@article{LeopoldvanderAaOffenbergetal.2019,
  author    = {Leopold, Henrik and van der Aa, Han and Offenberg, Jelmer and Reijers, Hajo A.},
  title     = {Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels},
  series = {Information systems},
  volume    = {83},
  journal   = {Information systems},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0306-4379},
  doi       = {10.1016/j.is.2019.02.005},
  pages     = {30 -- 39},
  year      = {2019},
  abstract  = {Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models' activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.},
  language  = {en}
}
@article{HanniganNendelKrull2022,
  author    = {Hannigan, Sara and Nendel, Claas and Krull, Marcos},
  title     = {Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius},
  series = {Journal of pest science},
  journal   = {Journal of pest science},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1612-4758},
  doi       = {10.1007/s10340-022-01510-7},
  pages     = {389 -- 402},
  year      = {2022},
  abstract  = {Even though the effects of insect pests on global agricultural productivity are well recognised, little is known about movement and dispersal of many species, especially in the context of global warming. This work evaluates how temperature and light conditions affect different movement metrics and the feeding rate of the large lupine beetle, an agricultural pest responsible for widespread damage in leguminous crops. By using video recordings, the movement of 384 beetles was digitally analysed under six different temperatures and light conditions in the laboratory. Bayesian linear mixed-effect models were used to analyse the data. Furthermore, the effects of temperature on the daily diffusion coefficient of beetles were estimated by using hidden Markov models and random walk simulations. Results of this work show that temperature, light conditions, and beetles' weight were the main factors affecting the flight probability, displacement, time being active and the speed of beetles. Significant variations were also observed in all evaluated metrics. On average, beetles exposed to light conditions and higher temperatures had higher mean speed and flight probability. However, beetles tended to stay more active at higher temperatures and less active at intermediate temperatures, around 20 degrees C. Therefore, both the diffusion coefficient and displacement of beetles were lower at intermediate temperatures. These results show that the movement behaviour and feeding rates of beetles can present different relationships in the function of temperature. It also shows that using a single diffusion coefficient for insects in spatially explicit models may lead to over- or underestimation of pest spread.},
  language  = {en}
}