Page Content
Topics:
Platform
- Fundamental Aspects of Crowdsourcing Platforms
- High Quality Crowd-Workflow Design
- Combination of Human Computation and AI
- Open Data, and Open Science (open-science.berlin)
Workers
- Motivation of Workers
- Gamification in Volunteer Crowdsourcing
- Personalized/adaptive Design to Increase Performance
- Quality Control Mechanisms (Data Reliability, Agreement)
Application of Crowdsourcing
- Crowd- and AI-based Hybrid Workflows (error correction and performance boost by adding crowd-services to support AI)
- Crowd- and AI-based Translation Workflows
- Crowd- and AI-based Text Summarization Workflows
- Crowd- and AI-based Knowledge Graph and Chatbot Supporting Workflows
- Crowd- and AI-based Chatbot/ Dialog Flow Supervision and in-time Correction
- Crowd- and AI-based Information Learning (autonomous knowledge base updates)
- Internal Crowdsourcing (employee sourcing)
- Gaming QoE Assessment using Crowdsourcing
- Speech Quality Assessment using Crowdsourcing approach (ITU-T Standardization)
- Text Simplification and Text Complexity: einfaches-wiki.de
Our research extends to the following areas:
Building on Crowdsourcing
- Mobile crowdsourcing (in the field)
- Crowd assessments: Usability, UX, QoE
- Privacy and confidentiality in crowdsourcing
- Mobile street application, urban mobility, city guarding
- Data collection in the field: crowd as (continuous) sensors
- Data management (clean, index, verify, tag, label, translate, etc. )
Improving Crowdsourcing
- Real-time interaction, human computation as a service, (HuaaS)
- Privacy and security in crowdsourcing
- Motivation in crowdsourcing, gamification
- Quality control (pattern recognition, cheater detection, anomaly)
- Automatic user segmentation (clustering)
- Training, E-learning and building expert-crowds
- Task complexity modeling
- Crowd and user biases, subjective normalization
- Scalable Crowdsourcing, Robustness, Reliability in Engineering
- Quality in Crowdsourcing (quality of opinion, audio/video, reliability)
Start-Up:
- Crowdee: High Quality Large Scale Crowdsourcing for Studies and AI-related Data Acquisition: (Start-Up from QU TU Berlin)
Projects:
- BRIDGE - Data & Fact Driven Decision-Making for Skills Based Inclusion of Migrants (EIT-Digital)
- DoNotFear - Perceived Security In Public Transport (EIT-Digital)
- SMESS - Towards a Standardized Methodology for Evaluating the Quality of Speech Services using Crowdsourcing
- OurPuppet - Pflegeunterstützung mit einer interaktiven Puppe für informell Pflegende (BMBF)
- ICU - Internes Crowdsourcing in Unternehmen: Arbeitnehmergerechte Prozessinnovationen durch digitale Beteiligung von Mitarbeiter/innen (BMBF)
- BOP - Berlin Open Science Platform for the Curation of Research Data (TU Berlin)
- DEKA - Design und Entwicklung einer kollaborativen digitalen Arbeitsplattform für die Digitalisierung von Innovationsprozessen (BMBF)
Past Projects:
- CrowdMAQA (Motivation and Quality Control in Crowdsourcing)
- AUNUMAP (Automated User Segmentation from Speech and Text for Market Research Applications)
- Vocalytics & SWYM (Fully Automated User Characterization and Personality Estmation)
- Speaker Recognition and Speaker Characterization through different Communication Channels
- Affect-based Indexing
- Anomaly Detection and Early Warning Systems
- Predicting the Perceived Quality of Audiovisual Speech (Perc Qual AVS)
Zusatzinformationen / Extras
Quick Access:
Schnellnavigation zur Seite über Nummerneingabe