TY - GEN A1 - Al Laban, Firas A1 - Reger, Martin A1 - Lucke, Ulrike T1 - Closing the Policy Gap in the Academic Bridge T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - The highly structured nature of the educational sector demands effective policy mechanisms close to the needs of the field. That is why evidence-based policy making, endorsed by the European Commission under Erasmus+ Key Action 3, aims to make an alignment between the domains of policy and practice. Against this background, this article addresses two issues: First, that there is a vertical gap in the translation of higher-level policies to local strategies and regulations. Second, that there is a horizontal gap between educational domains regarding the policy awareness of individual players. This was analyzed in quantitative and qualitative studies with domain experts from the fields of virtual mobility and teacher training. From our findings, we argue that the combination of both gaps puts the academic bridge from secondary to tertiary education at risk, including the associated knowledge proficiency levels. We discuss the role of digitalization in the academic bridge by asking the question: which value does the involved stakeholders expect from educational policies? As a theoretical basis, we rely on the model of value co-creation for and by stakeholders. We describe the used instruments along with the obtained results and proposed benefits. Moreover, we reflect on the methodology applied, and we finally derive recommendations for future academic bridge policies. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1310 KW - policy evaluation KW - higher education KW - virtual mobility KW - teacher training Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-583572 SN - 1866-8372 IS - 1310 ER - TY - THES A1 - Cichalla, Anika Katleen T1 - Ein konstruktivistisches Modell für die Didaktik der Informatik im Bachelorstudium T1 - A constructivistic model for the didactics of computational science in bachelor studies N2 - Lehrende in der Lehrkräfteausbildung sind stets damit konfrontiert, dass sie den Studierenden innovative Methoden modernen Schulunterrichts traditionell rezipierend vorstellen. In Deutschland gibt es circa 40 Universitäten, die Informatik mit Lehramtsbezug ausbilden. Allerdings gibt es nur wenige Konzepte, die sich mit der Verbindung von Bildungswissenschaften und der Informatik mit ihrer Didaktik beschäftigen und keine Konzepte, die eine konstruktivistische Lehre in der Informatik verfolgen. Daher zielt diese Masterarbeit darauf ab, diese Lücke aufgreifen und anhand des „Didaktik der Informatik I“ Moduls der Universität Potsdam ein Modell zur konstruktivistischen Hochschullehre zu entwickeln. Dabei soll ein bestehendes konstruktivistisches Lehrmodell auf die Informatikdidaktik übertragen und Elemente zur Verbindung von Bildungswissenschaften, Fachwissenschaften und Fachdidaktiken mit einbezogen werden. Dies kann eine Grundlage für die Planung von Informatikdidaktischen Modulen bieten, aber auch als Inspiration zur Übertragung bestehender innovativer Lehrkonzepte auf andere Fachdidaktiken dienen. Um ein solches konstruktivistisches Lehr-Lern-Modell zu erstellen, wird zunächst der Zusammenhang von Bildungswissenschaften, Fachwissenschaften und Fachdidaktiken erläutert und anschließend die Notwendigkeit einer Vernetzung hervorgehoben. Hieran folgt eine Darstellung zu relevanten Lerntheorien und bereits entwickelten innovativen Lernkonzepten. Anknüpfend wird darauf eingegangen, welche Anforderungen die Kultusminister- Konferenz an die Ausbildung von Lehrkräften stellt und wie diese Ausbildung für die Informatik momentan an der Universität Potsdam erfolgt. Aus allen Erkenntnissen heraus werden Anforderungen an ein konstruktivistisches Lehrmodell festgelegt. Unter Berücksichtigung der Voraussetzungen der Studienordnung für das Lehramt Informatik wird anschließend ein Modell für konstruktivistische Informatikdidaktik vorgestellt. Weiterführende Forschung könnte sich damit auseinandersetzen, inwiefern sich die Motivation und Leistung im vergleich zum ursprünglichen Modul ändert und ob die Kompetenzen zur Unterrichtsplanung und Unterrichtsgestaltung durch das neue Modulkonzept stärker ausgebaut werden können. N2 - Teachers in teacher training are always confronted with the fact that they present innovative methods of modern school teaching to students in a traditionally receptive way. In Germany, there are about 40 universities that train computational science with a focus on teaching. However, there are only a few concepts that deal with the connection of educational science and computer science with its didactics and no concepts that pursue constructivist teaching in computational science. Therefore, this