Co-creation methodology with Smart technologies in Health and well-being to enable communication between isolated and disperse small communities: a literature review

Objective: The objective is to determine reported cases of co-creation methodology about the use of smart technologies in public spaces in order to create new forms of social interactions and practices, which in turn creates new socio-spatial relations and promotes interactions and communication between isolated and disperse communities. Methods: The literature published in the last 5 years (2016-2020) has been reviewed. Searches on Co-creation methodology and ICTs in Health and Biomedicine, on topics such as interaction among users, ICT and social behaviour, spatial analyses, planning methodologies and public involvement, on-line gaming, self‐learning, and the prevention of risky habbits are made manually. Results: Search strategies developed through electronic databases and manual search identified a total of 180 references, included in the supplementary material. They have been divided by the technologies used in the studies, co-creation methodology, and according to the type of socio-medical application. This research highlights the penetration of ICT in social and healthcare environments and clearly demonstrates the high number of publications that have come out over recent years and a lack of publications that evaluate co-creation methodology in this field. Conclusions: Most of the papers included only partially cover the subject matter of ICT in Health and Biomedicine and how to use smart technologies to transform public spaces in small communities into people-friendly human environments. The research carried out for this paper clearly demonstrates the high number of publications concerning technology assessment. However, there is a distinct lack of publications that evaluate co-creation methodology.

Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning

This work presents a machine-learning approach to predict peak-stress clusters in heterogeneous polycrystalline materials. Prior work on using machine learning in the context of mechanics has largely focused on predicting the effective response and overall structure of stress fields. However, their ability to predict peak – which are of critical importance to failure – is unexplored, because the peak-stress clusters occupy a small spatial volume relative to the entire domain, and hence require computationally expensive training. This work develops a deep-learning-based convolutional encoder–decoder method that focuses on predicting peak-stress clusters, specifically on the size and other characteristics of the clusters in the framework of heterogeneous linear elasticity. This method is based on convolutional filters that model local spatial relations between microstructures and stress fields using spatially weighted averaging operations. The model is first trained against linear elastic calculations of stress under applied macroscopic strain in synthetically generated microstructures, which serves as the ground truth. The trained model is then applied to predict the stress field given a (synthetically generated) microstructure and then to detect peak-stress clusters within the predicted stress field. The accuracy of the peak-stress predictions is analyzed using the cosine similarity metric and by comparing the geometric characteristics of the peak-stress clusters against the ground-truth calculations. It is observed that the model is able to learn and predict the geometric details of the peak-stress clusters and, in particular, performed better for higher (normalized) values of the peak stress as compared to lower values of the peak stress. These comparisons showed that the proposed method is well-suited to predict the characteristics of peak-stress clusters.

A fuzzy approach for determining the cognitive spatial location of an object in geographical information system

The work is devoted to the problem of interpretation of fuzzy semantics of cognitive descriptions of spatial relations in natural language and their visualization in a geographic information system (GIS). The solution to the problem of determining the fuzzy spatial location of an object based on vague descriptions of the observer in natural language is considered. The task is relevant in critical situations when there is no way to report the exact coordinates of the observed object, except by describing its location relative to the observer itself. Such a situation may be the result of a crime, terrorist act or natural disaster. An observer who finds itself at the scene transmits a text message, which is a description of the location of the object or place (for example, the crime scene, the location of dangerous objects, the crash site). The semantics of the spatial location of the object can be further extracted from the text message. The proposed fuzzy approach is based on the formalization of the observer's phrases, with which it can describe spatial relations, in the form of a set of linguistic variables that determine the direction and distance to the object. Examples of membership functions for linguistic variables are given. The spatial knowledge base is built on the basis of the phrases of observers and their corresponding fuzzy regions. Algorithms for constructing cognitive regions in GIS have been developed. Methods of their superposition to obtain the final fuzzy location of the object are proposed. An example of the implementation of a fuzzy model for identifying cognitive regions based on vague descriptions of several observers, performed using developed Python scripts integrated into ArcGIS 10.5, is considered.

