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- ItemAn AI-based open recommender system for personalized labor market driven education(Amsterdam [u.a.] : Elsevier Science, 2022) Tavakoli, Mohammadreza; Faraji, Abdolali; Vrolijk, Jarno; Molavi, Mohammadreza; Mol, Stefan T.; Kismihók, GáborAttaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements
- ItemArtificial intelligence in marketing: friend or foe of sustainable consumption?(London : Springer, 2021) Hermann, Erik[No abstract available]
- ItemElectron beam induced dehydrogenation of MgH2 studied by VEELS(Cham : Springer International Publishing AG, 2016) Surrey, Alexander; Schultz, Ludwig; Rellinghaus, BerndNanosized or nanoconfined hydrides are promising materials for solid-state hydrogen storage. Most of these hydrides, however, degrade fast during the structural characterization utilizing transmission electron microscopy (TEM) upon the irradiation with the imaging electron beam due to radiolysis. We use ball-milled MgH2 as a reference material for in-situ TEM experiments under low-dose conditions to study and quantitatively understand the electron beam-induced dehydrogenation. For this, valence electron energy loss spectroscopy (VEELS) measurements are conducted in a monochromated FEI Titan3 80–300 microscope. From observing the plasmonic absorptions it is found that MgH2 successively converts into Mg upon electron irradiation. The temporal evolution of the spectra is analyzed quantitatively to determine the thickness-dependent, characteristic electron doses for electron energies of both 80 and 300 keV. The measured electron doses can be quantitatively explained by the inelastic scattering of the incident high-energy electrons by the MgH2 plasmon. The obtained insights are also relevant for the TEM characterization of other hydrides.
- ItemPrecise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera(Basel : MDPI, 2015) Dworak, Volker; Huebner, Michael; Selbeck, JoernMost of the relevant technology related to precision agriculture is currently controlled by Global Positioning Systems (GPS) and uploaded map data; however, in sensitive areas with young or expensive plants, small robots are becoming more widely used in exclusive work. These robots must follow the plant lines with centimeter precision to protect plant growth. For cases in which GPS fails, a camera-based solution is often used for navigation because of the system cost and simplicity. The low-cost plant camera presented here generates images in which plants are contrasted against the soil, thus enabling the use of simple cross-correlation functions to establish high-resolution navigation control in the centimeter range. Based on the foresight provided by images from in front of the vehicle, robust vehicle control can be established without any dead time; as a result, off-loading the main robot control and overshooting can be avoided.
- ItemMagnetofluidic platform for multidimensional magnetic and optical barcoding of droplets(Cambridge : RSC, 2014) Lin, Gungun; Makarov, Denys; Medina-Sánchez, Mariana; Guix, Maria; Baraban, Larysa; Cuniberti, Gianaurelio; Schmidt, Oliver G.We present a concept of multidimensional magnetic and optical barcoding of droplets based on a magnetofluidic platform. The platform comprises multiple functional areas, such as an encoding area, an encoded droplet pool and a magnetic decoding area with integrated giant magnetoresistive (GMR) sensors. To prove this concept, penicillin functionalized with fluorescent dyes is coencapsulated with magnetic nanoparticles into droplets. While fluorescent dyes are used as conventional optical barcodes which are decoded with an optical decoding setup, an additional dimensionality of barcodes is created by using magnetic nanoparticles as magnetic barcodes for individual droplets and integrated micro-patterned GMR sensors as the corresponding magnetic decoding devices. The strategy of incorporating a magnetic encoding scheme provides a dynamic range of ~40 dB in addition to that of the optical method. When combined with magnetic barcodes, the encoding capacity can be increased by more than 1 order of magnitude compared with using only optical barcodes, that is, the magnetic platform provides more than 10 unique magnetic codes in addition to each optical barcode. Besides being a unique magnetic functional element for droplet microfluidics, the platform is capable of on-demand facile magnetic encoding and real-time decoding of droplets which paves the way for the development of novel non-optical encoding schemes for highly multiplexed droplet-based biological assays.
- ItemOn specification-based cyber-attack detection in smart grids(2022) Sen, Ömer; van der Velde, Dennis; Lühman, Maik; Sprünken, Florian; Hacker, Immanuel; Ulbig, Andreas; Andres, Michael; Henze, MartinThe transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication flows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner.
