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Institute
To enable smart devices of the internet of things to be connected to a blockchain, a blockchain client needs to run on this hardware. With the Trustless Incentivized Remote Node Network, in short Incubed, it will be possible to establish a decentralized and secure network of remote nodes, which enables trustworthy and fast access to a blockchain for a large number of low-performance IoT devices. Currently, Incubed supports the verification of Ethereum data. To serve a wider audience and more applications this paper proposes the verification of Bitcoin data as well, which can be achieved due to the modularity of Incubed. This paper describes the proof data that is necessary for a client to prove the correctness of a node’s response and the process to verify the response by using this proof data as well. A proof-object which contains the proof data will be part of every response in addition to the actual result. We design, implement and evaluate Bitcoin verification for Incubed. Creation of the proof data for supported methods (on the server-side) and the verification process using this proof data (on the client-side) has been demonstrated. This enables the verification of Bitcoin in Incubed.
The number of Internet of Things (IoT) devices is increasing rapidly. The Trustless Incentivized Remote Node Network, in short IN3 (Incubed), enables trustworthy and fast access to a blockchain for a large number of low-performance IoT devices. Although currently IN3 only supports the verification of Ethereum data, it is not limited to one blockchain due to modularity. This thesis describes the fundamentals, the concept and the implementation of the Bitcoin verification in IN3.
Tokenization projects are currently very present when it comes to new blockchain technologies. After explaining the fundamentals of cross-chain interaction, the bachelor thesis will focus on tokenizing technology for Bitcoin on Ethereum. To get a more practical context, implementing the currently most successful decentralized tokenization project is described.
The financial world of blockchains is mostly covered by Bitcoin, taking up about 210 billion dollars in market cap. Despite the huge security and independence which the technology offers to the users, it's not quite easy to adapt with upcoming applications due to the regulated infrastructure behind. For small-scale transactions, everyday use applications or the access to a variety of crypto technologies and projects, Bitcoin is relatively limited in future development. The compatibility for most of those applications is covering currencies from more development-driven blockchains like Ethereum. Those want to reach out for the user base that's already in hold of Bitcoins and offer them a seamless transition to new applications without the risk of losing their funds. Within the article, atomic swaps and tokenization are covered up and current approaches compared. Both mechanisms are used to fulfill this symbiosis between Bitcoin and Ethereum.
To get a more practical view, an example on how to implement such a tokenization within an app is shown. This will give deeper insights and offers inspiration for digital identity-based app development.
The impact of organisational structure and organisational culture on the efficiency of a business
(2020)
The fear of losing flexibility and effectiveness due to an increased organisational structure induced by personal growth is causing SME's to defer structural changes. The purpose of this work is to examine whether the structural and cultural demands of employees match the structure and predominant culture within such a medium-sized company. As part of this, a survey was made to evaluate the current status and to suggest furthermore where and how changes would make sense to regain or even improve organisational efficiency.
Both cryptocurrency researchers and early adopters of cryptocurrencies agree that they possess a special kind of materiality, based on the laborious productive process of digital ‘mining’ [1]. This idea first appears in the Bitcoin White Paper [2] that encourages Bitcoin adopters to construct and justify its value in metaphoric comparison to gold mining. In
this paper, I explore three material aspects of blockchain: physical infrastructure, human language and computer code. I apply the concept of 'continuous materiality' [3] to show how these three aspects interact in practical implementations of blockchain such as Bitcoin and Ethereum. I start from the concept of ‘digital metallism’ that stands for ‘fundamental value’ of cryptocurrencies, and end with the move of Ethereum to ‘proof-of-stake’, partially as a countermeasure against ‘evil miners’. I conclude that ignoring material aspects of blockchain technology can only further problematize complicated relations between their technical, semiotic and social materiality.
The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses.
While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using
static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious
exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.
The subject of the following paper is the analysis of global company motives for taking on sport sponsorships as a corporate social responsibility (CSR) initiative. This work is compilatory in nature because it is derived from literature released by experts as well as real-life case studies. The expert literature provides a basis of theories and models regarding the fundamental motives for CSR and sport sponsoring and visualizes them by means of statistics and real-life case studies. This paper aims to inform individuals, leaders and specifically global organizations about the benefits that taking on a sport sponsorship may have for fulfilling a company’s CSR objectives
The emerging Internet of Things (IoT) technology interconnects billions of embedded devices with each other. These embedded devices are internet-enabled, which collect, share, and analyze data without any human interventions. The integration of IoT technology into the human environment, such as industries, agriculture, and health sectors, is expected to improve the way of life and businesses. The emerging technology possesses challenges and numerous
security threats. On these grounds, it is a must to strengthen the security of IoT technology to avoid any compromise, which affects human life. In contrast to implementing traditional cryptosystems on IoT devices, an elliptic curve cryptosystem (ECC) is used to meet the limited resources of the devices. ECC is an elliptic curve-based public-key cryptography which provides equivalent security with shorter key size compared to other cryptosystems such as Rivest–Shamir–Adleman (RSA). The security of an ECC hinges on the hardness to solve the elliptic curve discrete logarithm problem (ECDLP). ECC is faster and easier to implement and also consumes less power and bandwidth. ECC is incorporated in internationally recognized standards for lightweight applications due to the
benefits ECC provides.
