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Institute
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.
This thesis aims to research the platform YouTube and whether “being a YouTuber” qualifies as a profession or not and what leads to this. The author combines existing scientific data and information provided by YouTubers doing this as a job and uses the compilation method. The author merges that material and uses it to create a bachelor thesis that covers both the theoretical and practical approach. The aim was to find out if there is a success recipe that can be followed that leads to views and clicks which are essential for the profession as a YouTuber. To do this, the author created two channels to see how the factors mentioned in this thesis are applied and if the approach leads to success. The findings of this thesis showed, that although the profession of a YouTuber can be classified as a job, it needs to be viewed differently from commonly known and in society accepted careers. Becoming a YouTuber and making money from this business, therefore, cannot be guaranteed.
As economies are getting more and more interconnected, the importance of the global logistics sector grew accordingly. However, both structural challenges and current events lead to recent supply chain disruptions, exposing the vulnerabilities of the sector. Simultaneously, blockchain has emerged as a key innovative technology with use cases going far beyond the exchange of virtual currencies. This paper aims to analyze how the technology is transforming global logistics and its challenges. Therefore, six use cases, are presented to give an overview of the technological possibilities of blockchain and smart contracts. The analysis combines theoretical approaches from scientific journals and combines them with findings from real-world implementations. The paper finds that the technology can change supply chain design fundamentally, with processes and decisions being automated and power within supply chain structures changing. However, implementations also face technological, environmental, and organizational challenges that need to be solved for wide-spread adoption.
Sequences are an important data structure in molecular biology, but unfortunately it is difficult for most machine learning algorithms to handle them, as they rely on vectorial data. Recent approaches include methods that rely on proximity data, such as median and relational Learning Vector Quantization. However, many of them are limited in the size of the data they are able to handle. A standard method to generate vectorial features for sequence data does not exist yet. Consequently, a way to make sequence data accessible to preferably interpretable machine learning algorithms needs to be found. This thesis will therefore investigate a new approach called the Sensor Response Principle, which is being adapted to protein sequences. Accordingly, sequence similarity is measured via pairwise sequence alignments with different sequence alignment algorithms and various substitution matrices. The measurements are then used as input for learning with the Generalized Learning Vector Quantization algorithm. A special focus lies on sequence length variability as it is suspected to affect the sequence alignment score and therefore the discriminative quality of the generated feature vectors. Specific datasets were generated from the Pfam protein family database to address this question. Further, the impact of the number of references and choice of substitution matrices is examined.
More than 10 years after the invention of Bitcoin, the underlying blockchain technology is having an increasing effect on today’s society. Although one of the most popular application areas of blockchain is still the field of cryptocurrencies, the technological concepts are crossing into further application domains such as international supply chains. Fast-changing markets, high costs of time and risk management as well as biased relationships between the actors pose big challenges to an appropriate supply chain management. Based on a case study about sensor tracking, this paper explores the potential impact of blockchain on small and medium enterprises within an international supply chain. We will show that blockchain technologies offers a high potential to reduce inequalities of power relations between involved actors within supply chains. To achieve this, the requirements for the use of blockchain in supply chain management will be analyzed by means of a conducted case study and an expert survey of the companies concerned.
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.
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.
Influenza A viruses are responsible for the outbreak of epidemics as well as pandemics worldwide. The surface protein neuraminidase of this virus is responsible, among other things, for the release of virions from the cell and is thus of interest in pharmacological research. The aim of this work is to gain knowledge about evolutionary changes in sequences of influenza A neuraminidase through different methods. First, EVcouplings is used with the goal of identifying evolutionary couplings within the protein sequences, but this analysis was unsuccessful. This is probably due to the great sequence length of neuraminidase. Second, the natural vector method will be used for sequence embedding purposes, in hopes to visualize sequential progression of the virus protein over time. Last, interpretable machine learning methods will be applied to examine if the data is classifiable by the different years and to gain information if the extracted information conform to the results from the EVcouplings analysis. Additionally to using the class label year, other labels such as groups or subtypes are used in classification with varying results. For balanced classes the machine learning models performed adequately, but this was not the case for imbalanced data. Groups and subtypes can be classified with a high accuracy, which was not the case for the years, continents or hosts. To identify the minimal number of features necessary for linear separation of neuraminidase group 1 subtypes, a logistic regression was performed at last, resulting in the identification of 15 combinations of nine amino acid frequencies. Since the sequence embedding as well as the machine learning methods did not show neuraminidase evolution over time, further research is necessary, for example with focus on one subtype with balanced data.