- ItemFirst-Order Methods for Convex Optimization(Amsterdam : Elsevier, 2021) Dvurechensky, Pavel; Shtern, Shimrit; Staudigl, MathiasFirst-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important class of algorithms is motivated by the success stories reported in various applications, including most importantly machine learning, signal processing, imaging and control theory. First-order methods have the potential to provide low accuracy solutions at low computational complexity which makes them an attractive set of tools in large-scale optimization problems. In this survey, we cover a number of key developments in gradient-based optimization methods. This includes non-Euclidean extensions of the classical proximal gradient method, and its accelerated versions. Additionally we survey recent developments within the class of projection-free methods, and proximal versions of primal-dual schemes. We give complete proofs for various key results, and highlight the unifying aspects of several optimization algorithms.
- ItemA function space framework for structural total variation regularization with applications in inverse problems(Bristol [u.a.] : Inst., 2018) Hintermüller, Michael; Holler, Martin; Papafitsoros, KostasIn this work, we introduce a function space setting for a wide class of structural/weighted total variation (TV) regularization methods motivated by their applications in inverse problems. In particular, we consider a regularizer that is the appropriate lower semi-continuous envelope (relaxation) of a suitable TV type functional initially defined for sufficiently smooth functions. We study examples where this relaxation can be expressed explicitly, and we also provide refinements for weighted TV for a wide range of weights. Since an integral characterization of the relaxation in function space is, in general, not always available, we show that, for a rather general linear inverse problems setting, instead of the classical Tikhonov regularization problem, one can equivalently solve a saddle-point problem where no a priori knowledge of an explicit formulation of the structural TV functional is needed. In particular, motivated by concrete applications, we deduce corresponding results for linear inverse problems with norm and Poisson log-likelihood data discrepancy terms. Finally, we provide proof-of-concept numerical examples where we solve the saddle-point problem for weighted TV denoising as well as for MR guided PET image reconstruction.
- ItemResistance of the Montgomery Ladder Against Simple SCA: Theory and Practice(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Kabin, Ievgen; Dyka, Zoya; Klann, Dan; Aftowicz, Marcin; Langendoerfer, PeterThe Montgomery kP algorithm i.e. the Montgomery ladder is reported in literature as resistant against simple SCA due to the fact that the processing of each key bit value of the scalar k is done using the same sequence of operations. We implemented the Montgomery kP algorithm using Lopez-Dahab projective coordinates for the NIST elliptic curve B-233. We instantiated the same VHDL code for a wide range of clock frequencies for the same target FPGA and using the same compiler options. We measured electromagnetic traces of the kP executions using the same input data, i.e. scalar k and elliptic curve point P, and measurement setup. Additionally, we synthesized the same VHDL code for two IHP CMOS technologies, for a broad spectrum of frequencies. We simulated the power consumption of each synthesized design during an execution of the kP operation, always using the same scalar k and elliptic curve point P as inputs. Our experiments clearly show that the success of simple electromagnetic analysis attacks against FPGA implementations as well as the one of simple power analysis attacks against synthesized ASIC designs depends on the target frequency for which the design was implemented and at which it is executed significantly. In our experiments the scalar k was successfully revealed via simple visual inspection of the electromagnetic traces of the FPGA for frequencies from 40 to 100 MHz when standard compile options were used as well as from 50 MHz up to 240 MHz when performance optimizing compile options were used. We obtained similar results attacking the power traces simulated for the ASIC. Despite the significant differences of the here investigated technologies the designs’ resistance against the attacks performed is similar: only a few points in the traces represent strong leakage sources allowing to reveal the key at very low and very high frequencies. For the “middle” frequencies the number of points which allow to successfully reveal the key increases when increasing the frequency.
- ItemHigh order discretization methods for spatial-dependent epidemic models(Amsterdam [u.a.] : Elsevier Science, 2022) Takács, Bálint; Hadjimichael, YiannisIn this paper, an epidemic model with spatial dependence is studied and results regarding its stability and numerical approximation are presented. We consider a generalization of the original Kermack and McKendrick model in which the size of the populations differs in space. The use of local spatial dependence yields a system of partial-differential equations with integral terms. The uniqueness and qualitative properties of the continuous model are analyzed. Furthermore, different spatial and temporal discretizations are employed, and step-size restrictions for the discrete model’s positivity, monotonicity preservation, and population conservation are investigated. We provide sufficient conditions under which high-order numerical schemes preserve the stability of the computational process and provide sufficiently accurate numerical approximations. Computational experiments verify the convergence and accuracy of the numerical methods.