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Prototype-based Vector Quantization is one of the key methods in data processing like data compression or interpretable classification learning. Prototype vectors serve as references for data and data classes. The data are given as vectors representing objects by numerical features. Famous approaches are the Neural Gas Vector Quantizer (NGVQ) for data compression and Learning Vector Quantizers (LVQ) for classification tasks. Frequently, training of those models is time consuming. In the contribution we discuss modifications of these algorithms adopting ideas from quantum computing. The aim for this is a least twofold: First quantum computing provides ideas for enormous speedup making use of quantum mechanical systems and inherent parallelization.
Second, considering data and prototype vectors in terms of quantum systems, implicit data processing is performed, which frequently results in better data separation. We will highlight respective ideas and difficulties when equipping vector quantizers with quantum computing features.
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.
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.
Over recent years, Maximal Extractable Value (MEV) has gained significant importance within the decentralized finance (DeFi) ecosystem. Remarkably, within just two years of its emergence, MEV has seen an extraction of approximately 600 million USD - a phenomenon that has sparked concerns regarding potential threats to blockchain stability.
With growing interest in the Ethereum network and the growing DeFi sector, research surrounding MEV has substantially increased. This work aims to offer a comprehensive understanding of MEV. Additionally, this research quantifies the largest types of MEV (Arbitrage, Sandwich and Liquidations) from March 2022 to March 2023. The data are then compared to other sources, revealing a general upward trend, with a particularly noticeable increase in Sandwich Attacks.
Decentralizing Smart Energy Markets - tamper-proof-documentation of flexibility market processes
(2020)
The evolving granularity and structural decentralization of the energy system leads to a need for new tools for the efficient operation of electricity grids. Local Flexibility Markets (or "Smart Markets") provide platform concepts for market based congestion management. In this context there is a distinct need for a secure, reliable and tamper-resistant market design which requires transparent and independent monitoring of platform operation. Within the following paper different concepts for blockchain-based documentation of relevant processes on the proposed market platform are described. On this basis potential technical realizations are discussed. Finally, the implementation of one setup using Merkle tree operations is presented by using open source libraries.
Sensor fusion is an important and crucial topic in many industrial applications. One of the challenging problems is to find an appropriate sensor combination for the dedicated application or to weight their information adequately. In our contribution, we focus on the application of the sensor fusion concept together with the reference to the distance-based learning for object classification purposes. The developed machine learning model has a bi-functional architecture, which learns on the one side the discrimination of the data regarding their classes and, on the other side, the importance of the single signals, i.e., the contribution of each sensor to the decision. We show that the resulting bi-functional model is interpretative, sparse, and simple to integrate in many standard artificial neural networks.