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If we believe Edward Snowden, encryption is "the only true protection against surveillance". However, advances in quantum technology might endanger this safeguard. Our article discusses why quantum computing poses a threat to data security and what to do about it. Instead of a purely theoretical analysis, we build on code examples using Python, C, and Linux.
There are numerous articles about the programming languages most commonly used by hackers. Among them, however, there are hardly any scientific studies. One reason might be that hackers mainly operate anonymously and are difficult to reach. This paper aims to shed light on this interesting and relevant research question. In order to find answers, we conducted a survey among the members of the German Chaos Computer Club. As one of the world's largest organisations for information security and hacking, the club provides a good basis for our study. We examine the question of which programming languages are used by hackers as well as the importance of the programming language for their work. The paper offers first insights into the topic and can provide a starting point for further research.
Data engineering makes up a large part of the data science process. In CRISP-DM this process stage is called "data preparation". It comprises tasks such as data ingestion, data transformation and data quality assurance. In our article we solve typical data engineering tasks using ChatGPT and Python. By doing so, we explore the link between data engineering and the new discipline of prompt engineering.
The Collatz conjecture is an unsolved number theory problem. We approach the question by examining the divisions by two that are performed within Collatz sequences. Aside from classical mathematical methods, we use techniques of data science. Based on the analysis of 10,000 sequences we show that the number of divisions by two lies within clear boundaries. Building on the results, we develop and prove an equation to calculate the maximum possible number of divisions by two for any given a Collatz sequence. Whenever this maximum is reached, a sequence leads to the result one, as conjectured by Lothar Collatz. Furthermore, we show how many divisions by two are required for a cycle of a specific length. The findings are valuable for further investigations and could form the basis for a comprehensive proof of the conjecture.
The Collatz conjecture is an unsolved number theory problem. We approach the question by examining the divisions by two that are performed within Collatz sequences. Aside from classical mathematical methods, we use techniques of data science. Based on the analysis of 10,000 sequences we show that the number of divisions by two lies within clear boundaries. Building on the results, we formulate and prove several theorems on the occurrence of cycles and the termination of Collatz sequences. The findings are useful for further investigations and could form the basis for a comprehensive proof of the conjecture.
In 2021, together with fellow scientists, we conducted a survey at the German Chaos Computer Club (CCC). Our goal was to find out which programming languages are most commonly used by hackers. This article follows up the survey and compares its findings with an analysis of the Exploit Database. Readers get step-by-step instructions on how to set up the analysis environment and a summary of the results. Thus, the article not only covers applied techniques, but also offers insights into the world of cybersecurity.
Diese Arbeit falsifiziert den Laplaceschen Dämon mit Hilfe eines Computerspiels. Das Spiel ist in der Programmiersprache C++ implementiert und basiert auf deterministischen Regeln. Es führt auf dem verarbeitenden Rechner zu einem probabilistischen Ergebnis. Geht man davon aus, dass der Laplacesche Dämon seinen schnellsten Rechner zur Ausführung des Spiels nutzt, oder es selbst ausführt, kann er die Ergebnisse nicht zu jedem Zeitpunkt vorhersagen. Da der Laplacesche Dämon dadurch definiert ist, dass er die Zukunft vollständig vorhersehen kann, ist er durch das Spiel widerlegt.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions. This second version includes adjustments resulting from
the first iteration of the review.