@phdthesis{Andjelkovic2021, author = {Andjelkovic, Marko}, title = {A methodology for characterization, modeling and mitigation of single event transient effects in CMOS standard combinational cells}, doi = {10.25932/publishup-53484}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-534843}, school = {Universit{\"a}t Potsdam}, pages = {xxiv, 216}, year = {2021}, abstract = {With the downscaling of CMOS technologies, the radiation-induced Single Event Transient (SET) effects in combinational logic have become a critical reliability issue for modern integrated circuits (ICs) intended for operation under harsh radiation conditions. The SET pulses generated in combinational logic may propagate through the circuit and eventually result in soft errors. It has thus become an imperative to address the SET effects in the early phases of the radiation-hard IC design. In general, the soft error mitigation solutions should accommodate both static and dynamic measures to ensure the optimal utilization of available resources. An efficient soft-error-aware design should address synergistically three main aspects: (i) characterization and modeling of soft errors, (ii) multi-level soft error mitigation, and (iii) online soft error monitoring. Although significant results have been achieved, the effectiveness of SET characterization methods, accuracy of predictive SET models, and efficiency of SET mitigation measures are still critical issues. Therefore, this work addresses the following topics: (i) Characterization and modeling of SET effects in standard combinational cells, (ii) Static mitigation of SET effects in standard combinational cells, and (iii) Online particle detection, as a support for dynamic soft error mitigation. Since the standard digital libraries are widely used in the design of radiation-hard ICs, the characterization of SET effects in standard cells and the availability of accurate SET models for the Soft Error Rate (SER) evaluation are the main prerequisites for efficient radiation-hard design. This work introduces an approach for the SPICE-based standard cell characterization with the reduced number of simulations, improved SET models and optimized SET sensitivity database. It has been shown that the inherent similarities in the SET response of logic cells for different input levels can be utilized to reduce the number of required simulations. Based on characterization results, the fitting models for the SET sensitivity metrics (critical charge, generated SET pulse width and propagated SET pulse width) have been developed. The proposed models are based on the principle of superposition, and they express explicitly the dependence of the SET sensitivity of individual combinational cells on design, operating and irradiation parameters. In contrast to the state-of-the-art characterization methodologies which employ extensive look-up tables (LUTs) for storing the simulation results, this work proposes the use of LUTs for storing the fitting coefficients of the SET sensitivity models derived from the characterization results. In that way the amount of characterization data in the SET sensitivity database is reduced significantly. The initial step in enhancing the robustness of combinational logic is the application of gate-level mitigation techniques. As a result, significant improvement of the overall SER can be achieved with minimum area, delay and power overheads. For the SET mitigation in standard cells, it is essential to employ the techniques that do not require modifying the cell structure. This work introduces the use of decoupling cells for improving the robustness of standard combinational cells. By insertion of two decoupling cells at the output of a target cell, the critical charge of the cell's output node is increased and the attenuation of short SETs is enhanced. In comparison to the most common gate-level techniques (gate upsizing and gate duplication), the proposed approach provides better SET filtering. However, as there is no single gate-level mitigation technique with optimal performance, a combination of multiple techniques is required. This work introduces a comprehensive characterization of gate-level mitigation techniques aimed to quantify their impact on the SET robustness improvement, as well as introduced area, delay and power overhead per gate. By characterizing the gate-level mitigation techniques together with the standard