Continuous affect state annotation using a joystick-based user interface

Ongoing research at the DLR (German Aerospace Center) aims to employ affective computing techniques to ascertain the emotional states of users in motion simulators. In this work, a novel user feedback interface employing a joystick to acquire subjective evaluation of the affective experience is presented. This interface allows the subjects to continuously annotate their affect states, elicited in this scenario by watching video clips. Several physiological parameters (e.g. heart rate, electrodermal activity, respiration rate, etc.) were acquired during the viewing session. A statistical analysis is presented, which shows expected patterns in data that validate the design and methodology of the experiment and lay the groundwork for further experiments to be undertaken at the DLR

Motivation through gamification: A Self-Determination Theory perspective for the design of an adaptive reward system

Research on the nature and origins of human motivation has addressed the role of rewards in learning and behaviour. Gamification finds its raison d'être in being able to leverage motivational theories, to foster motivation in users through the use of game elements. One of the main criticisms moved to the use of gamification for learning purposes is related to the one-size-fits-all approach that tends to characterize many gamified applications. In this paper we explore the possibilities that can arise from the convergence of Self-Determination Theory principles and machine learning, to improve the efficacy of gamification reward systems

Rule Mining for Local Boundary Detection in Melodies

The task of melodic segmentation is a long-standing MIR task that has not been solved, yet. In this paper, we shortly review existing approaches, most of which are either based on rule-sets derived from Gestalt principles, or on a statis- tical learning approach. We use a method related to both approaches. A rule mining algorithm is employed to find a rule set that classifies notes within their local context as phrase boundary. The advantage of a rule-based model is its interpretability. By inspecting the rules, some important clues are revealed about what constitutes a melodic phrase boundary, notably a prevalence of rhythmic features over pitch features. Both the discovered rule set and a Random Forest Classifier trained on the same data set outper- form previous methods on the task of melodic segmenta- tion of melodies from the Essen Folk Song Collection, the Meertens Tune Collections, and the set of Bach Chorales

Multi-Temporal Convolutions for Human Action Recognition in Videos

Effective extraction of temporal patterns is crucial for the recognition of temporally varying actions in video. We argue that the fixed-sized spatio-temporal convolution kernels used in convolutional neural networks (CNNs) can be improved to extract informative motions that are executed at different time scales. To address this challenge, we present a novel convolution block that is capable of extracting spatio-temporal patterns at multiple temporal resolutions. Our proposed multi-temporal convolution (MTConv) blocks utilize two branches that focus on brief and prolonged spatio-temporal patterns, respectively. The extracted time-varying features are aligned in a third branch, with respect to global motion patterns through recurrent cells. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture. This introduces a substantial reduction in computational costs. Extensive experiments on Kinetics, Moments in Time and HACS action recognition benchmark datasets demonstrate competitive performance of MTConvs compared to the state-of-the-art with a significantly lower computational footprint 11Our code is available at: https://git.io/JfuPi

ICT: Health’s best friend and worst enemy?

I propose a paradigm shift for health care, as there is an urgent need for i) continuous (semi-)automatic medical checkups, ii) cost reduction, and iii) cure for the 21st century black plague (i.e., stress-related diseases) are very much needed. To realize this ICT’s Paradox has to be solved. On the one hand, ICT can cause i) musculoskeletal problems, ii) vision problems, iii) headache, iv) obesity, v) stress disorders (e.g., burn out), vi) metabolic issues, vii) addiction (e.g., to games, social media, and Internet), viii) sleeping problems, ix) social isolation, and x) an unrealistic world view. On the other hand, ICT claims to provide these problems’ solutions. Consequently, health informatics needs to adopt a holistic approach, improve its fragile theoretical frameworks, and handle the incredible variance we all show. As a remedy, I propose to take up the challenge to next-generation models of personality, as they are a crucial determinant in people’s stress coping style

The Cover Song Variation Dataset

As digital music collections grow larger, music similarity becomes one of the most prominent concepts in the field of Music Information Retrieval (MIR). Modelling similarity between music pieces allows efficient retrieval and organizing of such collections. Studies have shown that the concept of variation is closely related to similarity, since listeners tend to cluster together musical patterns that are repeated, transformed but still recognizable. Subsequently, musical pieces or segments that contain such patterns are considered similar. Such structural variations are notably present in oral-transmission processes. Folk songs are a standing example of such a process, capturing a huge amount of varying patterns moulded through time. Variations in cover songs in western popular music are also very interesting examples, since a) they can be considered products of a “modern” oral-transmission procedure and b) covers themselves are typically well documented with rich metadata