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The importance of machine learning has been increasing dramatically for years. From assistance systems to production optimisation to support the health sector, almost every area of daily life and industry comes into contact with machine learning. Besides all the benefits that ML brings, the lack of transparency and the difficulty in creating traceability pose major risks. While there are solutions that make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge. Unnoticed modification of a model is also a danger when using ML. One solution is to create an ML birth certificate and an ML family tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
This paper describes the concept and some results of the project "Menschen Lernen Maschinelles Lernen" (Humans Learn Machine Learning, ML2) of the University of Applied Sciences Offenburg. It brings together students of different courses of study and practitioners from companies on the subject of Machine Learning. A mixture of blended learning and practical projects ensures a tight coupling of machine learning theory and application. The paper details the phases of ML2 and mentions two successful example projects.
eLetter zum Artikel "Condiciones neuropsi-quiátricas y probable causa de muerte de Maurice Ravel" von Gómez-Carvajal AM, Botero-Meneses JS, Palacios-Espinosa X und Palacios-Sánchez L., veröffentlicht in Iatreia 35(3), Seite 341-8 (DOI: https://doi.org/10.17533/udea.iatreia.154).
The Transport Layer Security (TLS) protocol is a cornerstone of secure network communication, not only for online banking, e-commerce, and social media, but also for industrial communication and cyber-physical systems. Unfortunately, implementing TLS correctly is very challenging, as becomes evident by considering the high frequency of bugfixes filed for many TLS implementations. Given the high significance of TLS, advancing the quality of implementations is a sustained pursuit. We strive to support these efforts by presenting a novel, response-distribution guided fuzzing algorithm for differential testing of black-box TLS implementations. Our algorithm generates highly diverse and mostly-valid TLS stimulation messages, which evoke more behavioral discrepancies in TLS server implementations than other algorithms. We evaluate our algorithm using 37 different TLS implementations and discuss―by means of a case study―how the resulting data allows to assess and improve not only implementations of TLS but also to identify underspecified corner cases. We introduce suspiciousness as a per-implementation metric of anomalous implementation behavior and find that more recent or bug-fixed implementations tend to have a lower suspiciousness score. Our contribution is complementary to existing tools and approaches in the area, and can help reveal implementation flaws and avoid regression. While being presented for TLS, we expect our algorithm's guidance scheme to be applicable and useful also in other contexts. Source code and data is made available for fellow researchers in order to stimulate discussions and invite others to benefit from and advance our work.
In this study the dynamics and stability of thin and electrically conductive aqueous films under the influence of a time-periodic electric field are explored. With the help of analytical linear stability analysis for long wavelength disturbances, the stability threshold of the system as a function of various electrochemical parameters and transport coefficients is presented. The contributions of parameters like surface tension, disjoining pressure, electric double layer (Debye length and interfacial zeta potential), and unsteady Maxwell and viscous stresses are highlighted with the help of appropriate dimensionless groups. The physical mechanisms affecting the stability of thin films are detailed with the above-mentioned forces and parametric dependence of stability trends is discussed.
The present study describes medium-chain-length polyhydroxyalkanoates (mcl-PHAs) production by the Pseudomonas Gl01 strain isolated from mixed microbial communities utilized for PHAs synthesis. A two-step fedbatch fermentation was conducted with glucose and waste rapeseed oil as the main carbon source for obtaining cell growth and mcl-PHAs accumulation, respectively. The results show that the Pseudomonas Gl01 strain is capable of growing and accumulating mcl-PHAs using a waste oily carbon source. The biomass value reached 3.0 g/l of CDW with 20% of PHAs content within 48 h of cultivation. The polymer was purified from lyophilized cells and analyzed by gas chromatography (GC). The results revealed that the monomeric composition of the obtained polyesters depended on the available substrate. When glucose was used in the growth phase, 3-hydroxyundecanoate and 3- hydroxydodecanoate were found in the polymer composition, whereas in the PHAs-accumulating stage, the Pseudomonas Gl01 strain synthesized mcl-PHAs consisting mainly of 3- hydroxyoctanoate and 3-hydroxydecanoate. The transcriptional analysis using reverse-transcription real-time PCR reaction revealed that the phaC1 gene could be transcribed simultaneously to the phaZ gene.
The low cost and small size of MEMS inertial sensors allows their combination into a multi sensor module in order to improve performance. However the different linear accelerations measured on different places on a rotating rigid body have to be considered for the proper fusion of the measurements. The errors in measurement of MEMS inertial sensors include deterministic imperfection, but also random noise. The gain in accuracy of using multiple sensors depends strongly on the correlation between these errors from the different sensors. Although for sensor fusion it usually assumed that the measurement errors of different sensors are uncorrelated, estimation theory shows that for the combination of the same type of sensors actually a negative correlation will be more beneficial. Therefore we describe some important and often neglected considerations for the combination of several sensors and also present some preliminary results with regard to the correlation of measurements from a simple multi sensor setup.