@article{SeewannVerwiebeBuderetal.2022,
  author    = {Seewann, Lena and Verwiebe, Roland and Buder, Claudia and Fritsch, Nina-Sophie},
  title     = {"Broadcast your gender."},
  series = {Frontiers in Big Data},
  journal   = {Frontiers in Big Data},
  number    = {5},
  publisher = {Frontiers},
  address   = {Lausanne, Schweiz},
  issn      = {2624-909X},
  doi       = {10.3389/fdata.2022.908636},
  pages     = {16},
  year      = {2022},
  abstract  = {Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Na{\"i}ve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.},
  language  = {en}
}
@article{ChenLangeAndjelkovicetal.2022,
  author    = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstić, Miloš},
  title     = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning},
  series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  volume    = {10},
  journal   = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  number    = {2},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {[New York, NY]},
  issn      = {2168-6750},
  doi       = {10.1109/TETC.2022.3147376},
  pages     = {564 -- 580},
  year      = {2022},
  abstract  = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.},
  language  = {en}
}
@article{PanzerBenderGronau2022,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Neural agent-based production planning and control},
  series = {Journal of Manufacturing Systems},
  volume    = {65},
  journal   = {Journal of Manufacturing Systems},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0278-6125},
  doi       = {10.1016/j.jmsy.2022.10.019},
  pages     = {743 -- 766},
  year      = {2022},
  abstract  = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.},
  language  = {en}
}