Survey Musik und Medien 2012 : Die Nutzung neuer digitaler Technologien und Angebote des alltäglichen Musikhörens durch Jugendliche

Es werden ausgewählte Ergebnisse einer deutschlandweiten Repräsentativbefragung zur Nutzung von Audiotechnologien des alltäglichen Musikhörens in 2012 vorgestellt. Anhand einer Latent Class Analysis werden für das jugendliche Alterssegment (14-21 Jahre) zwei zentrale Nutzertypen identifiziert, mit Hilfe einer logistischen Regression soziale Ungleichheiten (Geschlecht, Haushaltseinkommen) in Bezug auf deren Angehörige demonstriert und die Resultate im Hinblick auf Fragen der Mediensozialisation beleuchtet.This article illustrates selected results of a Germany-wide representativesurvey concerning the use of audio technologies for everyday music consumption in 2012. By means of Latent Class Analysis two essentialuser types are identified within the age group of adolescents (14-21 years) and social inequalities (gender, household income) between the members of the two classes are uncovered via logistic regression. In conclusion, the results are discussed with respect to media socialization issues.DFG, 223657291, Survey Musik und Medien. Empirische Basisdaten und theoretische Modellierung der Mediatisierung alltäglicher Musikrezeption in Deutschlan

PREDICTIVE BUSINESS PROCESS MONITORINGWITH CONTEXT INFORMATION FROM DOCUMENTS

Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process-context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately

A next click recommender system for web-based service analytics with context-aware LSTMs

Software companies that offer web-based services instead of local installations can record the user’s interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs. We present a technique for a next click recommender system. Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks. We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context. The technique was evaluated with a real-life data set from a financial software provider. We used a hidden Markov model (HMM) as the baseline. The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation

Hindustani raga and singer classification using 2D and 3D pose estimation from video recordings

Using pose estimation with video recordings, we apply an action recognition machine learning algorithm to demonstrate the use of the movement information to classify singers and the ragas (melodic modes) they perform. Movement information is derived from a specially recorded video dataset of solo Hindustani (North Indian) raga recordings by three professional singers each performing the same nine ragas, a smaller duo dataset (one singer with tabla accompaniment) as well as recordings of concert performances by the same singers. Data is extracted using pose estimation algorithms, both 2D (OpenPose) and 3D. A two-pathway convolutional neural network structure is proposed for skeleton action recognition to train a model to classify 12-second clips by singer and raga. The model is capable of distinguishing the three singers on the basis of movement information alone. For each singer, it is capable of distinguishing between the nine ragas with a mean accuracy of 38.2% (with the most successful model). The model trained on solo recordings also proved effective at classifying duo and concert recordings. These findings are consistent with the view that while the gesturing of Indian singers is idiosyncratic, it remains tightly linked to patterns of melodic movement: indeed we show that in some cases different ragas are distinguishable on the basis of movement information alone. A series of technical challenges are identified and addressed, with code shared alongside audiovisual data to accompany the paper

Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction

Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provide predictions about running process instances. Recent methods increasingly focus on deep neural networks (DNN) due to their superior prediction performances and their independence from process knowledge. However, DNNs generally have the disadvantage of showing black-box characteristics, which hampers the dissemination in critical environments such as healthcare. To this end, we propose the design of HIXPred, a novel artifact combining predictive power with explainable results for patient pathway predictions. We instantiate HIXPred and apply it to a real-life healthcare use case for evaluation and demonstration purposes and conduct interviews with medical experts. Our results confirm high predictive performance while ensuring sufficient interpretability and explainability to provide comprehensible decision support