Refine
Year of publication
Document Type
- Article (613)
- Conference Proceeding (184)
- Bachelor Thesis (156)
- Report (110)
- Working Paper (85)
- Book (76)
- Master's Thesis (68)
- Part of a Book (43)
- Periodical Part (39)
- Study Thesis (26)
Language
- German (891)
- English (565)
- Multiple languages (2)
Is part of the Bibliography
- no (1458)
Keywords
- Bibliothek (48)
- Digitalisierung (39)
- Milchwirtschaft (36)
- Euterentzündung (31)
- E-Learning (28)
- Molkerei (27)
- Computersicherheit (24)
- Ausbildung (23)
- Informationsmanagement (22)
- Dickdarmkrebs (20)
Automated question generation holds great promise in many fields, such as education, to reduce the workload and automate an otherwise tedious task. However, major challenges remain regarding the quality of generated questions. To identify and address these challenges generated questions are evaluated either automatically or manually. While several automated metrics, mostly based on the comparison with a gold standard, exist, their usefulness is limited and human evaluation is often used for more accurate assessment. Our research generates questions using several models and methods, including fine-tuning, zero-shot and few-shot. We compare model performance by classifying the generated questions using a multi-label approach. This approach evaluates by sorting generated questions into zero or more binary problem classes and attempting to identify different problems with the generated questions. Our results show that different models tend to generate questions that fit into different problem classes. Additionally, the problem classification evaluation is capable of recognizing these differences and weighing the classes for the models accordingly, creating model-specific distribution characteristics.
Generative AI is not Magic
(2024)
Although the capabilities of large language models are astonishing and exceed the expectations most researchers had about the potential of language modeling, these models are not complete black boxes, and the principles of how these systems work are not too difficult to understand. We show what theories, methods and skills can contribute to a basic understanding of large language models and how these can fit into a curriculum for students with and without a strong background in mathematics and computer science.
Although machine learning (ML) for intrusion detection is attracting research, its deployment in practice has proven difficult. Major hindrances are that training a classifier requires training data with attack samples, and that trained models are bound to a specific network.
To overcome these problems, we propose two new methods for anomaly-based intrusion detection. Both are trained on normal-only data, making deployment much easier. The first approach is based on One-class SVMs, while the second leverages our novel Cellwise Estimator algorithm, which is based on multidimensional OLAP cubes. The latter has the additional benefit of explainable output, in contrast to many ML methods like neural networks. The created models capture the normal behavior of a network and are used to find anomalies that point to attacks. We present a thorough evaluation using benchmark data and a comparison to related approaches showing that our approach is competitive.
International business management students are trained to become future international business managers, necessitating a thorough education including current and relevant information to be able to live up to their professional responsibilities. This study evaluates the quality of international business management textbooks in imparting knowledge about strategic alliances, a critical aspect of international business management. Nineteen textbooks were examined using a two-step approach: first, a theoretical framework was established based on academic papers and specialized literature; second, the relevant sections of the textbooks were identified and coded into a self-developed deductive-inductive category system. Through evaluative qualitative content analysis, a framework was developed to assess the alignment of textbook content with current, correct, and relevant information on strategic alliances. The findings reveal that while international business management textbooks generally provide information slightly below medium alignment with the current state of research, they exhibit variability in quality across different categories. Furthermore, some textbooks excel, while others perform poorly, indicating disparity in content quality. This study underscores the importance of ensuring that educational materials adequately prepare future international business managers with up-to-date and accurate information on strategic alliances, so they can make well-considered research-based decisions.
This paper introduces a method for analysing motion patterns that can be utilised to optimise data-driven systems. The aim is to use surveillance cameras and artificial intelligence to track multiple objects in a reliable manner, thereby preserving the authenticity of movement patterns for numerous and similar objects. In a case study, this method is applied to optimize lighting conditions in animal husbandry. Furthermore, this approach can be utilized not only in animal husbandry but also in other domains.
Background: Falls are a common problem experienced by people living with HIV yet predictive models specific to this population remain underdeveloped. We aimed to identify, assess and stratify the predictive strength of various physiological, behavioral, and HIV-specific factors associated with falls among people living with HIV and inform a predictive model for fall prevention.
Methods: Systematic review and meta-analysis were conducted to explore predictors of falls in people living with HIV. Data was sourced, screened, extracted, and analyzed by two independent reviewers from eight databases up to January 2nd, 2024, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Evidence quality and bias were assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and the Mixed Method Appraisal Tool (MMAT), respectively. Pooled odds ratios (OR) with 95% confidence intervals (CI) were computed using random-effects models to establish associations between predictors and falls risk. We applied established criteria (Bradford Hill’s criteria, Rothman’s and Nweke’s viewpoints) to stratify risk factors and create a weighted predictive algorithm.
