The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this paper, we propose an approach to address these challenges availed with machine learning techniques. The proposed system combines, fuses, and merges various types of data from different sources, encodes them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier, as well as other supervised machine learning models for comparisons. Our experimental evaluation on multiple real-world data sets comparing the performance of several classifiers (including Feedforward Neural Networks, Support Vector Machines, Bag of Decision Trees, k-Nearest Neighbors and Naïve Bayes), indicated the superiority of Random Forests in terms of the examined performance metrics (Accuracy, Specificity, Precision, Recall, F-measure and G-mean).

The convention for the safeguarding of Intangible Cultural Heritage (ICH) by UNESCO highlights the equal importance of intangible elements of cultural heritage to tangible ones. One of the most important domains of ICH is folkloric dances. A dance choreography is a time-varying 3D process (4D modelling), which includes dynamic co-interactions among different actors, emotional and style attributes, and supplementary elements, such as music tempo and costumes. Presently, research focuses on the use of depth acquisition sensors, to handle kinesiology issues. The extraction of skeleton data, in real time, contains a significant amount of information (data and metadata), allowing for various choreography-based analytics. In this paper, a trajectory interpretation method for Greek folkloric dances is presented. We focus on matching trajectories’ patterns, existing in a choreographic database, to new ones originating from different sensor types such as VICON and Kinect II. Then, a Dynamic Time Warping (DTW) algorithm is proposed to find out similarities/dissimilarities among the choreographic trajectories. The goal is to evaluate the performance of the low-cost Kinect II sensor for dance choreography compared to the accurate but of high-cost VICON-based choreographies. Experimental results on real-life dances are carried out to show the effectiveness of the proposed DTW methodology and the ability of Kinect II to localize dances in 3D space.

The need for effective cognitive training methodologies has increased, particularly for individuals dealing with Attention Deficit Hyperactivity Disorder (ADHD). In response to this demand, Virtual Reality (VR) technology emerges as a promising tool to support cognitive functions. Addressing this imperative, our paper introduces ADHD Dog, a VR game designed to aid individuals with ADHD by harnessing the advancements in VR technology and cognitive science. Our approach integrates behavioral and sociocultural theories, alongside gamification, to foster player engagement and reinforce cognitive functions. The theories employed, including operant conditioning and social constructivism, are specifically chosen for their relevance to ADHD’s cognitive aspects and their potential to promote active and context-based engagement. ADHD Dog, grounded in the principles of neuroplasticity and behaviorist methods, distinguishes itself by utilizing technology to amplify cognitive functions, like impulse control, attention, and short-term memory. An evaluation by individuals with ADHD, psychologists and computer scientists yielded promising results, underscoring the significant contribution of blending narrative-driven gameplay with behavioral and sociocultural theories, along with gamification, to ADHD cognitive training.

The persistent increase in the magnitude of urban data, combined with the broad range of sensors from which it derives in modern urban environments, poses issues including data integration, visualization, and optimal utilization. The successful selection of suitable locations for predetermined commercial activities and public utility services or the reuse of existing infrastructure arise as urban planning challenges to be addressed with the aid of the aforementioned data. In our previous work, we have integrated a multitude of publicly available real-world urban data in a visual semantic decision support environment, encompassing map-based data visualization with a visual query interface, while employing and comparing several classifiers for the selection of appropriate locations for establishing parking facilities. In the current work, we challenge the best representative of the previous approach, i.e., random forests, with convolutional neural networks (CNNs) in combination with a graph-based representation of the urban input data, relying on the same dataset to ensure comparability of the results. This approach has been inspired by the inherent visual nature of urban data and the increased capability of CNNs to classify image-based data. The experimental results reveal an improvement in several performance indices, implying a promising potential for this specific combination in decision support for urban planning problems.

The magnitude and multiformity of recorded and readily available urban data from independent sensors and mobile user devices have paved new possibilities for city environment planning and evolution. In previous works, a visual semantic decision support system for urban planning was presented, and the capability of machine learning approaches, ranging from random forests to graph-based convolutional neural networks, to infer preferable directions for future development was explored, extrapolating upon previous opted locations and selected alternatives for publicly commercial services. In this work, the anterior decision-making process leading to establishment choices is addressed with the same sets of criteria and samples within the same environment. A Declarative Modeling shell is proposed, and Multiple Criteria Decision Analysis processes are adopted to encompass the DM’s/DMs’ rationale, solely relying on methodologies close to human intuition. To this end, outranking representatives from the PROMETHEE family as well as weighted sum approaches are employed, fueled by the interpretation of the declarative description of decision parameters on behalf of the DM(s), exploring the ability to achieve classifications in a straight synthetic manner, i.e., in the absence of previous knowledge, thus exhibiting the potential of decision analysis methodologies, enhanced by Declarative Modeling, to be used as powerful intuitive tools in similar paradigm contexts.

The revelation of the internal structure of objects through computed tomography (CT scan) contributes to a more comprehensive understanding of their creation, the assessment of their preservation status, and the prediction of their future behavior. Consequently, in the case of Yiannis Pappas’ collection, this knowledge aids in the perpetuation of the models it hosts, which are made from malleable materials, such as wax, plasticine, and mazut, on metallic armature. This publication presents the complete methodology for extracting three-dimensional (3D) models (reconstructions) of the individual construction materials of the figurines, with the aim of subsequently utilizing them in research, as well as in their digital preservation and restoration. The 3D reconstructions were obtained by automatic segmentation algorithms based on the absorption measurements of the materials of the specific figurines, and were furthered edited (post-processing) to obtain the final models.