cells, the required effort in subsequent SER analysis of a target design can be reduced. The characterization database of the hardened standard cells can be utilized as a guideline for selection of the most appropriate mitigation solution for a given design. As a support for dynamic soft error mitigation techniques, it is important to enable the online detection of energetic particles causing the soft errors. This allows activating the power-greedy fault-tolerant configurations based on N-modular redundancy only at the high radiation levels. To enable such a functionality, it is necessary to monitor both the particle flux and the variation of particle LET, as these two parameters contribute significantly to the system SER. In this work, a particle detection approach based on custom-sized pulse stretching inverters is proposed. Employing the pulse stretching inverters connected in parallel enables to measure the particle flux in terms of the number of detected SETs, while the particle LET variations can be estimated from the distribution of SET pulse widths. This approach requires a purely digital processing logic, in contrast to the standard detectors which require complex mixed-signal processing. Besides the possibility of LET monitoring, additional advantages of the proposed particle detector are low detection latency and power consumption, and immunity to error accumulation. The results achieved in this thesis can serve as a basis for establishment of an overall soft-error-aware database for a given digital library, and a comprehensive multi-level radiation-hard design flow that can be implemented with the standard IC design tools. The following step will be to evaluate the achieved results with the irradiation experiments.}, language = {en} } @article{SchrapeAndjelkovicBreitenreiteretal.2021, author = {Schrape, Oliver and Andjelkovic, Marko and Breitenreiter, Anselm and Zeidler, Steffen and Balashov, Alexey and Krstić, Miloš}, title = {Design and evaluation of radiation-hardened standard cell flip-flops}, series = {IEEE transactions on circuits and systems : a publication of the IEEE Circuits and Systems Society: 1, Regular papers}, volume = {68}, journal = {IEEE transactions on circuits and systems : a publication of the IEEE Circuits and Systems Society: 1, Regular papers}, number = {11}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {New York, NY}, issn = {1549-8328}, doi = {10.1109/TCSI.2021.3109080}, pages = {4796 -- 4809}, year = {2021}, abstract = {Use of a standard non-rad-hard digital cell library in the rad-hard design can be a cost-effective solution for space applications. In this paper we demonstrate how a standard non-rad-hard flip-flop, as one of the most vulnerable digital cells, can be converted into a rad-hard flip-flop without modifying its internal structure. We present five variants of a Triple Modular Redundancy (TMR) flip-flop: baseline TMR flip-flop, latch-based TMR flip-flop, True-Single Phase Clock (TSPC) TMR flip-flop, scannable TMR flip-flop and self-correcting TMR flipflop. For all variants, the multi-bit upsets have been addressed by applying special placement constraints, while the Single Event Transient (SET) mitigation was achieved through the usage of customized SET filters and selection of optimal inverter sizes for the clock and reset trees. The proposed flip-flop variants feature differing performance, thus enabling to choose the optimal solution for every sensitive node in the circuit, according to the predefined design constraints. Several flip-flop designs have been validated on IHP's 130nm BiCMOS process, by irradiation of custom-designed shift registers. It has been shown that the proposed TMR flip-flops are robust to soft errors with a threshold Linear Energy Transfer (LET) from (32.4 MeV.cm(2)/mg) to (62.5 MeV.cm(2)/mg), depending on the variant.}, language = {en} } @article{TavakoliAlirezazadehHedayatipouretal.2021, author = {Tavakoli, Hamad and Alirezazadeh, Pendar and Hedayatipour, Ava and Nasib, A. H. Banijamali and Landwehr, Niels}, title = {Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks}, series = {Computers and electronics in agriculture : COMPAG online ; an international journal}, volume = {181}, journal = {Computers and electronics in agriculture : COMPAG online ; an international journal}, publisher = {Elsevier}, address = {Amsterdam [u.a.]