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Ein Einsatzfeld Künstlicher Intelligenz im Bereich der Sozialen Arbeit ist die automatische Inhaltsanalyse von Konversationen aus der psychosozialen Onlineberatung. Mögliche Anwendungen sind die statistische Untersuchung von Wirkzusammenhängen sowie die Entwicklung KI-basierter Dialog-und Unterstützungssysteme. Die Grundlage für das Training entsprechender Modelle sind aussagekräftige und realistische Trainingsdaten. Im Rahmen des Projektes GeCCo (German e-Counseling Conversation Dataset) wurde ein erster deutschsprachiger Datensatz für die Analyse psychosozialer Beratungskonversationen erstellt und veröffentlicht. Wesentlicher Bestandteil ist ein neu entwickeltes Kategoriensystem, das eine feingranulare inhaltliche Klassifikation in 40 Berater-und 28 Klienten-Kategorien ermöglicht. Basierend auf dem Datensatz konnten erfolgreich verschiedene Modelle mithilfe maschineller Lernverfahren trainiert und für die wissenschaftliche Nutzung bereitgestellt werden.
The illumination of registration plates poses challenges for lighting technology. To achieve the wide and steep angles required for homogeneous illumination of the license plate, optics are needed to refract and reflect light. In this work, registration plate lamps with different components are designed. After the construction, the concepts are tested for their limits in terms of position and manufacturability. The
entire work is carried out at the company CREAT GmbH in Ingolstadt. All developments in the field of registration plate lighting are subject to the UNECE and country-specific regulations, which must be considered in the development of the lights.[1] Some vehicles currently available on the automotive market are measured to classify and compare the different lamp systems. From the average values, the lamps are designed. Adjustments are repeatedly made to the lighting device to achieve the most
homogeneous illumination of the registration plate. As soon as the lights meet the legal requirements, the geometric limits of the lighting device are defined in relation to the plate. As there are different polymers to produce optics, these are simulated with their refractive indices and tested for their effects on the light image.
Recent flood events (FE) in Germany have shown that the extent and impact of extreme flood events cannot be estimated solely based on numerical models. For analyzing the development of such an event and to develop and implement safety measures more efficiently, additional data must be collected during the event. Within the scope of this research, the possibilities of near real-time recording using an unmanned aerial vehicle (UAV) and data processing with the Structure from Motion (SfM) method were tested in a case study. Different recording parameter combinations were tested in the Laufer
Muehle area on the Aisch river in Germany. The focus of the investigations was the identification of a parameter combination that allows a short recording interval for aerial imagery. Based on these findings, the identification of changes in the study area by comparing multitemporal photography (flood prevention), as well as the recording of flooded areas during a FE should be possible. The accuracy analysis of the different parameter combinations between two point clouds as well as the process of change detection was done by a Multiscale Model to Model Cloud Comparison (M3C2) and
including ground control points. As a result, a parameter combination was identified which led to the desired results in the study area. The processes were transformed into fully automated and scripted workflows. The results serve as a basis for establishing a workflow for near real-time analyses in future studies.
Diese Studienarbeit untersucht den Stromverbrauch auf regionaler Ebene in Deutschland und vergleicht drei verschiedene Systemdesigns für erneuerbare Energien. Die Systeme basieren auf dem Einsatz von geotechnischen, chemischen und Batteriespeicher-technologien. Es werden reale Wetter- und Lastdaten für einen Zeitraum von drei Jahren vom 01.09.2020 bis zum 31.08.2023 verwendet.
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.
In diesem Band werden die Beiträge der ersten Konferenz „Interdisziplinäre Lehre für nachhaltige Entwicklung“ an der Technischen Hochschule Nürnberg Georg Simon Ohm aus dem Jahr 2023 präsentiert. Die inhaltliche Vielfalt der 13 Beiträge verdeutlicht, dass die Diskussion der Interdisziplinarität in der hochschulgebundenen Ausbildung junger Menschen für die sich transformierende Gesellschaft gerade erst begonnen hat. Vorgestellt werden unterschiedlich wissenschaftlich verankerte und praxisorientierte Modelle zur Vermittlung notwendiger Kompetenzen, um gesellschaftliche Zukunft zu gestalten. Es gilt am Ball zu bleiben, den Austausch fortzusetzen und Chancen für interdisziplinäre Kooperationen für die Bildung für nachhaltige Entwicklung (BNE) zu identifizieren und zu ergreifen. Die Open Source Publikation soll in diesem Sinne Anschlusskommunikation ermöglichen.
Decision making is an intrinsic and complex aspect of social work practice, requiring consideration of diverse but connected aspects. Decisions are often required as to whether a situation requires protective state intervention or whether it reaches the criteria for public or charitable services. Such instances of deciding whether or not a situation is ‘on one side of the line or the other’ are referred to in this article as ‘threshold judgements’. This article draws on experiences and material from a range of social work contexts to explore generalisable theory-informed understandings of ‘threshold judgements’ and ‘threshold decisions’ to develop knowledge and skills on this topic. The article outlines signal detection theory and evidence accumulation (‘tipping point’) theory and discusses these as ways to understand the key concepts underpinning threshold decisions in social work. We then argue that although these threshold concepts are a necessary part of decision making in social work, as in many other aspects of life, they are not sufficient. Operationalising such decisions requires some form of sense-making. Naturalistic decision making and heuristic models of judgement are discussed as frameworks for practice which seem to be useful in this context.
Demographic changes have led to an increase in older people in prisons. Whereas the rehabilitative process of younger offenders is geared towards their reintegration into the labour market, successful ageing should be a policy aim for older prisoners. This study explores how older incarcerated persons view their ageing. A qualitative study using a written survey with only the single question What does ageing in prison mean to you? was conducted in Bavaria, Germany. A total of 64 prisoners (61 male, 3 female) supplied answers varying in length from a few words to several pages. The thematic analysis revealed that together with health concerns, social relations and everyday activities, the uncertainty of the future was a central focus point for the older adults in prison. The authors propose that a positive vision of the future needs to be included in any model of successful ageing. If successful ageing is used as an aim for older prisoners, more attention needs to be paid to support interventions during and after the release process.
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.