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7 results for “Apologies”
The Sincere Apology Corpus (SinA-C)
<p>This repository contains the Sincere Apology Corpus (SinA-C). SinA-C is an English speech corpus of acted apologies in various prosodic styles created with the purpose of investigating the attributes of the human voice which convey sincerity.</p> <p>Thirty-two speakers were recorded in a studio at the Columbia Computer Music Center inside a sound-proof recording booth. Audio was recorded with an AKG C414 dynamic microphone. The digital audio workstation Logic Pro 9 was used to collect the audio signals. Recordings were captured at 44.1 kHz and 16 bit in AIFF format and later converted to mono WAV files.</p> <p><strong>Speakers </strong></p> <ul> <li>Gender: 15 male and 17 female</li> <li>Age: 20-60 years old (mean: 29.8 years; std; 9.9 years)</li> <li>Background: 27 American born English native speakers, and 5 from other nationalities (all fluent in spoken English). 24 speakers were professional actors, and the remaining 12 were artists</li> </ul> <p><strong>Recordings</strong></p> <p>Speakers were given a description of the study, a set of 6 sentences (apologies; see Table 1) and a short definition for a set of 4 prosodic styles (see Table 2) to adopt when uttering each sentence (the recordings are not spontaneous, but rather acted). The sentences used were the following:</p> <ul> <li><em>Sorry.</em></li> <li><em>I am sorry for everything I have done to you. </em></li> <li><em>I cannot tell you how sorry I am for everything I did</em>.</li> <li><em>Please allow me to apologise for everything I did to you. I was inappropriate and lacked respect</em>.</li> <li><em>It was never my intention to offend you, for this I am very sorry. </em></li> <li><em>I am sorry but I am going to have to decline your generous offer. Thank you for considering me.</em></li> </ul> <p>The prosodic styles intended to be adopted when uttering each of the sentences were:</p> <ul> <li>monotonic;</li> <li>pitch prominence (labelled as `Stress');</li> <li>fast speaking rate;</li> <li>slow speaking rate.</li> </ul> <p><strong>Annotations</strong></p> <p>The SinA-C audio recordings were labelled in terms of the sincerity perceived by listeners (`<em>How sincere was the apology you just heard?</em>') on a 5-point Likert scale ranging from 0 (Not Sincere) to 4 (Very Sincere) by 22 volunteers (13 male and 9 female; age range: 18-22; μ 19.5 std 1.0). Of the 22 annotators, all reported to have normal hearing, and all were English speakers (6 reported to be bilingual with at least one other language).</p> <p>Raw annotations were standardised to zero mean and unit standard deviation on a per-subject basis in order to eliminate potential individual rating biases. We then computed the mean across all subjects for each utterance. This resulted in a set of ratings ranging from [-1.51, 1.72] (mean -0.002 std 0.60) which are used as the gold-standard for regression experiments. We also converted these ratings to binary labels. Average ratings larger than 0 were labelled as `Sincere` (S), and those smaller of equal to 0 were labelled as `Not Sincere` (NS). This resulted in 478 instances labelled as S and 438 as NS. These labels are the gold-standard for classification tasks.</p> <p><strong>SinA-C Baseline</strong></p> <p>The Baseline for the dataset is described in detail in the INTERSPEECH 2019 publication "Sincerity in Acted Speech: Presenting the Sincere Apology Corpus and Results" [1]. This article presents both classification and regression baseline results. The modelling experiments included both a 3-fold Speaker Independent Nest Cross Validation (SICV) schema as well as Speaker Independent folds (C-SIF) (train, validation, test). C-SIF is provided by the original database baseline from the INTERSPEECH 2016 COMputation PARalinguistics challengE (COMPARE) [2]. For reproducibility, speaker distributions across the two partitioning strategies are provided with the corpus package.</p> <p>The audio descriptors include conventional and state-of-the-art features extracted from the audio files. We used Support Vector Machines (SVM) for classification tests and linear Support Vector Regression (SVR) for the regression ones. In both cases we used linear kernels and both SVM and SVR were implemented using the open-source machine learning toolkit Scikit-Learn. During the development phase, we trained various models (using the training set) with different complexity parameters (C ∈ 10-7, 10-6, 10-5, 10-4, 10-3, 10-2, 10-1, 1), and evaluated their performance on the validation set. After determining the optimal value for C, we concatenated the training and validation sets, re-trained the model with this enlarged training set, and evaluated the performance on the test set. