Bots in Wikipedia: Unfolding their duties

The success of crowdsourcing systems such as Wikipedia relies on people participating in these systems. However, in this research we reveal to what extent human and machine intelligence is combined to carry out semi-automatic workflows of complex tasks. In Wikipedia, bots are used to realize such combination of human-machine intelligence. We provide an extensive overview on various edit types bots carry out in this regard through the analysis of 1,639 approved task requests. We classify existing tasks by an action-object-pair structure and reveal existing differences in their probability of occurrence depending on the investigated work context. In the context of community services, bots mainly create reports, whereas in the area of guidelines or policies bots are mostly responsible for adding templates to pages. Moreover, the analysis of existing bot tasks revealed insights that suggest general reasons, why Wikipedia’s editor community uses bots as well as approaches, how they organize machine tasks to provide a sustainable service. We conclude by discussing how these insights can prepare the foundation for further research

Stochastic volatility models for ordinal valued time series with application to finance

In this paper we introduce two stochastic volatility models where the response variable takes on only finite many ordered values. Corresponding time series occur in high-frequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an e±cient Grouped Move Multigrid Monte Carlo (GM-MGMC) sampler. We apply both models to price changes of the IBM stock in January, 2001 at the NYSE. Dependencies of the price change process on covariates are quantified and compared with theoretical considerations on such processes. We also investigate whether this data set requires modeling with a heavy-tailed Student-t distribution

The real estate bubble in spain has been Pumped Up by All of Us

The objective of this briefing, based on a more extensive investigation is to show that the Spanish media has played an important role in prolonging the speculation in the real estate sector. For the first time it is proven that the guilt for the crisis in the real estate sector is also up to the media. And not just political influence was implied in the disinformation. Since the two leading general papers have been analyzed (EL MUNDO y EL PAIS), representing the main political ideologies in Spain, it is shown that noone can be blamed we all are responsible for this crisis and we all have to find ways to get out of it.Real estate crisis, Speculation, Moral, Corruption, Media

The impact of the introduction of the Euro on firms' expectations concerning export behavior, product innovation and foreign competition - An empirical assessment of the German business-related services sector

This paper analyses the degree to which firms expect to be able to enter new markets, to develop new products and to contend with new foreign competition after the introduction of the Euro. Panel data taken from a quarterly business survey in the service sector are used for the empirical analysis. East German firms do not differ from their West German competitors in their expectations that the Euro will increase export activity and product innovation, but significantly more East German firms expect new foreign competitors to enter the home market. The impact of the Euro on firms' expectations of entering new markets and developing new products is stronger for firms which already export than for those who do not. Further, the degree of preparation for the Euro plays a significant role in firms' market expectations. --European Monetary Union,Euro,business-related services,binary probit model,export,foreign competition

Modeling migraine severity with autoregressive ordered probit models

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient dependencies among the measurements have to be accounted for. For this the autore- gressive ordinal probit (AOP) model of Müller and Czado (2004) is utilized and fitted by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covariates are selected using proportional odds models ignoring this dependency. Model fit and model comparison are discussed. The analysis shows that humidity, windchill, sunshine length and pressure differences have an effect in addition to a high dependence on previous measurements. A comparison with proportional odds specifications shows that the AOP models are preferred

The Impact of Concept Representation in Interactive Concept Validation (ICV)

Large scale ideation has developed as a promising new way of obtaining large numbers of highly diverse ideas for a given challenge. However, due to the scale of these challenges, algorithmic support based on a computational understanding of the ideas is a crucial component in these systems. One promising solution is the use of knowledge graphs to provide meaning. A significant obstacle lies in word-sense disambiguation, which cannot be solved by automatic approaches. In previous work, we introduce \textit{Interactive Concept Validation} (ICV) as an approach that enables ideators to disambiguate terms used in their ideas. To test the impact of different ways of representing concepts (should we show images of concepts, or only explanatory texts), we conducted experiments comparing three representations. The results show that while the impact on ideation metrics was marginal, time/click effort was lowest in the images only condition, while data quality was highest in the both condition

Regression Models for Ordinal Valued Time Series with Application to High Frequency Financial Data

In financial time series transaction price changes often occur in discrete increments, for example in eights of a dollar. We consider these price changes as discrete random variables which are assumed to be generated by a latent process which incorporates both exogenous variables and autoregressive components. A standard Gibbs sampling algorithm has been developed to estimate the parameters of the model. However this algorithm exhibits bad convergence properties. To improve the standard Gibbs sampler we utilize methods proposed by Liu and Sabatti (2000, Biometrika 87), based on transformation groups on the sample space. A simulation study will be given to demonstrate the substantial improvement by this new algorithm. Finally we apply our model to the data of the IBM stock on Nov 13, 2000, and estimate the influence of the duration between transactions, the volume, and the bid-offer-spread both to model fit and prediction