Notes

Evaluating reasons as well as sexual category as elements in college students' sights of nonmedical doctor prescribed catalyst utilize.
Duration time of the COVID-19 spread was reduced from 356 days to 232 days between later and earlier interventions. We observed that delaying intervention for 1 month caused the maximum number of cumulative cases reduce by -166.89 times that of earlier complete intervention, and the number of deaths increased from 53,560 to 8,938,725. Earlier and complete intervention is necessary to stem the tide of COVID-19 infection.In the Thematic Apperception Test, a picture story exercise (TAT/PSE; Heckhausen, 1963), it is assumed that unconscious motives can be detected in the text someone is telling about pictures shown in the test. Therefore, this text is classified by trained experts regarding evaluation rules. We tried to automate this coding and used a recurrent neuronal network (RNN) because of the sequential input data. There are two different cell types to improve recurrent neural networks regarding long-term dependencies in sequential input data long-short-term-memory cells (LSTMs) and gated-recurrent units (GRUs). Some results indicate that GRUs can outperform LSTMs; others show the opposite. So the question remains when to use GRU or LSTM cells. The results show (N = 18000 data, 10-fold cross-validated) that the GRUs outperform LSTMs (accuracy = .85 vs. .82) for overall motive coding. Further analysis showed that GRUs have higher specificity (true negative rate) and learn better less prevalent content. LSTMs have higher sensitivity (true positive rate) and learn better high prevalent content. A closer look at a picture x category matrix reveals that LSTMs outperform GRUs only where deep context understanding is important. As these both techniques do not clearly present a major advantage over one another in the domain investigated here, an interesting topic for future work is to develop a method that combines their strengths.We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing speech of non-native American-English speakers to a collection of native American-English speakers. An acoustic-only measure is valuable as it does not require the time-consuming and error-prone process of phonetically transcribing speech samples which is necessary for current edit distance-based approaches. We minimize speaker variability in the data set by employing speaker-based cepstral mean and variance normalization, and compute word-based acoustic distances using the dynamic time warping algorithm. Our results indicate a strong correlation of r = -0.71 (p less then 0.0001) between the acoustic distances and human judgments of native-likeness provided by more than 1,100 native American-English raters. Therefore, the convenient acoustic measure performs only slightly lower than the state-of-the-art transcription-based performance of r = -0.77. We also report the results of several small experiments which show that the acoustic measure is not only sensitive to segmental differences, but also to intonational differences and durational differences. However, it is not immune to unwanted differences caused by using a different recording device.Recent advances in access to spoken-language corpora and development of speech processing tools have made possible the performance of "large-scale" phonetic and sociolinguistic research. This study illustrates the usefulness of such a large-scale approach-using data from multiple corpora across a range of English dialects, collected, and analyzed with the SPADE project-to examine how the pre-consonantal Voicing Effect (longer vowels before voiced than voiceless obstruents, in e.g., bead vs. beat) is realized in spontaneous speech, and varies across dialects and individual speakers. Compared with previous reports of controlled laboratory speech, the Voicing Effect was found to be substantially smaller in spontaneous speech, but still influenced by the expected range of phonetic factors. Dialects of English differed substantially from each other in the size of the Voicing Effect, whilst individual speakers varied little relative to their particular dialect. This study demonstrates the value of large-scale phonetic research as a means of developing our understanding of the structure of speech variability, and illustrates how large-scale studies, such as those carried out within SPADE, can be applied to other questions in phonetic and sociolinguistic research.Allowing machines to choose whether to kill humans would be devastating for world peace and security. Zelavespib nmr But how do we equip machines with the ability to learn ethical or even moral choices? In this study, we show that applying machine learning to human texts can extract deontological ethical reasoning about "right" and "wrong" conduct. We create a template list of prompts and responses, such as "Should I [action]?", "Is it okay to [action]?", etc. with corresponding answers of "Yes/no, I should (not)." and "Yes/no, it is (not)." The model's bias score is the difference between the model's score of the positive response ("Yes, I should") and that of the negative response ("No, I should not"). For a given choice, the model's overall bias score is the mean of the bias scores of all question/answer templates paired with that choice. Specifically, the resulting model, called the Moral Choice Machine (MCM), calculates the bias score on a sentence level using embeddings of the Universal Sentence Encoder since the moral value of an action to be taken depends on its context. It is objectionable to kill living beings, but it is fine to kill time. It is essential to eat, yet one might not eat dirt. It is important to spread information, yet one should not spread misinformation. Our results indicate that text corpora contain recoverable and accurate imprints of our social, ethical and moral choices, even with context information. Actually, training the Moral Choice Machine on different temporal news and book corpora from the year 1510 to 2008/2009 demonstrate the evolution of moral and ethical choices over different time periods for both atomic actions and actions with context information. By training it on different cultural sources such as the Bible and the constitution of different countries, the dynamics of moral choices in culture, including technology are revealed. That is the fact that moral biases can be extracted, quantified, tracked, and compared across cultures and over time.
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