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YAWL (Yet Another Workflow Language) is an open source Business Process Management System, first released in 2003. YAWL grew out of a university research environment to become a unique system that has been deployed worldwide as a laboratory environment for research in Business Process Management and as a productive system in other scientific domains.
Who do you trust: Peers or Technology? A conjoint analysis about computational reputation mechanisms
(2020)
Peer-to-peer sharing platforms are taking over an increasingly important role in the platform economy due to their sustainable business model. By sharing private goods and services, the challenge arises to build trust between peers online mostly without any kind of physical presence. Peer rating has been proven as an important mechanism. In this paper, we explore the concept called Trust Score, a computational rating mechanism adopted from car telematics, which can play a similar role in carsharing. For this purpose, we conducted a conjoint analysis where 77 car owners chose between fictitious user profiles. Our results show that in our experiment the telemetric-based score slightly outperforms the peer rating in the decision process, while the participants perceived the peer rating more helpful in retrospect. Further, we discuss potential benefits with regard to existing shortcomings of user rating, but also various concerns that should be considered in concepts like telemetric-based reputation mechanism that supplements existing trust factors such as user ratings.
This paper aspires to develop a deeper understanding of the sharing/collaborative/platform economy, and in particular of the technical mechanisms upon which the digital platforms supporting it are built. In surveying the research literature, the paper identifies a gap between studies from economical, social or socio-technical angles, and presentations of detailed technical solutions. Most cases study larger, ‘monotechnological’ platforms, rather than local platforms that lend components from several technologies. Almost no literature takes a design perspective. Rooted in Sharing & Caring, an EU COST Action (network), the paper presents work to systematically map out functionalities across domains of the sharing economy. The 145 technical mechanisms we collected illustrate how most platforms are depending on a limited number of functionalities that lack in terms of holding communities together. The paper points to the necessity of a better terminology and concludes by discussing challenges and opportunities for the design of future and more inclusive platforms.
Efficient and comprehensive assessment of students knowledge is an imperative task in any learning process. Short answer grading is one of the most successful methods in assessing the knowledge of students. Many supervised learning and deep learning approaches have been used to automate the task of short answer grading in the past. We investigate why assistive grading with active learning would be the next logical step in this task as there is no absolute ground truth answer for any question and the task is very subjective in nature. We present a fast and easy method to harness the power of active learning and natural language processing in assisting the task of grading short answer questions. A webbased GUI is designed and implemented to incorporate an interactive short answer grading system. The experiments show that active learning saves the time and effort of graders in assessment and reaches the performance of supervised learning with less amount of graded answers for training.
The development of metals tailored to the metallurgical conditions of laser-based additive manufacturing is crucial to advance the maturity of these materials for their use in structural applications. While efforts in this regard are being carried out around the globe, the use of high strength eutectic alloys have, so far, received minor attention, although previous works showed that rapid solidification techniques can result in ultrafine microstructures with excellent mechanical performance, albeit for small sample sizes. In the present work, a eutectic Ti-32.5Fe alloy has been produced by laser powder bed fusion aiming at exploiting rapid solidification and the capability to produce bulk ultrafine microstructures provided by this processing technique.
Process energy densities between 160 J/mm³ and 180 J/mm³ resulted in a dense and crack-free material with an oxygen content of ~ 0.45 wt.% in which a hierarchical microstructure is formed by µm-sized η-Ti4Fe2Ox dendrites embedded in an ultrafine eutectic β-Ti/TiFe matrix. The microstructure was studied three-dimensionally using near-field synchrotron ptychographic X-ray computed tomography with an actual spatial resolution down to 39 nm to analyse the morphology of the eutectic and dendritic structures as well as to quantify their mass density, size and distribution. Inter-lamellar spacings down to ~ 30–50 nm were achieved, revealing the potential of laser-based additive manufacturing to generate microstructures smaller than those obtained by classical rapid solidification techniques for bulk materials. The alloy was deformed at 600 °C under compressive loading up to a strain of ~ 30% without damage formation, resulting in a compressive yield stress of ~ 800 MPa.
This study provides a first demonstration of the feasibility to produce eutectic Ti-Fe alloys with ultrafine microstructures by laser powder bed fusion that are suitable for structural applications at elevated temperature.
Development of colored surfaces by formation of nano-structured aggregates is a widely used strategy in nature to color lightweight structures (e.g. butterflies) without the use of dye pigments. The deposition of nanoscale particles mimics nature in it’s approach coloring surfaces. This work presents sol-gel modification of cellulose surfaces used to form a template for growth of Cu/Cu2O core-shell particles with defined size-distributions. Besides improving the adhesion of the deposited particulate material, the sol-gel matrix serves as a template for the control of particle sizes of the Cu/Cu2O structures, and as a consequence of particle size variation the surface color is tunable. As an example, red color was achieved with an average particle size of 35 nm, and shifts gradually to blue appearance when particles have grown to 80 nm on the sol-gel modified fabric. The copper concentration on representative fabrics is kept low to avoid modifying the textile characteristics and were all in the range of 150–170 mg per g of cellulose material. As a result of copper deposition on the surface of the material, the cellulose fabric also became electrically conductive. Remarkably, the electrical conductivity was found to be dependent on the average particle sizes of the deposits and thus related to the change in observed color. The generation of color by growth of nano-sized particles on sol-gel templates provides a highly promising approach to stain surfaces by physical effects without use of synthetic colorants, which opens a new strategy to improve environmental profile of coloration.
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
This dissertation presents a probabilistic state estimation framework for integrating data-driven machine learning models and a deformable facial shape model in order to estimate continuous-valued intensities of 22 different facial muscle movements, known as Action Units (AU), defined in the Facial Action Coding System (FACS). A practical approach is proposed and validated for integrating class-wise probability scores from machine learning models within a Gaussian state estimation framework. Furthermore, driven mass-spring-damper models are applied for modelling the dynamics of facial muscle movements. Both facial shape and appearance information are used for estimating AU intensities, making it a hybrid approach. Several features are designed and explored to help the probabilistic framework to deal with multiple challenges involved in automatic AU detection. The proposed AU intensity estimation method and its features are evaluated quantitatively and qualitatively using three different datasets containing either spontaneous or acted facial expressions with AU annotations. The proposed method produced temporally smoother estimates that facilitate a fine-grained analysis of facial expressions. It also performed reasonably well, even though it simultaneously estimates intensities of 22 AUs, some of which are subtle in expression or resemble each other closely. The estimated AU intensities tended to the lower range of values, and were often accompanied by a small delay in onset. This shows that the proposed method is conservative. In order to further improve performance, state-of-the-art machine learning approaches for AU detection could be integrated within the proposed probabilistic AU intensity estimation framework.
Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
Towards a conceptual framework for sustainable business models in the food and beverage industry
(2020)
Toshiyuki Fukao
(2020)