Results: This review included 12 studies on falls/balance dysfunction in 117,638 participants (54,513 people living with HIV), with varying ages (45–50 years), sample sizes (32 − 26,373), study durations (6 months to 15 years), disease stages (CD4 + counts 347.2 cells/mm³ to ≥ 500 cells/µL) and fall definitions (self-reported histories to real-time reporting). Some predictors of falls in people living with HIV including depression, cannabis use, cognitive impairment/neurocognitive adverse effects (NCAE), hypertension, and stavudine—showed perfect risk responsiveness (Ri = 1), indicating their strong association with falls. Notably, cannabis use demonstrated the highest risk weight (Rw = 3.0, p < 0.05, 95%CI:1.51–5.82), followed by NCAE (Rw = 2.3, p < 0.05, 95%CI:1.66–3.21) and frailty with a broad confidence interval (Rw = 2.2, p < 0.05, 95%CI:0.73–14.40). Other significant predictors included hypertension (Rw = 1.8, p < 0.05, 95%CI:1.33–2.33), depression (Rw = 1.6, p < 0.05, 95%CI:1.22–2.18), stavudine use (Rw = 1.5, p < 0.05, 95%CI: 0.95–2.25), neuropathy (Rw = 1.3, p < 0.05, 95%CI:1.26–2.11), and polypharmacy (Rw = 1.2, p < 0.05, 95%CI:1.16–1.96). The fall risk threshold score was 12.8, representing the 76th percentile of the specific and sufficient risk weight.
Conclusion: Our meta-analysis identifies predictors of falls in people living with HIV, emphasizing physiological, behavioral, and HIV-specific factors. Integrating these into clinical practice could mitigate falls-related sequelae. We propose a novel approach to falls risk prediction using a novel clinical index, resulting in a HIV-specific falls risk assessment tool.
Late blowing is a prevalent and costly cheese defect caused by clostridia. In organic cheese production, the use of additives that inhibit the growth of clostridia is prohibited. Furthermore, mechanical methods for the removal of clostridia are impractical in organic dairies due to the small batch sizes involved and separation process temperatures (~55 °C) that are incompatible with the standards required for raw milk cheese production. The aim of this study was to investigate whether sufficient spore reduction can be achieved at lower temperatures (10, 35 °C) with a downsized separator (CSC18-01-077, GEA Westfalia) by varying the process parameters to describe the influence on the suitability of the treated milk for cheese production. In addition to spore reduction, total mesophilic bacteria count, the effects of separation on fat and casein losses, and damage to milk fat globules were assessed, as they can affect the yield and cheese quality. A significant reduction (p < 0.01) in spore concentration and total bacteria count in milk was achieved, regardless of the process parameters employed. Casein losses are reduced at 35 °C compared to 55 °C. The extent of fat loss in the sludge at 35 °C was minimal. The reduction in milk fat globule size was significant. Nonetheless, the results of this study demonstrate that a downsized centrifuge can be employed to augment the quality of small-batch raw milk cheese, particularly at a temperature of 35 °C.
This paper highlights the significance of AI-powered maintenance strategies in modern industry for operational optimization and reduced downtime. It emphasizes the crucial role of sensor data analysis in identifying anomalies and predicting failures. The research specifically examines sensor data from an automotive press shop, addressing questions related to data selection, collection challenges, and knowledge generation. By utilizing unsupervised learning on compressed air data from a press line, the study identifies patterns, anomalies, and correlations. The results offer insights into the potential for implementing an effective predictive maintenance strategy. Additionally, a systematic literature review underscores the importance of data analysis in production systems, particularly in the context of maintenance.
Seit 2023 bietet die Singapore Management University (SMU) die Weiterbildung „Emerging Library Leaders’ Summer School for Asia-Pacific“ an. Es handelt sich hierbei um eine fünftägige Weiterbildung für Bibliothekare am Anfang oder in der Mitte ihrer Karriere, wobei auch Fachangestellte für Medien- und Informationsdienste und Bibliotheksassistenten zugelassen werden. Obwohl sich die Summer School vorrangig an Beschäftigte in Asien richtet, ist der Veranstalter grundsätzlich bereit, Plätze an Interessierte aus Europa zu vergeben.
Context: Cultural aspects are of high importance as they guide people’s behaviour and thus, influence how people apply methods and act in projects. In recent years, software engineering research emphasized the need to analyze the challenges of specific cultural characteristics. Investigating the influence of cultural characteristics is challenging due to the multi-faceted concept of culture. People’s behaviour, their beliefs and underlying values are shaped by different layers of culture, e.g., regions, organizations, or groups. In this study, we focus on agile methods, which are agile approaches that focus on underlying values, collaboration and communication. Thus, cultural and social aspects are of high importance for their successful use in practice.
Objective: In this paper, we address challenges that arise when using the model of cultural dimensions by Hofstede to characterize specific cultural values. This model is often used when discussing cultural influences in software engineering.
Method: As a basis, we conducted an exploratory, multiple case study, consisting of two cases in Japan and two in Germany.
Contributions: In this study, we observed that cultural characteristics of the participants differed significantly from cultural characteristics that would typically be expected for people from the respective country. This drives our conclusion that for studies in empirical software engineering that address cultural factors, a case-specific analysis of the characteristics is needed.