}, issn = {0168-1699}, doi = {10.1016/j.compag.2020.105935}, pages = {11}, year = {2021}, abstract = {In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87\% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.}, language = {en} } @article{CabalarFandinoFarinasdelCerro2021, author = {Cabalar, Pedro and Fandi{\~n}o, Jorge and Fari{\~n}as del Cerro, Luis}, title = {Splitting epistemic logic programs}, series = {Theory and practice of logic programming / publ. for the Association for Logic Programming}, volume = {21}, journal = {Theory and practice of logic programming / publ. for the Association for Logic Programming}, number = {3}, publisher = {Cambridge Univ. Press}, address = {Cambridge [u.a.]}, issn = {1471-0684}, doi = {10.1017/S1471068420000058}, pages = {296 -- 316}, year = {2021}, abstract = {Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. As it can be imagined, the associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. We formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing approaches fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond 1991 and a recent proposal by the authors, called Founded Autoepistemic Equilibrium Logic.}, language = {en} } @article{MoeringLeino2022, author = {M{\"o}ring, Sebastian and Leino, Olli Tapio}, title = {Die neoliberale Bedingung von Computerspielen}, series = {Kontrollmaschinen - zur Dispositivtheorie des Computerspiels}, journal = {Kontrollmaschinen - zur Dispositivtheorie des Computerspiels}, publisher = {LiteraturWissenschaft.de}, address = {M{\"u}nster}, isbn = {978-3-643-14780-6}, pages = {41 -- 61}, year = {2022}, language = {de} } @article{PrasseIversenLienhardetal.2022, author = {Prasse, Paul and Iversen, Pascal and Lienhard, Matthias and Thedinga, Kristina and Herwig, Ralf and Scheffer, Tobias}, title = {Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction}, series = {MDPI}, volume = {14}, journal = {MDPI}, edition = {16}, publisher = {MDPI}, address = {Basel, Schweiz}, issn = {2072-6694}, doi = {10.3390/cancers14163950}, pages = {1 -- 14}, year = {2022}, abstract = {Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models' accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.}, language = {en} } @article{Hecher2022, author = {Hecher, Markus}, title = {Treewidth-aware reductions of normal ASP to SAT}, series = {Artificial intelligence}, volume = {304}, journal = {Artificial intelligence}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0004-3702}, doi = {10.1016/j.artint.2021.103651}, pages = {24}, year = {2022}, abstract = {Answer Set Programming (ASP) is a paradigm for modeling and solving problems for knowledge representation and reasoning. There are plenty of results dedicated to studying the hardness of (fragments of) ASP. So far, these studies resulted in characterizations in terms of computational complexity as well as in fine-grained insights presented in form of dichotomy-style results, lower bounds when translating to other formalisms like propositional satisfiability (SAT), and even detailed parameterized complexity landscapes. A generic parameter in parameterized complexity originating from graph theory is the socalled treewidth, which in a sense captures structural density of a program. Recently, there was an increase in the number of treewidth-based solvers related to SAT. While there are translations from (normal) ASP to SAT, no reduction that preserves treewidth or at least keeps track of the treewidth increase is known. In this paper we propose a novel reduction from normal ASP to SAT that is aware of the treewidth, and guarantees that a slight increase of treewidth is indeed sufficient. Further, we show a new result establishing that, when considering treewidth, already the fragment of normal ASP is slightly harder than SAT (under reasonable assumptions in computational complexity). This also confirms that our reduction probably cannot be significantly improved and that the slight increase of treewidth is unavoidable. Finally, we present an empirical study of our novel reduction from normal ASP to SAT, where we compare treewidth upper bounds that are obtained via known decomposition heuristics. Overall, our reduction works better with these heuristics than existing translations. (c) 2021 Elsevier B.V. All rights reserved.