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.
Digital Power of Attorney catalyzed by Software Requirements for Blockchain-based Applications
(2022)
Blockchain Technology (BT) with so-called web3 is at an inflection point between new sub-theme hypes and world-wide industrialization over last three years thanks to large companies like MicroStrategy [1], Facebook [2] and several Venture-Capital formations [3] who are already fighting over market share and community growth. Our work represents insights from Literature-based Software Requirement (SR) elicitation for a specific Blockchain-based Application, which is creation, managing and control of digital Power of Attorney (POA). The context of POA is not only a financial driven use-case it is by far a heavy weight universal legal transaction. We use a morphological box and reduced PRIMS-P to synthesis a generic specification for further Blockchain-based Application development. Formulated SRs in POA context are reflected on our core actors which are Grantor and authorized, trusted, external Entities. Proposed characteristics for relationship and effects are visualized in a reference model originally used in digital platform ecosystems [4]. This design and modelling approach facilitated closing discussion of BT and its future eCommerce perspective.
Current research in identity management is focusing on decentralized trust establishment for distributed identities. One of these decentralized trust models is Self-Sovereign Identities (SSI). With SSI each entity should be able to independently present and manage provable information about itself as well as request and review evidence from other entities. Using a distributed blockchain, information for verifying the authenticity of this evidence can be obtained from any other entity. This concept can be used not only for people, but also for authentication and authorization during the life cycle of devices in the Internet of Things (IoT). This paper presents an SSI-based concept for authentication and authorization of IoT devices among each other, intended to contribute to the change in trust on the internet. The SSI methodology employing a blockchain offers the possibility to establish mutual trust and proof of ownership without relying on any third party. The paper describes the concept, offers a reference implementation, and gives a discussion of the approach.
Pollinating insects are of vital importance for the ecosystem and their drastic decline imposes severe consequences for the environment and humankind. The comprehension of their interaction networks is the first step in order to preserve these highly complex systems. For that purpose, the following study describes a protocol for the investigation of honey bee pollen samples from different agro-environmental areas by DNA extraction, PCR amplification and nanopore sequencing of the barcode regions rbcL and ITS. It was shown, that the most abundant species were classified consistently by both DNA barcodes, while species richness was enhanced by single-barcode detection of less abundant species. The analysis of the the different landscape variables exhibited a decline of species richness, Shannon diversity index, and species evenness with increasing organic crop area. However, sampling was only carried out in August and further investigations are suggested to display a more complete picture of honey bee foraging throughout the seasons.
Classification label security determines the extent to which predicted labels from classification results can be trusted. The uncertainty surrounding classification labels is resolved by the security to which the classification is made. Therefore, classification label security is very significant for decision-making whenever we are encountered with a classification task. This thesis investigates the determination of the classification label security by utilizing fuzzy probabilistic assignments of Fuzzy c-means. The investigation is accompanied by implementation, experimentation, visualization and documentation of the results.
Dynamic object roles and corresponding contexts can model complex applications with higher-level abstraction. These abstracted applications can be used in wider areas such as financial institutions, health care, and supply chain network. Role management which consists of the creation of role objects, and binding role object between core objects still suffers from non-intrusive logging-monitoring, auditing, and resilient data source for role-based applications. Moreover, immutable smart contracts cause problems concerning bug fixing and maintenance without dynamic binding to new smart contract objects. An object that is created from a smart contract (contract class) can be transparently attached to a role object utilizing the Role Object Pattern (ROP). However, ROP itself does not contain a context definition and context-specific role assignment grouping the definition of smart contract relationships in abstracted data types. In this study, we would like to implement an extended version of the role object pattern called Context-based Role Object Pattern (ContextROP) with an onchain smart contract language called Solidity to solve fundamental problems. To evaluate the proposal, we will implement a use case with the design pattern proceeding with qualitative and quantitative analysis.
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.
Simulating complex physical systems involves solving nonlinear partial differential equations (PDEs), which can be very expensive. Generative Adversarial Networks (GAN) has recently been used to generate solutions to PDEs-governed complex systems without having to numerically solve them.
