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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.
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.
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.
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.
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 for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.
Programming by Demonstration (PbD) is a method to program robots through the performance of a task by humans. Most implementations are online methods that use visual or force feedback of the demonstrator. However, we developed an offline programming approach for PbD with a special input device within an Augmented Reality Environment. Therefore, this paper aims to answer how the characteristics and functionality of the end effector of a jointed-arm robot can be represented by a haptic input device in order to perform PbD. The PbD process is first carried out on a digital twin of the robot, visualized to the user in real physical space by means of augmented reality technology. The programming of the digital twin can later be transferred to the real robot. The haptic input device in this context is the main part of the Cyber-Physical-System (CPS), which enables the user to interact with the virtual robot. Therefore, the specification of the mechanical and software components of the CPS is of main importance. Within this paper, strategies for the implementation of shape and function abstraction, as well as for ensuring communication, have been worked out. The physical shape of the CPS is kept generic and is only subject to ergonomic restrictions. However, Augmented Reality overlays the physical shape with an exact digital image of the end effector used later in the process. Nevertheless, the physical characteristics of the real robot should be represented as real as possible by the CPS. Therefore, the CPS is equipped with various sensors and actuators. With the CPS it is possible to determine contact forces and to manipulate objects to a certain extent in order to teach gripping strategies to the digital twin. An operating system was developed for communication and control of the electronic components. For the validation of the functionality of the CPS an exemplary PbD process was developed, the results were analyzed and evaluated.
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.
The main idea of this paper is to present a framework for an easy and intuitive program generation for human-robot collaboration implementations. This framework consists of three key ideas, which make up the three main sections. The first section is about automated task allocation with focus on economics. The second part is about intuitive robotic teaching with an offline motion capture (MoCap) tool and the third section features an automated program generation of the paths and the human-robot collaborative aspects of the application. With this framework, we provide a method to program human-robot collaborative (HRC) applications for future industry purposes.
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.
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.