With the increasing usage of blockchain technology, legal challenges such as GDPR compliance arise. Especially the right of erasure is considered challenging as blockchains are tamperproof by design. Several approaches investigated
possibilities to weaken the tamperproof aspect of blockchains in favor of GDPR compliance. This paper presents several approaches, then focuses on chameleon hash functions by evaluating the possibility to use these specific functions in a private blockchain. The goal of the built system is to take a step towards the digitization of the bill of lading used in international trade. This paper describes the developed software as well as the core considerations around the system such as network design or block structure.
Robust soft learning vector quantization (RSLVQ) is a probabilistic approach of Learning vector quantization (LVQ) algorithm. Basically, the RSLVQ approach describes its functionality with respect to Gaussian mixture model and its cost function is defined in terms of likelihood ratio. Our thesis work involves an approach of modifying standard RSLVQ with non-Gaussian density functions like logistic, lognormal, and Cauchy (referred as PLVQ). In this approach, we derive new update rules for prototypes using gradient of cost function with respect to non-Gaussian density functions. We also derive new learning rules for the model parameters like s and s, by differentiating the cost function with respect to parameters. The main goal of the thesis is to compare the performance results of PLVQ model with Gaussian-RSLVQ model. Therefore, the performance of these classification models have been tested on the Iris and Seeds dataset. To visualize the results of the classification models in an adequate way, the Principal component analysis (PCA) technique has been used.
The Infinica product suite consists of multiple individual microservice applications, mainly gathered around Infinica Process Engine which allows the execution of highly individualised process definitions. For estimating process performance, a layered queuing network approach has been applied. In the first step this required the implementation of a basic modelling framework. Subsequently the implemented framework was used to evaluate the applicability of the approach by creating two models and comparing them with actual performance measurements. Although the calculated results deviated from the expected results, analysis showed that the differences may
derive from an inaccurate model. Nevertheless the general approach seems to be appropriate for the given application as well as for microservices in general, especially when extended with advanced modelling techniques, as the analysed modelled results appear consistent.
A Protein is a large molecule that consists of a vast number of atoms; one can only imagine the complexity of such a molecule. Protein is a series of amino acids that bind to each other to form specific sequences known as peptide chains. Proteins fold into three-dimensional conformations (or so-called protein’s native structure) to perform their functions. However, not every protein folds into a correct structure as a result of mutations occurring in their amino acid sequences. Consequently, this mutation causes many protein misfolding diseases. Protein folding is a severe problem in the biological field. Predicting changes in protein stability free energy in relation to the amino acid mutation (ΔΔG) aids to better comprehend the driving forces underlying how proteins fold to their native structures. Therefore, measuring the difference in Gibbs free energy provides more insight as to how protein folding occurs. Consequently, this knowledge might prove beneficial in designing new drugs to treat protein misfolding related diseases. The protein-energy profile aids in understanding the sequential, structural, and functional relationship, by assigning an energy profile to a protein structure. Additionally, measuring the changes in the protein-energy profile consequent to the mutation (ΔΔE) by using an approach derived from statistical physics will lead us to comprehend the protein structure thoroughly. In this work, we attempt to prove that ΔΔE values will be approximate to ΔΔG values, which can lead the future studies to consider that the energy profile is a good predictor of protein binding affinity as Gibbs free energy to solve the protein folding problem.
Anomaly Detection is a very acute technical problem among various business enterprises. In this thesis a combination of the Growing Neural Gas and the Generalized Matrix Learning Vector Quantization is presented as a solution based on collected theoretical and practical knowledge. The whole network is described and implemented along with references and experimental results. The proposed model is carefully documented and all the further open researching questions are stated for future investigations.
Mathematics Behind the Zcash
(2020)
Among all the new developed cryptocurrencies from Bitcoin, Zcash comes out to be the strongest cryptocurrency providing both transparency and anonymity to the transactions and its users by deploying the strong mathematics of zk-SNARKs.
We discussed the zero knowledge proofs which is a basic building block for providing the functionality to zk-SNARKs. It offers schnorr and sigma protocols with interactive and noninteractive versions. Non-interactive proofs are further used in Zcash transactions where the validation of sent transaction is proved by cryptographic proof.
Further, we deploy zk-SNARKs proofs following common reference string as public parameter when transaction is made. The proof allows sender to prove that she knows a secret for an instance such that the proof is succinct, can be verified very efficiently and does not leak the
secret. Non-malleability, small proofs and very effective verification make zk-SNARKs a classic tool in Zcash. Since we deal with NP problems therefore we have considered the elliptic curve cryptography to provide the same security like RSA but with smaller parameter size.