However, concerns are raised that the standard GAN system cannot capture some important physical and statistical properties of a complex PDE-governed system, along side with other concerns for difficult and unstable training, the noisy appearance of generated samples and lack of robust assessment methods of the sample quality apart from visual examination. In this thesis, a standard GAN system is trained on a data set of Heat transfer images. We show that the generated data set can capture the true distribution of training data with respect to both visual and statistical properties, specifically the vertical statistical profile. Furthermore, we construct a GAN model which can be conditioned using variance-induced class label. We show that the variance threshold t = 0. 01 constructs a good conditional class label, such that the generated images achieve 96% accuracy
rate in complying with the given conditions.
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
This master thesis covers the topics of Studying customers’ behavior on the example of skin care brand Nivea. There are presented theoretical basis for the following research about marketing, customers’ behavior and conducting marketing research properly. Then, there is the analysis of German market. Since Nivea is the brand of Beiersdorf company, there is a description of Beiersdorf’s activity and operation work. The main idea of the paper work is to analyze customers’ behavior of Nivea. Therefore, the work contains huge research about the brand along with its’ micro- and macroenvironment. There also were conducted an in-depth interview and a survey to understand customers’
current needs. With all the results the author of the work proposed some ideas for Nivea brand.
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.
The aim of this bachelor thesis is to find out how the use of artificial intelligence, specifically the one used in combat situations, can increase the playing time or even the replay value of games in the action role-playing genre. Thereby, it focuses mainly on combat situations between a player and an artificial intelligence.
To begin with, this bachelor thesis examines the action role-playing genre in order to find a suitable definition for it. Accordingly, action role-playing games involve titles that send the player on a hero’s journey-like adventure in which they must prove their skills in combat against virtual opponents. The greatest challenge of these real-time battles comes from the required quick reflexes, skill queries and hand-eye coordination.
Next, six means of increasing the replayability of a game are explored: Experience and Nostalgia, Variety and Randomness, Goals and Completion, Difficulty, Learning, and Social Aspect. The paper then proceeds to give an explanation for the term Artificial Intelligence and examines the various methods used to create intelligent behavior as well as the general advancement of the research field. Special attention is given to the implementation methods of Finite State Machines and Behavior Trees, as they are the most widely used methods for creating behavioral patterns of virtual characters.
Finally, a study conducted as part of the bachelor thesis is described, which compares a mathematically balanced artificial intelligence with a behaviorally balanced one in terms of game performance regarding the willingness of test subjects to purchase and play through the game as well as its replay value. The thesis concludes with the findings that while the behavioral approach is more promising than the mathematical approach, a combination of the two methods ultimately leads to the best outcome. Furthermore, the study shows that the use of artificial intelligence to individualize gaming experiences is promising for the future of the gaming industry.
Embeddings for Product Data
(2022)
The E-commerce industry has grown exponentially in the last decade, with giants like Amazon, eBay, Aliexpress, and Walmart selling billions of products. Machine learning techniques can be used within the e-commerce domain to improve the overall customer journey on a platform and increase sales. Product data, in specific, can be used for various applications, such as product similarity, clustering, recommendation, and price estimation. For data from these products to be used for such applications, we have to perform feature engineering. The idea is to transform these products into feature vectors before training a machine learning model on them. In this thesis, we propose an approach to create representations for heterogeneous product data from Unite’s platform in the form of structured tabular records. These tables consist of attributes having different information ranging from product-ids to long descriptions. Our model combines popular deep learning approaches used in natural language processing to create numerical representations, which contain mostly non-zeros elements in an array or matrix called as dense representation for all products. To evaluate the quality of these feature vectors, we validate how well the similarities between products are captured by these dense representations. The evaluations are further divided into two categories. The first category directly compares the similarities between individual products. On the other hand, the second category uses these dense vectors in any of the above- mentioned applications as inputs. It then evaluates the quality of these dense representation vectors based on the accuracy or performance of the defined application. As result, we explain the impact of different steps within our model on the quality of these learned representations.
This scientific work reveals the potential for the development of the renewable energy market, due to many reasons. The reasons are the unstable political situation in the world, rising energy prices, environmental degradation and the growing demand of Ger man residents for government measures to reduce the negative impact on the environment. This work is related to business planning and development using strategies based on the above reasons. The purpose of the study is to develop methods for successfully regulating the market for renewable resources to solve the problem of environmental pollution through the promotion of environmentally friendly products. The work explores the driving forces and problems hindering the development of the market for renewable resources. The problems raised concerned all interested parties, from consumers and producers to the state body for regulating and stimulating the industry . An analysis was also made of the methods of environmentally oriented companies and the tools they use to strengthen their positions in the market. Based on the data obtained from the conducted research, a concept and business strategy for a new environmentally oriented generation” was created. The business consulting company “Sun’s idea of the new company is to involve all parties using marketing tools, creating a healthy competitive environment among commercial companies and benefiting not only the companies themselves but also the end user of the products and the German government.