}, language = {en} } @article{AlLabanRegerLucke2022, author = {Al Laban, Firas and Reger, Martin and Lucke, Ulrike}, title = {Closing the Policy Gap in the Academic Bridge}, series = {Education sciences}, volume = {12}, journal = {Education sciences}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {2227-7102}, doi = {10.3390/educsci12120930}, year = {2022}, abstract = {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.}, language = {en} } @phdthesis{Boeken2022, author = {B{\"o}ken, Bj{\"o}rn}, title = {Improving prediction accuracy using dynamic information}, doi = {10.25932/publishup-58512}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-585125}, school = {Universit{\"a}t Potsdam}, pages = {xii, 160}, year = {2022}, abstract = {Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions. This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets. Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information. Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.}, language = {en} } @misc{PrasseIversenLienhardetal.2022, author = {Prasse, Paul and Iversen, Pascal and Lienhard, Matthias and Thedinga, Kristina and Herwig, Ralf and Scheffer, Tobias}, title = {Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {1866-8372}, doi = {10.25932/publishup-57734}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-577341}, pages = {1 -- 14}, year = {2022}, abstract = {Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models' accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.}, language = {en} } @misc{AlLabanRegerLucke2022, author = {Al Laban, Firas and Reger, Martin and Lucke, Ulrike}, title = {Closing the Policy Gap in the Academic Bridge}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1310}, issn = {1866-8372}, doi = {10.25932/publishup-58357}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-583572}, pages = {22}, year = {2022}, abstract = {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.}, language = {en} } @misc{Cichalla2022, type = {Master Thesis}, author = {Cichalla, Anika Katleen}, title = {Ein konstruktivistisches Modell f{\"u}r die Didaktik der Informatik im Bachelorstudium}, doi = {10.25932/publishup-55071}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-550710}, school = {Universit{\"a}t Potsdam}, pages = {66}, year = {2022}, abstract = {Lehrende in der Lehrkr{\"a}fteausbildung sind stets damit konfrontiert, dass sie den Studierenden innovative Methoden modernen Schulunterrichts traditionell rezipierend vorstellen. In Deutschland gibt es circa 40 Universit{\"a}ten, die Informatik mit Lehramtsbezug ausbilden. Allerdings gibt es nur wenige Konzepte, die sich mit der Verbindung von Bildungswissenschaften und der Informatik mit ihrer Didaktik besch{\"a}ftigen und keine Konzepte, die eine konstruktivistische Lehre in der Informatik verfolgen. Daher zielt diese Masterarbeit darauf ab, diese L{\"u}cke aufgreifen und anhand des „Didaktik der Informatik I" Moduls der Universit{\"a}t Potsdam ein Modell zur konstruktivistischen Hochschullehre zu entwickeln. Dabei soll ein bestehendes konstruktivistisches Lehrmodell auf die Informatikdidaktik {\"u}bertragen und Elemente zur Verbindung von Bildungswissenschaften, Fachwissenschaften und Fachdidaktiken mit einbezogen werden. Dies kann eine Grundlage f{\"u}r die Planung von Informatikdidaktischen Modulen bieten, aber auch als Inspiration zur {\"U}bertragung bestehender innovativer Lehrkonzepte auf andere Fachdidaktiken dienen. Um ein solches konstruktivistisches Lehr-Lern-Modell zu erstellen, wird zun{\"a}chst der Zusammenhang von Bildungswissenschaften, Fachwissenschaften und Fachdidaktiken erl{\"a}utert und anschließend die Notwendigkeit einer Vernetzung hervorgehoben. Hieran folgt eine Darstellung zu relevanten Lerntheorien und bereits entwickelten innovativen Lernkonzepten. Ankn{\"u}pfend wird darauf eingegangen, welche Anforderungen die Kultusminister- Konferenz an die Ausbildung von Lehrkr{\"a}ften stellt und wie diese Ausbildung f{\"u}r die Informatik momentan an der Universit{\"a}t Potsdam erfolgt. Aus allen Erkenntnissen heraus werden Anforderungen an ein konstruktivistisches Lehrmodell festgelegt. Unter Ber{\"u}cksichtigung der Voraussetzungen der Studienordnung f{\"u}r das Lehramt Informatik wird anschließend ein Modell f{\"u}r konstruktivistische Informatikdidaktik vorgestellt. Weiterf{\"u}hrende Forschung k{\"o}nnte sich damit auseinandersetzen, inwiefern sich die Motivation und Leistung im vergleich zum urspr{\"u}nglichen Modul {\"a}ndert und ob die Kompetenzen zur Unterrichtsplanung und Unterrichtsgestaltung durch das neue Modulkonzept st{\"a}rker ausgebaut werden k{\"o}nnen.