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The extractive logic of Big Data-driven technology and knowledge production has raisedserious concerns. While most criticism initially focused on impacts on Western societies,attention is now increasingly turning to consequences for countries and communities inthe Global South. To date, most criticism has focused on private sector activities. In thispaper, however, we argue that publicly funded knowledge and technology productionprojects must also be viewed through the lens of a neocolonial critique. To do so, weanalyze the dynamics of collaboration in an EU-funded research project that iscollecting data for the development of a social platform with a focus on diversity. Theproject includes pilot sites in China, Denmark, the UK, India, Italy, Mexico, Mongolia,and Paraguay. We present the experiences on four of the pilot sites and reflect on theoriginal conception of the project, the challenges, ambiguities, and missed opportunitieswe encountered, as well as our progress and results to date. We then analyze thedifferent experiences in comparison. As a result of our analysis, we identify key actionsneeded to counter prevailing logics of data colonialism and eurocentrism, which webelieve are rooted in both historically grown structures of inequality and currentconstraints ingrained in recent EU-funding policies.

Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for profit maximization. This paper presents a design solution that centers the well-being of users. It presents the theory and practice of designing a diversity-aware platform for social relations. In this approach, the diversity of users is leveraged in a way that allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity. The end users of the envisioned platform are students, who participate in the design process. Diversity-aware platform design involves numerous steps, of which two are highlighted in this paper: 1) defining a framework and operationalizing the "diversity" of students, 2) collecting "diversity" data to build diversity-aware algorithms. The paper further reflects on the ethical challenges encountered during the design of a diversity-aware platform., AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2021

The interactive role of the ecological, architectural, biophilic, and sensory qualities of outdoor and indoor spaces in the restorative experiences of urban inhabitants is little known. We analyzed the restorative influence on mood states and situational stress related to exposure to vegetation proportion, spatial extension, landmark salience, biophilic architecture, people density, street visual access, olfactory pleasantness, and noise of 65 public spaces in a Mexican city. The environmental qualities of these places were analyzed with multidimensional scaling (MDS), leading to eight space categories (e.g., historic squares with biophilic architecture, large parks, street scenes, and interiors with non-biophilic architecture). Ratings of the restorative potential, mood states, situational stress, olfactory pleasantness, and noise annoyance were evaluated on such places and modeled through a structural equation modeling (SEM). The model shows that the restorative influence of the environmental qualities on moods and stress was related to a decrease in experiences of negative moods and perceived stress, and an increase of positive mood states. Based on our findings, we discuss design guidelines, emphasizing the relevance of including vegetation and built elements with biophilic qualities to create restorative environments.

We propose a generalized 3D shape descriptor for the efficient classification of 3D archaeological artifacts. Our descriptor is based on a multi-view approach of curvature features, consisting of the following steps: pose normalization of 3D models, local curvature descriptor calculation, construction of 3D shape descriptor using the multi-view approach and curvature maps, and dimensionality reduction by random projections. We generate two descriptors from two different paradigms: 1) handcrafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier and 2) machine learnt, in which the descriptor automatically learns the object features through a pretrained deep neural network model (VGG-16) for transfer learning and passed on to the classifier. These descriptors are applied to two different archaeological datasets: 1) non-public Mexican dataset, represented by a collection of 963 3D archaeological objects from the Templo Mayor Museum in México City that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and 2) 3D pottery content-based retrieval benchmark dataset, consisting of 411 objects. Once the multi-view descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: $K$ -nearest neighbor, support vector machine, and structured support vector machine. Our object descriptors classification results are compared against five popular 3D descriptors in the literature, namely, rotation invariant spherical harmonic, histogram of spherical orientations, signature of histograms of orientations, symmetry descriptor, and reflective symmetry descriptor. Experimentally, we were able to verify that our machine learnt and handcrafted descriptors offer the best classification accuracy (20% better on average than comparative descriptors), independently of the classification methods. Our proposed descriptors are able to capture sufficient information to discern among different classes, concluding that it adequately characterizes the datasets.

Smartphone applications that use passive sensing to support human health and well-being primarily rely on: (a) generating low-dimensional representations from high-dimensional data streams; (b) making inferences regarding user behavior; and (c) using those inferences to benefit application users. Meanwhile, sometimes these datasets are shared with third parties as well. Human-centered ubiquitous systems need to ensure that sensitive attributes of users are protected when applications provide utility to people based on such behavioral inferences. In this paper, we demonstrate that inferences of sensitive attributes of users (gender, body mass index category) are possible using low-dimensional and sparse data coming from mobile food diaries (a combination of sensor data and self-reports). After exposing this potential risk, we demonstrate how deep learning techniques can be used for feature transformation to preserve sensitive user information while achieving high accuracies for application-related inferences (e.g. inferring the type of consumed food). Our work is based on two datasets of daily eating behavior of 160 young adults from Switzerland (NCH=122) and Mexico (NMX=38). Results show that using the proposed approach, accuracies in the order of 75%-90% can be achieved for application related inferences, while reducing the sensitive inference to almost random performance.

The sensorimotor rhythm (SMR) is an electroencephalographic rhythm associated with motor and cognitive development observed in the central brain regions during wakefulness in the absence of movement, and it reacts contralaterally to generalized and hemibody movements. The purpose of this work was to characterize the SMR of 4-month-old infants, born either healthy at term or prematurely with periventricular leukomalacia (PVL). Two groups of infants were formed: healthy and premature with PVL. Their electroencephalograms (EEGs) were recorded in four conditions: rest, free movement, right-hand grasping and left-hand grasping, in order to explore general reactivity to free movement and contralateral reactivity in hand-grasping conditions. Associations between SMR, and cognitive and motor performance were analyzed. The healthy infants showed a SMR between 5.47 and 7.03 Hz, with clear contralateral reactivity to free movement and right-hand grasping. However, the premature infants with PVL did not show enough electroencephalographic characteristics to evidence the presence of SMR. Poor performance, characteristic of children with PVL, was related to low-frequency SMR, while good performance was associated with a higher frequency rhythm in the left hemisphere. The presence of SMR in the group of healthy infants could be considered a sign of health at this age. Thus, poor SMR evidence in the EEG of infants with PVL is probably a sign of brain immaturity or brain dysfunction. Our results provide data on infant SMR development that is needed to design neurofeedback protocols for infants with PVL.

Resumen. De manera natural las personas vamos formando rutinas para adaptarnos al entorno social en el cual nos desempenamos. Estas rutinas se ven reflejadas en las secuencias de ubicaciones que visitamos para desplazarnos hacia los lugares donde realizamos nuestras actividades diarias, sin embargo, ?como podemos determinar la presencia de ellas? El presente trabajo consiste en un analisis exploratorio de un conjunto de datos de movilidad de usuarios utilizando modelos de topicos con el fin de determinar si existe una estructura subyacente en la movilidad de las personas de acuerdo a las rutinas que estas manifiestan en sus vidas. Se presenta la introduccion al problema explicando el modelo a utilizar aśi como su desarrollo teorico, validacion experimental de su funcionamiento a traves de conjuntos de datos sinteticos y su aplicacion a un conjunto de datos real con presentacion de los resultados obtenidos y conclusiones.