Evaluating graph resilience with tensor stack networks: a Keras implementation

Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia

Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.

Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors

Freezing of gait (FOG) is a paroxysmal dyskinesia, which is common in patients with advanced Parkinson’s disease (PD). It is an important cause of falls in PD patients and is associated with serious disability. In this study, we implemented a novel FOG detection system using deep learning technology. The system takes multi-channel acceleration signals as input, uses one-dimensional deep convolutional neural network to automatically learn feature representations, and uses recurrent neural network to model the temporal dependencies between feature activations. In order to improve the detection performance, we introduced squeeze-and-excitation blocks and attention mechanism into the system, and used data augmentation to eliminate the impact of imbalanced datasets on model training. Experimental results show that, compared with the previous best results, the sensitivity and specificity obtained in 10-fold cross-validation evaluation were increased by 0.017 and 0.045, respectively, and the equal error rate obtained in leave-one-subject-out cross-validation evaluation was decreased by 1.9%. The time for detection of a 256 data segment is only 0.52 ms. These results indicate that the proposed system has high operating efficiency and excellent detection performance, and is expected to be applied to FOG detection to improve the automation of Parkinson’s disease diagnosis and treatment.