@article{Schweppenhaeuser1997,
  author    = {Schweppenh{\"a}user, Gerhard},
  title     = {Der Sklavenaufstand der instrumentellen Vernunft : philosophische {\"U}berlegungen zur k{\"u}nstlichen Intelligenz},
  doi       = {10.25643/bauhaus-universitaet.1155},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-11559},
  year      = {1997},
  abstract  = {Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universit{\"a}t zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien'},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {de}
}
@article{BrunsBimber2008,
  author    = {Bruns, Erich and Bimber, Oliver},
  title     = {Phone-to-Phone Communication for Adaptive Image Classification},
  doi       = {10.25643/bauhaus-universitaet.1296},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20080722-13685},
  year      = {2008},
  abstract  = {In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide - a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments.},
  subject      = {Peer-to-Peer-Netz},
  language  = {en}
}
@article{BrombachBrunsBimber2008,
  author    = {Brombach, Benjamin and Bruns, Erich and Bimber, Oliver},
  title     = {Subobject Detection through Spatial Relationships on Mobile Phones},
  doi       = {10.25643/bauhaus-universitaet.1353},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20081007-14296},
  year      = {2008},
  abstract  = {We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. Finally, all detected subobjects are labeled and the user can select the object of interest for retrieving corresponding multimedia information. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. We show that the recognition of subobjects using spatial relationships is up to 68\% faster than related approaches without spatial relationships. Results of a field experiment in a local museum illustrate that unexperienced users reach an average recognition rate for subobjects of 85.6\% under realistic conditions.},
  subject      = {Objekterkennung},
  language  = {en}
}
@article{TrapplKrajewskiRuttkayetal.,
  author    = {Trappl, Robert and Krajewski, Markus and Ruttkay, Zs{\´o}fia and Widrich, Virgil},
  title     = {Robots as Companions: What can we Learn from Servants and Companions in Literature, Theater, and Film?},
  series = {Procedia Computer Science},
  journal   = {Procedia Computer Science},
  doi       = {10.1016/j.procs.2011.12.029},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170426-31650},
  pages     = {96 -- 98},
  abstract  = {Many researchers are working on developing robots into adequate partners, be it at the working place, be it at home or in leisure activities, or enabling elder persons to lead a self-determined, independent life. While quite some progress has been made in e.g. speech or emotion understanding, processing and expressing, the relations between humans and robots are usually only short-term. In order to build long-term, i.e. social relations, qualities like empathy, trust building, dependability, non-patronizing, and others will be required. But these are just terms and as such no adequate starting points to "program" these capacities even more how to avoid the problems and pitfalls in interactions between humans and robots. However, a rich source for doing this is available, unused until now for this purpose: artistic productions, namely literature, theater plays, not to forget operas, and films with their multitude of examples. Poets, writers, dramatists, screen-writers, etc. have studied for centuries the facets of interactions between persons, their dynamics, and the related snags. And since we wish for human-robot relations as master-servant relations - the human obviously being the master - the study of these relations will be prominent. A procedure is proposed, with four consecutive steps, namely Selection, Analysis, Categorization, and Integration. Only if we succeed in developing robots which are seen as servants we will be successful in supporting and helping humans through robots.},
  subject      = {Roboter},
  language  = {en}
}
@article{BandJanizadehSahaetal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Saha, Sunil and Mukherjee, Kaustuv and Khosrobeigi Bozchaloei, Saeid and Cerd{\`a}, Artemi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data},
  series = {Land},
  volume    = {2020},
  journal   = {Land},
  number    = {volume 9, issue 10, article 346},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/land9100346},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43424},
  pages     = {1 -- 22},
  abstract  = {Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70\%) and validation (30\%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianLahmerKumarietal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Kumari, Vandana and Jadhav, Kirti},
  title     = {Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {volume 13, issue 13, 3340},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13133340},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200707-41915},
  pages     = {15},
  abstract  = {The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 D{\"u}zce Earthquake in Turkey, where the building's data consists of 22 performance modifiers that have been implemented with supervised machine learning.},
  subject      = {Erdbeben},
  language  = {en}
}