@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}
}
@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}
}