Decision-making under Imperfect Information with Bayesian Learning or Heuristic Rules - Burs, Carina; Gries, Thomas
Information is one of the most important ingredients for decision-making. While the neoclassical assumption of perfect information is surely an important conceptual benchmark for discussing efficient allocations, it is obviously far from describing a rational choice under real conditions. In reality, optimal choices should be considered choices under imperfect information. Thus, decision-makers' information problem can be solved by two strategies. Either they collect an optimal set of information to make an optimal allocation choice under this imperfect information set or they can apply heuristic reasoning. In this paper, we suggest a formal model framework for the example of a simple consumer decision for the allocation of differentiated goods to explore information acquisition strategies in such a simple standard choice situation. Using the model variation under perfect information as a benchmark, we answer the following questions. First and most importantly, under imperfect information, can a heuristic rule substitute information acquisition as an optimal choice? Second, what is the role of risk aversion in the information acquisition process? Finally, we explore the differences to the benchmark, both ex ante the first purchase decision and ex post when repeated purchases and consumption allows for experiences with the choices made.
Keywords: information economics; imperfect information; Bayesian learning; risk; heuristics; differentiated products

HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs - Qudus, Umair; Röder, Michael; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (The Semantic Web -- ISWC 2022)
We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.
Keywords: fact checking · ensemble learning · knowledge graph veracit

Ein kritisch-multiperspektivischer Blick auf Forschendes Lernen in der Lehrkräftebildung - Bloh, Thiemo; Caruso, Carina (die hochschullehre)
Forschendes Lernen in der Lehrer:innenbildung ist seit der Ausweitung schulpraktischer Anteile bzw. der Einführung des sogenannten Praxissemesters eng verwoben mit der Lehrer:innenausbildung. Gleichzeitig wird bisher weitestgehend different beantwortet, was Forschendes Lernen ist und sein kann sowie warum es wie hochschuldidaktisch gerahmt wird und werden sollte. Der Beitrag widmet sich dieser Frage. Dabei zeigt sich einerseits, dass hochschuldidaktische Zugänge zur Realisierung Forschenden Lernens in der Lehrer:innenbildung erst vor dem Hintergrund theoretischer Annahmen zur Entwicklung von Lehrpersonen und deren Professionalität sowie zur Gestalt - gemeint ist hier der Beitrag zu dieser Entwicklung sowie der Anteil an Professionalität - Forschenden Lernens entwickelt werden können, eine solche Fundierung aber oftmals ausbleibt. Andererseits wird herausgearbeitet, inwiefern eine Differenz zwischen wissenschaftlicher Forschung und Forschung im Forschenden Lernen besteht. Daran anschließend wird eine habitustheoretische Fundierung Forschenden Lernens vorgestellt und es werden exemplarisch deren Implikationen für die Gestaltung Forschenden Lernens benannt. Abschließend wird anhand empirischer Rekonstruktionen beispielhaft eine praktische Umsetzung diskutiert.
Keywords: Forschendes Lernen; Lehrer:innenausbildung; Praxissemester; Professionalisierung / Research-based learning; teacher education; internships; professionalization

Systematizing the lexicon of platforms in information systems: a data-driven study - Bartelheimer, Christian; zur Heiden, Philipp; Lüttenberg, Hedda; Beverungen, Daniel (Electronic Markets)
While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.
Keywords: Platform; Text mining; Machine learning; Data communications; Interpretive research; Systems design and implementation

Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding - Afifi, Haitham; Sauer, Fabian Jakob; Karl, Holger (2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21))
Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.
Keywords: reinforcement learning; admission control; wireless sensor networks

Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities - Afifi, Haitham; Guenther, Michael; Brendel, Andreas; Karl, Holger; Kellermann, Walter (14. ITG Conference on Speech Communication (ITG 2021))
Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons
Keywords: microphone utility; microphone selection; wireless acoustic sensor network; network delay; reinforcement learning

Learning Flow Scheduling - Hasnain, Asif; Karl, Holger (2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC))
Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.
Keywords: Flow scheduling; Deadlines; Reinforcement learning

Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie - Drumm, Sandra; Müller, Mareike; Stenzel, Nadja (Informationen Deutsch als Fremdsprache)
Das Auftreten der COVID-19-Pandemie stellt Fremdsprachenkurse vielerorts vor Herausforderungen. Unter Zuhilfenahme diverser digitaler Tools werden nicht nur Lernmaterialien online geteilt, sondern auch die Interaktion zwischen Lehrenden und Lernenden sowie der Lernenden untereinander in den virtuellen Raum verlagert. Qualitative Interviews mit den Beteiligten erfassen, wie diese mit den Herausforderungen videogestützten Sprachunterrichts umgehen und welche Strategien sie wählen, um Sprachenlernen zu ermöglichen. Die Ergebnisse zeigen auf, wo seitens der Kursorganisation und -durchführung Handlungsbedarf besteht. ----- The rise of the COVID-19 pandemic challenges the teaching and learning of foreign languages at many institutions. The implementation of various digital tools aids not only the online sharing of learning materials, but also shifts teacher-learner and learner-learner interaction to the virtual space. Via qualitative interviews, this study examines how both teachers and learners handle the challenges of language instruction based on videoconferences, and what strategies they employ to enable language learning. The results highlight areas in need of improvement in terms of course organization and facilitation.
Keywords: German language courses at university; interaction; digital space; language learning/teaching via videoconference

IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING - Bäumer, Frederik Simon; Kersting, Joschka; Denisov, Sergej; Geierhos, Michaela (PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021)
Content is the new oil. Users consume billions of terabytes a day while surfing on news sites or blogs, posting on social media sites, and sending chat messages around the globe. While content is heterogeneous, the dominant form of web content is text. There are situations where more diversity needs to be introduced into text content, for example, to reuse it on websites or to allow a chatbot to base its models on the information conveyed rather than of the language used. In order to achieve this, paraphrasing techniques have been developed: One example is Text spinning, a technique that automatically paraphrases text while leaving the intent intact. This makes it easier to reuse content, or to change the language generated by the bot more human. One method for modifying texts is a combination of translation and back-translation. This paper presents NATTS, a naive approach that uses transformer-based translation models to create diversified text, combining translation steps in one model. An advantage of this approach is that it can be fine-tuned and handle technical language.
Keywords: Software Requirements; Natural Language Processing; Transfer Learning; On-The-Fly Computing

Developing and Evaluating the Concept Data Awareness for K12 Computing Education - Höper, Lukas (21st Koli Calling International Conference on Computing Education Research)
Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.
Keywords: data awareness; machine learning; data science education; data-driven digital artifacts; artificial intelligence