master thesis aims to address this gap and to develop a model for constructivist university teaching based on the "Didactics of Computational Science I" module at the University of Potsdam. An existing constructivist teaching model is to be transferred to computational science didactics and elements for the connection of general pedagogy, scientific theory and didactics are to be included. This can provide a basis for planning computational science didactic modules, but also serve as inspiration for transferring existing innovative teaching concepts to other subject didactics. In order to create such a constructivist teaching-learning model, the interrelationship of general pedagogy, scientific theory and didactics is first explained and then the necessity of networking is emphasized. This is followed by a presentation of relevant learning theories and innovative learning concepts already developed. Subsequently, the requirements of the Standing Conference of the Ministers of Education and Cultural Affairs (Kultusministerkonferenz) for the training of teachers and how this training for computer science is currently carried out at the University of Potsdam are discussed. From all findings, requirements for a constructivist teaching model are defined. Taking into account the requirements of the study regulations for the computer science teaching profession, a model for constructivist computer science didactics is then presented. Further research could address the extent to which motivation and performance change in comparison to the original module and whether the competencies for lesson planning and lesson design can be more developed on base of the new module concept. KW - education KW - university education KW - teacher training KW - Hochschulbildung KW - Lehrkräfteausbildung KW - Konstruktivismus KW - construktivism KW - Informatik KW - Informatikdidaktik KW - Computational Science KW - didactics Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-550710 ER - TY - JOUR A1 - Lorenz, Claas A1 - Clemens, Vera Elisabeth A1 - Schrötter, Max A1 - Schnor, Bettina T1 - Continuous verification of network security compliance JF - IEEE transactions on network and service management N2 - Continuous verification of network security compliance is an accepted need. Especially, the analysis of stateful packet filters plays a central role for network security in practice. But the few existing tools which support the analysis of stateful packet filters are based on general applicable formal methods like Satifiability Modulo Theories (SMT) or theorem prover and show runtimes in the order of minutes to hours making them unsuitable for continuous compliance verification. In this work, we address these challenges and present the concept of state shell interweaving to transform a stateful firewall rule set into a stateless rule set. This allows us to reuse any fast domain specific engine from the field of data plane verification tools leveraging smart, very fast, and domain specialized data structures and algorithms including Header Space Analysis (HSA). First, we introduce the formal language FPL that enables a high-level human-understandable specification of the desired state of network security. Second, we demonstrate the instantiation of a compliance process using a verification framework that analyzes the configuration of complex networks and devices - including stateful firewalls - for compliance with FPL policies. Our evaluation results show the scalability of the presented approach for the well known Internet2 and Stanford benchmarks as well as for large firewall rule sets where it outscales state-of-the-art tools by a factor of over 41. KW - Security KW - Tools KW - Network security KW - Engines KW - Benchmark testing; KW - Analytical models KW - Scalability KW - Network KW - security KW - compliance KW - formal KW - verification Y1 - 2021 U6 - https://doi.org/10.1109/TNSM.2021.3130290 SN - 1932-4537 VL - 19 IS - 2 SP - 1729 EP - 1745 PB - Institute of Electrical and Electronics Engineers CY - New York ER - TY - JOUR A1 - Prasse, Paul A1 - Iversen, Pascal A1 - Lienhard, Matthias A1 - Thedinga, Kristina A1 - Bauer, Christopher A1 - Herwig, Ralf A1 - Scheffer, Tobias T1 - Matching anticancer compounds and tumor cell lines by neural networks with ranking loss JF - NAR: genomics and bioinformatics N2 - Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data. Y1 - 2022 U6 - https://doi.org/10.1093/nargab/lqab128 SN - 2631-9268 VL - 4 IS - 1 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Steinert, Fritjof A1 - Stabernack, Benno T1 - Architecture of a low latency H.264/AVC video codec for robust ML based image classification how region of interests can minimize the impact of coding