), and from this obtains the visualization. Currently, we make available two visualization schemes: MSC (Message Sequence Chart) and Knowledge Graphs. The modularity of the pipeline enables the future extension to new annotation sources, different annotation schemes, and alternative visualizations or representations. We illustrate the pipeline using examples from an European Portuguese news corpus. Copyright © by the paper's authors.

Abstract
Entity-oriented search tasks heavily rely on exploiting unstructured and structured collections. Moreover, it is frequent for text corpora and knowledge bases to provide complementary views on a common topic. While, traditionally, the retrieval unit was the document, modern search engines have evolved to also retrieve entities and to provide direct answers to the information needs of the users. Cross-referencing information from heterogeneous sources has become fundamental, however a mismatch still exists between text-based and knowledge-based retrieval approaches. The former does not account for complex relations, while the latter does not properly support keyword-based queries and ranked retrieval. Graphs are a good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine text-based approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models for entity-oriented search, providing an overview on link analysis and exploring graph-based text representation and retrieval, leveraging knowledge graphs for document or entity retrieval, building entity graphs from text, using graph matching for querying with subgraphs, exploiting hypergraph-based representations, and ranking based on random walks on graphs. We close with a discussion on the topic and a view of the future to motivate the research of graph-based models for entity-oriented search, particularly as joint representation models for the generalization of retrieval tasks. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.