724 research outputs found
The Question of Spontaneous Wing Oscillations : Determination of Critical Velocity Through Flight-oscillation Tests
Determination of the spontaneous oscillations of a wing or tail unit entail many difficulties, both the mathematical determination and the determination by static wing oscillation tests being far from successful and flight tests involving very great risks. The present paper gives a method developed at the Junkers Airplane Company by which the critical velocity with respect to spontaneous oscillations of increasing amplitude can be ascertained in flight tests without undue risks, the oscillation of the surface being obtained in the tests by the application of an external force
Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT
Recently, generative AIs like ChatGPT have become available to the wide
public. These tools can for instance be used by students to generate essays or
whole theses. But how does a teacher know whether a text is written by a
student or an AI? In our work, we explore traditional and new features to (1)
detect text generated by AI from scratch and (2) text rephrased by AI. Since we
found that classification is more difficult when the AI has been instructed to
create the text in a way that a human would not recognize that it was generated
by an AI, we also investigate this more advanced case. For our experiments, we
produced a new text corpus covering 10 school topics. Our best systems to
classify basic and advanced human-generated/AI-generated texts have F1-scores
of over 96%. Our best systems for classifying basic and advanced
human-generated/AI-rephrased texts have F1-scores of more than 78%. The systems
use a combination of perplexity, semantic, list lookup, error-based,
readability, AI feedback, and text vector features. Our results show that the
new features substantially help to improve the performance of many classifiers.
Our best basic text rephrasing detection system even outperforms GPTZero by
183.8% relative in F1-score
Exploring ChatGPT's Empathic Abilities
Empathy is often understood as the ability to share and understand another
individual's state of mind or emotion. With the increasing use of chatbots in
various domains, e.g., children seeking help with homework, individuals looking
for medical advice, and people using the chatbot as a daily source of everyday
companionship, the importance of empathy in human-computer interaction has
become more apparent. Therefore, our study investigates the extent to which
ChatGPT based on GPT-3.5 can exhibit empathetic responses and emotional
expressions. We analyzed the following three aspects: (1) understanding and
expressing emotions, (2) parallel emotional response, and (3) empathic
personality. Thus, we not only evaluate ChatGPT on various empathy aspects and
compare it with human behavior but also show a possible way to analyze the
empathy of chatbots in general. Our results show, that in 91.7% of the cases,
ChatGPT was able to correctly identify emotions and produces appropriate
answers. In conversations, ChatGPT reacted with a parallel emotion in 70.7% of
cases. The empathic capabilities of ChatGPT were evaluated using a set of five
questionnaires covering different aspects of empathy. Even though the results
show, that the scores of ChatGPT are still worse than the average of healthy
humans, it scores better than people who have been diagnosed with Asperger
syndrome / high-functioning autism
Diacritization as a machine translation problem and as a sequence labeling problem
In this paper we describe and compare two techniques for the automatic diacritization of Arabic text: First, we treat diacritization as a monotone machine translation problem, proposing and evaluating several translation and language models, including word and character-based models separately and combined as well as a model which uses statistical machine translation (SMT) to post-edit a rule-based diacritization system. Then we explore a more traditional view of diacritization as a sequence labeling problem, and propose a solution using conditional random fields (Lafferty et al., 2001). All these techniques are compared through word error rate and diacritization error rate both in terms of full diacritization and ignoring vowel endings. The empirical experiments showed that the machine translation approaches perform better than the sequence labeling approaches concerning the error rates
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