artifacts JF - Journal of Signal Processing Systems for Signal, Image, and Video Technology N2 - The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays. KW - H.264 KW - Advanced Video Codec (AVC) KW - Low Latency KW - Region of Interest KW - Machine Learning KW - Inference KW - FPGA KW - Hardware accelerator Y1 - 2022 U6 - https://doi.org/10.1007/s11265-021-01727-2 SN - 1939-8018 SN - 1939-8115 VL - 94 IS - 7 SP - 693 EP - 708 PB - Springer CY - New York ER - TY - JOUR A1 - Marco Figuera, Ramiro A1 - Riedel, Christian A1 - Rossi, Angelo Pio A1 - Unnithan, Vikram T1 - Depth to diameter analysis on small simple craters at the lunar south pole - possible implications for ice harboring JF - Remote sensing N2 - In this paper, we present a study comparing the depth to diameter (d/D) ratio of small simple craters (200-1000 m) of an area between -88.5 degrees to -90 degrees latitude at the lunar south pole containing Permanent Shadowed Regions (PSRs) versus craters without PSRs. As PSRs can reach temperatures of 110 K and are capable of harboring volatiles, especially water ice, we analyzed the relationship of depth versus diameter ratios and its possible implications for harboring water ice. Variations in the d/D ratios can also be caused by other processes such as degradation, isostatic adjustment, or differences in surface properties. The conducted d/D ratio analysis suggests that a differentiation between craters containing PSRs versus craters without PSRs occurs. Thus, a possible direct relation between d/D ratio, PSRs, and water ice harboring might exist. Our results suggest that differences in the target's surface properties may explain the obtained results. The resulting d/D ratios of craters with PSRs can help to select target areas for future In-Situ Resource Utilization (ISRU) missions. KW - craters KW - lunar exploration KW - ice harboring Y1 - 2022 U6 - https://doi.org/10.3390/rs14030450 SN - 2072-4292 VL - 14 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Abdelwahab, Ahmed A1 - Landwehr, Niels T1 - Deep Distributional Sequence Embeddings Based on a Wasserstein Loss JF - Neural processing letters N2 - Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions. KW - Metric learning KW - Sequence embeddings KW - Deep learning Y1 - 2022 U6 - https://doi.org/10.1007/s11063-022-10784-y SN - 1370-4621 SN - 1573-773X PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Tran, Son Cao A1 - Pontelli, Enrico A1 - Balduccini, Marcello A1 - Schaub, Torsten T1 - Answer set planning BT - a survey JF - Theory and practice of logic programming N2 - Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research. KW - planning KW - knowledge representation and reasoning KW - logic programming Y1 - 2022 U6 - https://doi.org/10.1017/S1471068422000072 SN - 1471-0684 SN - 1475-3081 PB - Cambridge University Press CY - New York ER - TY - JOUR A1 - Michallek, Florian A1 - Genske, Ulrich A1 - Niehues, Stefan Markus A1 - Hamm, Bernd A1 - Jahnke, Paul T1 - Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging BT - a phantom study JF - European Radiology N2 - Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KW - Tomography KW - X-ray computed KW - Phantoms KW - imaging KW - Liver neoplasms KW - Algorithms KW - Reproducibility of results Y1 - 2022 U6 - https://doi.org/10.1007/s00330-022-08592-y SN - 1432-1084 VL - 32 IS - 7 SP - 4587 EP - 4595 PB - Springer CY - New York ER - TY - JOUR A1 - Bandyopadhyay, Soumyadip A1 - Sarkar, Dipankar A1 - Mandal, Chittaranjan A1 - Giese, Holger T1 - Translation validation of coloured Petri net models of programs on integers JF - Acta informatica N2 - Programs are often subjected to significant optimizing and parallelizing transformations based on extensive dependence analysis. Formal validation of such transformations needs modelling paradigms which can capture both control and data dependences in the program vividly. Being value-based with an inherent scope of capturing parallelism, the untimed coloured Petri net (CPN) models, reported in the literature, fit the bill well; accordingly, they are likely to be more convenient as the intermediate representations (IRs) of both the source and the transformed codes for translation validation than strictly sequential variable-based IRs like sequential control flow graphs (CFGs). In this work, an efficient path-based equivalence checking method for CPN models of programs on integers is presented. Extensive experimentation has been carried out on several sequential and parallel examples. Complexity and correctness issues have been treated rigorously for the method. Y1 - 2022 U6 - https://doi.org/10.1007/s00236-022-00419-z SN - 0001-5903 SN - 1432-0525 VL - 59 IS - 6 SP - 725 EP - 759 PB - Springer CY - New York ER -