Remembering Pokémania: Memory, Cognitive Assemblages, and Augmented Reality

Introduced in July 2016, Pokémon GO is widely considered the killer app for contemporary augmented reality. Popular attention to the game has waned in recent years, but Pokémon GO remains enormously successful in terms of both player base and revenue generation. Whether individuals experienced the game for a short time or remain dedicated hardcore players, Pokémon GO exists as memories of time and place, imbuing familiar sites and routes with new meaning and temporal connection. Attending to these complex interrelationships of place, space, mobility, humans, technologies, infrastructures, environments, and memory, we situate Pokémon GO as what Hayles (2016) calls a cognitive assemblage—sociotechnical systems of interconnectivity in which cognition is an exteriorized process occurring across multiple levels, sites, and boundaries. In turn, we conceptualize cognition (and specifically memory) not as confined within a delimited hominid body, but instead operating through contextual relations, at multiple sites, and in a constant state of becoming. By reflecting on our own experiences as part of the distributed memory of Pokémon GO, we situate memory as momentary convergence of signals made possible by infrastructures, inscribed on servers and silicon, and made part of algorithmic suggestion and learning AI. Additionally, our own memories and experiences serve to highlight the experiential complexity of cognitive assemblages in relation to structures of feeling, as well as new temporal and spatial relations.

The geoattributed entity extraction technology for visual representation of objects spatial relations based on automated schematic map generation

This paper considers the problem of extracting geoattributed entities from natural language texts to visualize the spatial relations of geographical objects. For visualization we use the technology of automated generation of schematic maps as subject-oriented components of geographic information systems. The paper describes the information technology that allows extracting geoattributed entities from natural language texts by combining several approaches. These are the neural network approach, the rule-based approach and the approach based on the use of lexico-syntactic patterns for the analysis of natural language texts. For data visualization we propose to use automated geocoding tools in conjunction with the capabilities of modern geographic information systems. The result of this technology is a cartogram that displays the spatial relations of the objects mentioned in the text.

A New Method for Arabic Text Detection in Natural Scene Images

Text in scene images can provide useful and vital information for content-based image analysis. Therefore, text detection and script identification in images are an important task. In this paper, we propose a new method for text detection in natural scene images, particularly for Arabic text, based on a bottom-up approach where four principal steps can be highlighted. The detection of extremely stable and homogeneous regions of interest (ROIs) is based on the Color Stability and Homogeneity Regions (CSHR) proposed technique. These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting.

Prepositions and Spatial Relations in Natural Languages According to Leibniz

In his 1677 Dialogue, Leibniz answers the question of how it is possible that speakers of different languages agree on the same truths by postulating “a certain correspondence between characters and things”. In the mid-1680s, he arguably attempts to specify this “correspondence” by explaining how linguistic particles are connected to our perception of spatial relations among things in the world. Firstly, this paper focuses on the role that, according to Leibniz, signs and characters play in our knowledge. Secondly, it introduces the solution that can be found in the Dialogue to the problem of how the same truth can be expressed in different languages. After briefly expounding Leibniz’s theory of natural languages, the paper gives an account of Leibniz’s analysis of the nature of prepositions and of how they contribute, in a natural language, to determine the correspondence between characters and things that is mentioned in the Dialogue.

Virtual Reality-Based Fuzzy Spatial Relation Knowledge Extraction Method for Observer-Centered Vague Location Descriptions

Many documents contain vague location descriptions of observed objects. To represent location information in geographic information systems (GISs), these vague location descriptions need to be transformed into representable fuzzy spatial regions, and knowledge about the location descriptions of observer-to-object spatial relations must serve as the basis for this transformation process. However, a location description from the observer perspective is not a specific fuzzy function, but comes from a subjective viewpoint, which will be different for different individuals, making the corresponding knowledge difficult to represent or obtain. To extract spatial knowledge from such subjective descriptions, this research proposes a virtual reality (VR)-based fuzzy spatial relation knowledge extraction method for observer-centered vague location descriptions (VR-FSRKE). In VR-FSRKE, a VR scene is constructed, and users can interactively determine the fuzzy region corresponding to a location description under the simulated VR observer perspective. Then, a spatial region clustering mechanism is established to summarize the fuzzy regions identified by various individuals into fuzzy spatial relation knowledge. Experiments show that, on the basis of interactive scenes provided through VR, VR-FSRKE can efficiently extract spatial relation knowledge from many individuals and is not restricted by requirements of a certain place or time; furthermore, the knowledge obtained by VR-FSRKE is close to the knowledge obtained from a real scene.