AutoML for Multi-Label Classification: Overview and Empirical Evaluation - Wever, Marcel Dominik; Tornede, Alexander; Mohr, Felix; Hüllermeier, Eyke (IEEE Transactions on Pattern Analysis and Machine Intelligence)
Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.
Keywords: Automated Machine Learning; Multi Label Classification; Hierarchical Planning; Bayesian Optimization

Learning Coflow Admissions - Hasnain, Asif; Karl, Holger (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))
Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.
Keywords: Coflow scheduling; Reinforcement learning; Deadlines

A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks - Afifi, Haitham; Ramaswamy, Arunselvan; Karl, Holger (2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (CCNC 2021))
Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1%. Meanwhile, the RL solution is better than the baseline with improvements up to 23%, when using the trade-off objective.
Keywords: reinforcement learning; wireless sensor networks; resource allocation; acoustic sensor networks

Distributed Online Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Qarawlus, Haydar; Karl, Holger (IEEE International Conference on Distributed Computing Systems (ICDCS))
Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).
Keywords: network management; service management; coordination; reinforcement learning; distributed

Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction - Sander, Tom; Lange, Sven; Hilleringmann, Ulrich; Geneis, Volker; Hedayat, Christian; Kuhn, Harald; Gockel, Franz-Barthold (22nd IEEE International Conference on Industrial Technology (ICIT))
During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.
Keywords: Image Processing; Defect Detection; wooden surfaces; Machine Learning; Neural Networks

Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Khalili, Ramin; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Hecker, Artur (Transactions on Network and Service Management)
Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.
Keywords: network management; service management; coordination; reinforcement learning; self-learning; self-adaptation; multi-objective

Ranking Structured Objects with Graph Neural Networks - Damke, Clemens; Hüllermeier, Eyke (Proceedings of The 24th International Conference on Discovery Science (DS 2021))
Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.
Keywords: Graph-structured data; Graph neural networks; Preference learning; Learning to rank

Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach - Prester, Julian; Wagner, Gerit; Schryen, Guido; Hassan, Nik Rushdi (Decision Support Systems)
Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.
Keywords: Ideational impact; citation classification; academic recommender systems; natural language processing; deep learning; cumulative tradition

DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning - Schneider, Stefan Balthasar; Karl, Holger; Khalili, Ramin; Hecker, Artur
Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.
Keywords: mobility management; coordinated multipoint; CoMP; cell selection; resource management; reinforcement learning; multi agent; MARL; self-learning; self-adaptation; QoE

Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis - Gerholz, Karl-Heinz; Goller, Michael (Edition Fachdidaktiken)
Das Studium der Wirtschaftspädagogik bereitet Studierende auf das didaktische Handeln in beruflichen Lehr-Lernkontexten (u. a. berufliche Schulen, Ausbildung in Betrieben) vor. Theorie-Praxis-Verzahnung ist somit aus zwei Perspektiven zu modellieren: Einerseits geht es um den Aufbau eines fachwissenschaftlichen Verständnisses, welches von den Handlungszusammenhängen in einer beruflichen Domäne mit kaufmännisch-verwaltenden Bezügen ausgeht und weniger auf einer rein fachwissenschaftlichen Bildung beruht. Die zukünftige Berufspraxis der Schülerinnen und Schüler muss in den Blick genommen werden. Andererseits geht es um die Professionalisierung als pädagogisches Personal, welches berufsbezogene Lernprozesse fachdidaktisch gestalten kann. Die zukünftige Lehrpraxis in beruflichen Lehr-Lernkontexten ist in den Blick zu nehmen. Zielstellung des Beitrages ist es, diese doppelte Theorie-Praxis-Verzahnung als Konstitutionsmerkmal der Wirtschaftspädagogik aufzuzeigen (Abschn. 2), um darauf basierend anhand von Theorien des Lernens am Arbeitsplatz Potenziale und Grenzen des Lernortes Praxis als Beitrag zur Professionalisierung angehender Wirtschaftspädagog*innen im Studium herauszuarbeiten (Abschn. 3). Am Beispiel des Konzeptes von Universitätsschulen soll eine Umsetzungsvariante zur Theorie-Praxis-Verzahnung unter Herausarbeitung der Potenziale der jeweiligen Lernorte Schule und Universität aufgezeigt werden (Abschn. 4).
Keywords: Berufliche Lehrerbildung; Professional Learning; Theorie-Praxis-Verzahnung; Wirtschaftspädagogik; Universitätsschulen