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Institute
As part of the research project Trusted Blockchains for the Open, Smart Energy Grid of the Future (tbiEnergy), one of the objectives is to investigate how a holistic blockchain approach for the realization of a local energy market could be accomplished and how corresponding hardware security mechanisms can be integrated. This paper provides an overview of the implemented prototype and describes the system and its processes.
Humans started using the principles of insurance thousands of years ago when they lived in tribes in smaller villages. If one of the tribe members were injured, the others would take care of him and his family. The basic principle of insurance is several people covering each other against a particular risk. Today, most people in regions like Europe have access to insurance, while many people worldwide still have no access at all. The cost and accessibility may be improved with a blockchain-based parametric approach. The insurance process in a parametric approach is exclusively based on data, and decisions are made objectively. Blockchain is a necessary and integral part of the approach to create transparency and connect the customer’s and investor’s risk capital. The paper offers an overview of the opportunities and challenges of blockchain-based parametric insurance, a catalog of criteria for such insurance, a description of all components and their interaction for implementation on Ethereum, and a reference implementation of a train delay insurance in Germany.
Due to the intractability of the Discrete Logarithm Problem (DLP), it has been widely used in the field of cryptography and the security of several cryptosystems is based on the hardness of computation of DLP. In this paper, we start with the topics on Number Theory and Abstract Algebra as it will enable one to study the nature of discrete logarithms in a comprehensive way, and then, we concentrate on the application and computation of discrete logarithms. Application of discrete logarithms such as Diffie Hellman key exchange, ElGamal signature scheme, and several attacks over the DLP such as Baby-step Giant-step method, Silver Pohlig Hellman algorithm, etc have been analyzed. We also focus on the elliptic curve along with the discrete logarithm over the elliptic curve. Attacks for the elliptic curve discrete logarithm problem, ECDLP have been discussed. Moreover, the extension of several discrete logarithms-based protocols over the elliptic curve such as the elliptic curve digital signature algorithm, ECDSA have been discussed also.
This scientific work deals with the current opportunities of business development. Purpose of the work is study and analysis of the organization's development strategy and its development. The subject of the study is the mechanism of formation of an organization's development strategy, understanding of business development and its core methodologies and branches. This thesis is based on the operations of the real engineering company and main part of the research could be applied in reality. Main goal of the thesis is to find recommendations on the implementation of strategic changes organization's development strategy.
In this thesis, we focus on using machine learning to automate manual or rule-based processes for the deduplication task of the data integration process in an enterprise customer experience program. We study the underlying theoretical foundations of the most widely used machine learning algorithms, including logistic regression, random forests, extreme gradient boosting trees, support vector machines, and generalized matrix learning vector quantization. We then apply those algorithms to a real, private data set and use standard evaluation metrics for classification, such as confusion matrix, precision, and recall, area under the precision-recall curve, and area under the Receiver Operating Characteristic curve to compare their performances and results.
Applications and Potential Impacts of Blockchain Technology in Logistics and Supply Chain Areas
(2022)
The motive of the present thesis is to analyze the applications and potential impacts of blockchain technology in the logistics and supply chain areas. For this purpose, the literature from different sources has been used to analyze and get an overview of the current status and role of blockchain technology within the logistics and supply chain areas. Different use cases, as well as pilot projects from organizations all over the world and also from Germany, have been included. Suggestions for further applications and implementations of blockchain technology along with their potential impacts have been made. Additionally, the cost of implementing blockchain-based solutions and applications has been estimated along with providing recommendations and suggestions for important and key points to be considered before preparing and deciding to implement blockchain-based solutions in any organization.
Noise in the oceans is a constantly increasing factor. The growing industrialisation due to shipping, offshore wind parks, seismic studies and other anthropogenic noise is putting the eco system under immense stress. The focus of this thesis is on the assessment of continuous underwater noise from ships. Based on existing strategies in air as well as underwater and a comparison of both an alternative strategy for the assessment of con-tinuous noise from ships is given. The concept developed is based on published, scien-tifically observed responses of animals to ship passes with an indication of an effect range. A model is created to describe the strategy using publicly available data for cargo ships as an example. The results are summarized in maps depicting the affected area for an MRU of the OSPAR II region and the MPA “Borkum Riffgrund”. The strategy is discussed and evaluated on the basis of these results. From this, further improvements and the need for additional information in publicly available data on vessel traffic are derived.
Differentiation is ubiquitous in the field of mathematics and especially in the field of Machine learning for calculations in gradient-based models. Calculating gradients might be complex and require handling multiple variables. Supervised Learning Vector Quantization models, which are used for classification tasks, also use the Stochastic Gradient Descent method for optimizing their cost functions. There are various methods to calculate these gradients or derivatives, namely Manual Differentiation, Numeric Differentiation, Symbolic Differentiation, and Automatic Differentiation. In this thesis, we evaluate each of the methods mentioned earlier for calculating derivatives and also compare the use of these methods for the variants of Generalized Learning Vector Quantization algorithms.
In the past few years Generative models have become an interesting topic in the field of Machine Learning (ML). Variational Autoencoder (VAE) is one of the popular frameworks of generative models based on the work of D.P Kingma and M. Welling [6] [7]. As an alternative to VAE the authors in [12] proposed and implemented Information Theoretic Learning (ITL) based Autoencoder. VAE and ITL Autoencoder are a combination of the neural networks and probabilistic graphical models (PGM) [7]. In modern statistics it is difficult to compute the approximation ofthe probability densities. In this paper we make use of Variational Inference (VI) technique from machine learning that approximate the distributions through optimization. The closeness between the distributions are measured by the information theoretic divergence measures such as Kullbach-Liebler, Euclidean and Cauchy Schwarz divergences. In this thesis, we study theoretical and experimental results of two different frameworks of generative models which generate images of MNIST handwritten characters [8] and Yale face database B [3]. The results obtained show that the proposed VAE and ITL Autoencoder are capable of generating the underlying structure of the example datasets
Studying and understanding the metabolism of plants is essential to better adapt them to future climate conditions. Computational models of plant metabolism can guide this process by providing a platform for fast and resource-saving in silico analyses. The reconstruction of these models can follow kinetic or stoichiometric approaches with Flux Balance Analysis being one of the most common one for stoichiometric models. Advances in metabolic modelling over the years include the increasing number of compartments, the automation of the reconstruction process, the modelling of plant-environment interactions and genetic variants or temporally and spatially resolved models. In addition, there is a growing focus on introducing synthetic pathways in plants to increase their agricultural potential regarding yield, growth and nutritional value. One example is the β-hydroxyaspartate cycle (BHAC) to bypass photorespiration. After the implementation in a stoichiometric C3 plant model, in silico flux analyses can help to understand the resulting metabolic changes. When comparing with in vivo experiments with BHAC plants, the metabolic model can reproduce most results with exceptions regarding growth and oxaloacetate. To evaluate whether the BHAC is suitable to establish a synthetic C4 cycle, the pathway is implemented in a two-cell type model that is capable of running a C4 cycle. The results show that the BHAC is only beneficial under light limitation in the bundle sheath cell. An additional engineering target for improved performance of plants is malate synthase. This work serves as the basis for further analyses combining the different factors boosting the advantages of the BHAC and for in vivo experiments in C3 and C4 plants.