Lastly, we explain Zcash transaction process after minting the coin, the corresponding transaction completely hides the sender, receiver and amount of transaction using zero knowledge proof.
As future considerations, we talk about the improvements that can be done in term of decentralization, efficiency by comparing with top ranked cryptocurrencies namely Ethereum and Monero, privacy preserving against the thread of quantum computers and enhancements in shielded transactions.
Mathematics behind the Zcash
(2020)
Among all the new developed cryptocurrencies, Zcash comes out to be the strongest cryptocurrency providing both transparency and anonymity to the transactions and its users by deploying the strong mathematics of zk-SNARKs. We discussed the zero knowledge proofs as a building block for providing the functionality to zk-SNARKs. It offers schnorr protocol which is further used in Zcash transactions where the validation of sent transaction is proved by cryptographic proof. Further, we deploy zk-SNARKs following common reference string that allows sender to prove that she knows a secret such that the proof is succinct, can be verified and does not leak the secret. Non-malleability, small proofs and effective verification make zk-SNARKs a classic tool in Zcash. We deal with NP problems therefore we have considered the elliptic curve cryptography to provide the security. Lastly, we explain Zcash transaction, the corresponding transaction completely hides the sender, receiver and amount of transaction using zero knowledge proof.
In bioinformatics one important task is to distinguish between native and mirror protein models based on the structural information. This information can be obtained from the atomic coordinates of the protein backbone. This thesis tackles the problem of distinction of these conformations, looking at the statistics of the dihedral angles’ distribution regarding the protein backbone. This distribution is visualized in Ramachandran plots. By means of an interpretable machine learning classification method – Generalized Matrix Learning Vector Quantization – we are able to distinguish between native and mirror protein models with high accuracy. Further, the classifier model supplies supplementary information on the important distributional regions for distinction, like α-helices and β-strands.
Vicia faba leaves and calli were transformed using CRISPR Cas RNP. Two kinds of CPP fused SpyCas9 were used with sgRNA7, sgRNA5 or sgRNA13 targeting PDS exon 1, PDS exon 2 or MgCh exon 3 respectively. RNP were applied using high pressure spraying, biolistic delivery, incubation in RNP solution and infiltration of leaf tissue. A PCR and restriction enzyme based approach was used for detection of mutation. Screening of 679 E. coli colonies containing the cloned fragments resulted in detection of 14 mutations. Most of the 14 mutations were deletions of sizes 150, 500 or 730 bp. 5 out of the 14 mutations were point mutations located two to three bp upstream of PAM.
Glycans play an important role in the intracellular interactions of pathogenic bacteria. Pathogenic bacteria possess binding proteins capable of recognizing certain sugar motifs on other cells, which are found in glycan structures. Artificial carbohydrate synthesis allows scientists to recreate those sugar motifs in a rational, precise, and pure form. However, due to the high specificity of sugar-binding proteins, known as lectins, to glycan structures, methods for identifying suitable binding agents need to be developed. To tackle this hurdle, the Fraunhofer Institute for Cell Therapy and Immunology (Fraunhofer IZI) and the Max-Planck Institute of Colloids and Interfaces (MPIKG) developed a binding assay for the high throughput testing of sugar motifs that are presented on modular scaffolds formed by the assembly of four DNA strands into simple, branched DNA nanostructures. The first generation of this assay was used in combination with bacteria that express a fluorescent protein as a proof-of-concept. Here, the assay was optimized to be used with bacteria not possessing a marker gene for a fluorescent protein by staining their genomic DNA with SYBR® Green. For the binding assay, DNA nanostructures were combined with artificially synthesized mannose polymers, typical targets for many lectins on the surface of bacteria, presenting them in a defined constellation to bind bacteria strongly due to multivalent cooperativity. The testing of multiple mannose polymers identified monomeric mannose with a 5’-carbon linker and 1,2-linked dimeric mannose with linker as the best binding candidates for E. coli, presumably due to binding with the FimH protein on the surface. Despite similarities between the FimH proteins of E. coli and K. pneumoniae, binding was only observed between E. coli and the different sugar molecules on DNA structures. Furthermore, the degree of free movement seemed to affect the binding of mannose polymers to targeted proteins, since when utilizing a more flexible DNA nanostructure, an increase in binding could be observed. An alternative to the simple DNA nanostructures described above is the use of larger, more complex DNA origami structures consisting of several hundred strands. DNA origami structures are capable of carrying dozens of modifications at the same time. The results for the DNA origami structure showed a successful functionalization with up to 71 1,2-linked dimeric mannose with linker molecules. These results point towards a solution for the high-throughput analysis of potential binding agents for pathogenic bacteria e.g. as an alternative treatment for antibiotic-resistant.