@inproceedings{33510,
  abstract     = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}},
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Machine Learning, CNN, Hashing, semi-supervised learning}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}},
  doi          = {{10.1109/ssi56489.2022.9901433}},
  year         = {{2022}},
}

@article{48878,
  abstract     = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}},
  author       = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}},
  issn         = {{2076-3417}},
  journal      = {{Applied Sciences}},
  keywords     = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}},
  number       = {{18}},
  pages        = {{9094}},
  publisher    = {{{Multidisciplinary Digital Publishing Institute}}},
  title        = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}},
  doi          = {{10.3390/app12189094}},
  volume       = {{12}},
  year         = {{2022}},
}

@inproceedings{34674,
  abstract     = {{Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.}},
  author       = {{Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf, Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}},
  booktitle    = {{International Conference on Human-Agent Interaction}},
  keywords     = {{human-agent interaction, smart homes, supervised learning, participation}},
  location     = {{Christchurch, New Zealand}},
  publisher    = {{ACM}},
  title        = {{{User Involvement in Training Smart Home Agents}}},
  doi          = {{10.1145/3527188.3561914}},
  year         = {{2022}},
}