@article{LamprechtWickertMargaria2014, author = {Lamprecht, Anna-Lena and Wickert, Alexander and Margaria, Tiziana}, title = {Lessons Learned}, series = {Process Design for Natural Scientists: an agile model-driven approach}, journal = {Process Design for Natural Scientists: an agile model-driven approach}, number = {500}, editor = {Lambrecht, Anna-Lena and Margaria, Tiziana}, publisher = {Springer Verlag}, address = {Berlin}, isbn = {978-3-662-45005-5}, issn = {1865-0929}, pages = {45 -- 64}, year = {2014}, abstract = {This chapter summarizes the experience and the lessons we learned concerning the application of the jABC as a framework for design and execution of scientific workflows. It reports experiences from the domain modeling (especially service integration) and workflow design phases and evaluates the resulting models statistically with respect to the SIB library and hierarchy levels.}, language = {en} } @article{LamprechtWickert2014, author = {Lamprecht, Anna-Lena and Wickert, Alexander}, title = {The Course's SIB Libraries}, series = {Process Design for Natural Scientists: an agile model-driven approach}, journal = {Process Design for Natural Scientists: an agile model-driven approach}, number = {500}, editor = {Lambrecht, Anna-Lena and Margaria, Tiziana}, publisher = {Springer Verlag}, address = {Berlin}, isbn = {978-3-662-45005-5}, issn = {1865-0929}, pages = {30 -- 44}, year = {2014}, abstract = {This chapter gives a detailed description of the service framework underlying all the example projects that form the foundation of this book. It describes the different SIB libraries that we made available for the course "Process modeling in the natural sciences" to provide the functionality that was required for the envisaged applications. The students used these SIB libraries to realize their projects.}, language = {en} } @article{LamprechtMargaria2015, author = {Lamprecht, Anna-Lena and Margaria, Tiziana}, title = {Scientific workflows and XMDD}, series = {Process design for natural scientists}, journal = {Process design for natural scientists}, editor = {Lamprecht, Anna-Lena and Margaria, Tiziana}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-662-45006-2}, pages = {1 -- 13}, year = {2015}, language = {en} } @article{AlSaffar2013, author = {Al-Saffar, Loay Talib Ahmed}, title = {Where girls take the role of boys in CS}, series = {Commentarii informaticae didacticae : (CID)}, journal = {Commentarii informaticae didacticae : (CID)}, number = {5}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {1868-0844}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-65034}, pages = {149 -- 154}, year = {2013}, abstract = {A survey has been carried out in the Computer Science (CS) department at the University of Baghdad to investigate the attitudes of CS students in a female dominant environment, showing the differences between male and female students in different academic years. We also compare the attitudes of the freshman students of two different cultures (University of Baghdad, Iraq, and the University of Potsdam).}, language = {en} } @article{Petre2013, author = {Petre, Marian}, title = {Computing is not a spectator sport}, series = {Commentarii informaticae didacticae : (CID)}, journal = {Commentarii informaticae didacticae : (CID)}, number = {5}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {1868-0844}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-65045}, pages = {155 -- 159}, year = {2013}, abstract = {This talk will describe My Digital Life (TU100), a distance learning module that introduces computer science through immediate engagement with ubiquitous computing (ubicomp). This talk will describe some of the principles and concepts we have adopted for this modern computing introduction: the idea of the 'informed digital citizen'; engagement through narrative; playful pedagogy; making the power of ubicomp available to novices; setting technical skills in real contexts. It will also trace how the pedagogy is informed by experiences and research in Computer Science education.}, language = {en} } @article{GautamZhangLandwehretal.2021, author = {Gautam, Khem Raj and Zhang, Guoqiang and Landwehr, Niels and Adolphs, Julian}, title = {Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building}, series = {Computers and electronics in agriculture : COMPAG online ; an international journal}, volume = {187}, journal = {Computers and electronics in agriculture : COMPAG online ; an international journal}, publisher = {Elsevier Science}, address = {Amsterdam [u.a.]}, issn = {0168-1699}, doi = {10.1016/j.compag.2021.106259}, pages = {10}, year = {2021}, abstract = {In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87\% of time, on average, by 0.53 degrees C compared to the current deviations.}, language = {en} } @article{BredeBotta2021, author = {Brede, Nuria and Botta, Nicola}, title = {On the correctness of monadic backward induction}, series = {Journal of functional programming}, volume = {31}, journal = {Journal of functional programming}, publisher = {Cambridge University Press}, address = {Cambridge}, issn = {1469-7653}, doi = {10.1017/S0956796821000228}, pages = {39}, year = {2021}, abstract = {In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material.}, language = {en} } @article{ChenLangeAndjelkovicetal.2022, author = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstić, Miloš}, title = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning}, series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, volume = {10}, journal = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {[New York, NY]}, issn = {2168-6750}, doi = {10.1109/TETC.2022.3147376}, pages = {564 -- 580}, year = {2022}, abstract = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.}, language = {en} } @article{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} }