The topic of soulbound, non-transferable tokens is getting lots of interest within the blockchain space lately as decentralized societies become more tangible with Web3 social media applications and DAOs. In this article, I want to outline how such tokens function, their problems for adoption and standardization, and how they differ from verifiable credentials in the SSI field. As such soulbound assets will likely rely on extended recovery and asset management schemes to become viable identities that safely gain reputation and trust, features like social recovery and contract-based accounting are incorporated. By combining those new technologies and the theoretical crypto-native identity construct, the paper will give an impression of the future user-centric data economy.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.
Where does the cocoa, which we consume on a regular basis, come from? Supply chains are not always transparent, much less easily comprehensible. The cocoa industry faces ongoing challenges. Whether it be the chocolate manufacturers’ promise to maintain a sustainable and ethical supply chain, the minimal impact on the environment or the maximum adherence to human rights in their production process. This paper revises important steps which lead to the compliance with UN standards and questions the role of consumers in the construct of ethical chocolate products.
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.
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [12]. In many tasks of classification, different variants of LVQ are considered while training a model. In this thesis, the two variants of LVQ, Generalized Matrix Learning Vector Quantization(GMLVQ) and Generalized Tangent Learning Vector Quantization(GTLVQ) have been discussed. And later, transfer learning technique for different variants of LVQ has been implemented, visualized and we have compared the results using different datasets.
Fermat proposed fermat’s little theorem in 1640, but a proof was not officially published until 1736. In this thesis paper, we mainly focus on different proofs of fermat’s little theorem like combinatorial proof by counting necklaces, multinomial proofs, proof by modular arithmetic, dynamical systems proof, group theory proof etc. We also concentrate on the generalizations of fermat’s little theorem given by Euler and Laplace. Euler was the first scientist to prove the fermat’s little theorem. We will also go through three different proofs given by Euler for fermat’s little theorem. This theorem has many applications in the field of mathematics and cryptography. We focus on applications of fermat’s little theorem in cryptography like primality testing and publickey cryptography. Primality test is used to determine if the given number n is a prime number or composite number. In this paper, we also concentrate on fermat primality test and Miller-Rabin primality test, which is an extension of fermat primality test. We also discuss the most widely used public-key cryptosystem i.e, the RSA Algorithm, named after its developers R. Rivest, A. Shamir, and L. Adleman. The algorithm was invented in 1978 and depends heavily on fermat’s little theorem.
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.
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.
With the growing market of cryptocurrencies, blockchain is becoming central to various research areas relevant from a mathematical and cryptographic point of view. Moreover, it is capable of transforming the traditional methods involving centralized network operations into decentralized peer-to-peer functionalities. At the same time, it provides an alternative to digital payments in a robust and tamperproof manner by adding the element of cryptography, consequently making it traversable for an individual who is a part of the blockchain network. Furthermore, for a blockchain to be optimal and efficient, it must handle the blockchain trilemma of security, decentralization, and scalability constraints in an effective manner. Algorand, a blockchain cryptocurrency protocol intended to solve blockchain’s trilemma, has been studied and discussed. It is a permissionless (public) blockchain protocol and uses pure proof of stake as its consensus mechanism.
Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.
Probabilistic Micropayments
(2022)
Probabilistic micropayments are important cryptography research topics in electronic commerce. The Probabilistic micropayments have the potential to be researched in order to obtain efficient algorithms with low transaction costs and high speeding computer power. To delve into the topic, it is vital to scrutinize the cryptographic preliminaries such as hash functions and digital signatures. This thesis investigates the important probabilistic methods based on a centralized or decentralized network. Firstly, centralized networks such as lottery-based tickets, Payword, coin-flipping, and MR2 are described, and an approach based on blind signatures is also discussed. Then, decentralized network methods such as MICROPAY3, a transferable scheme on the blockchain network, along with an efficient model for cryptocurrencies, are explained. Then we compare the different probabilistic micropayment methods by improving their drawback with a new technique. To set the results from the theoretical analysis of different methods into some context, we analyze the attacks that reduce the security and, therefore, the system’s efficiency. Particularly, we discuss various methods for detecting double-spending and eclipse attacks occurrence