}, language = {de} } @article{LorenzClemensSchroetteretal.2022, author = {Lorenz, Claas and Clemens, Vera Elisabeth and Schr{\"o}tter, Max and Schnor, Bettina}, title = {Continuous verification of network security compliance}, series = {IEEE transactions on network and service management}, volume = {19}, journal = {IEEE transactions on network and service management}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {New York}, issn = {1932-4537}, doi = {10.1109/TNSM.2021.3130290}, pages = {1729 -- 1745}, year = {2022}, abstract = {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.}, language = {en} } @article{PrasseIversenLienhardetal.2022, author = {Prasse, Paul and Iversen, Pascal and Lienhard, Matthias and Thedinga, Kristina and Bauer, Christopher and Herwig, Ralf and Scheffer, Tobias}, title = {Matching anticancer compounds and tumor cell lines by neural networks with ranking loss}, series = {NAR: genomics and bioinformatics}, volume = {4}, journal = {NAR: genomics and bioinformatics}, number = {1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {2631-9268}, doi = {10.1093/nargab/lqab128}, pages = {10}, year = {2022}, abstract = {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.}, language = {en} } @article{SteinertStabernack2022, author = {Steinert, Fritjof and Stabernack, Benno}, title = {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}, series = {Journal of Signal Processing Systems for Signal, Image, and Video Technology}, volume = {94}, journal = {Journal of Signal Processing Systems for Signal, Image, and Video Technology}, number = {7}, publisher = {Springer}, address = {New York}, issn = {1939-8018}, doi = {10.1007/s11265-021-01727-2}, pages = {693 -- 708}, year = {2022}, abstract = {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.}, language = {en} } @article{MarcoFigueraRiedelRossietal.2022, author = {Marco Figuera, Ramiro and Riedel, Christian and Rossi, Angelo Pio and Unnithan, Vikram}, title = {Depth to diameter analysis on small simple craters at the lunar south pole - possible implications for ice harboring}, series = {Remote sensing}, volume = {14}, journal = {Remote sensing}, number = {3}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs14030450}, pages = {13}, year = {2022}, abstract = {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.}, language = {en} } @article{AbdelwahabLandwehr2022, author = {Abdelwahab, Ahmed and Landwehr, Niels}, title = {Deep Distributional Sequence Embeddings Based on a Wasserstein Loss}, series = {Neural processing letters}, journal = {Neural processing letters}, publisher = {Springer}, address = {Dordrecht}, issn = {1370-4621}, doi = {10.1007/s11063-022-10784-y}, pages = {21}, year = {2022}, abstract = {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.}, language = {en} } @article{TranPontelliBalduccinietal.2022, author = {Tran, Son Cao and Pontelli, Enrico and Balduccini, Marcello and Schaub, Torsten}, title = {Answer set planning}, series = {Theory and practice of logic programming}, journal = {Theory and practice of logic programming}, publisher = {Cambridge University Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068422000072}, pages = {73}, year = {2022}, abstract = {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.}, language = {en} } @article{MichallekGenskeNiehuesetal.2022, author = {Michallek, Florian and Genske, Ulrich and Niehues, Stefan Markus and Hamm, Bernd and Jahnke, Paul}, title = {Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging}, series = {European Radiology}, volume = {32}, journal = {European Radiology}, number = {7}, publisher = {Springer}, address = {New York}, issn = {1432-1084}, doi = {10.1007/s00330-022-08592-y}, pages = {4587 -- 4595}, year = {2022}, abstract = {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.}, language = {en} } @article{BandyopadhyaySarkarMandaletal.2022, author = {Bandyopadhyay, Soumyadip and Sarkar, Dipankar and Mandal, Chittaranjan and Giese, Holger}, title = {Translation validation of coloured Petri net models of programs on integers}, series = {Acta informatica}, volume = {59}, journal = {Acta informatica}, number = {6}, publisher = {Springer}, address = {New York}, issn = {0001-5903}, doi = {10.1007/s00236-022-00419-z}, pages = {725 -- 759}, year = {2022}, abstract = {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.}, language = {en} }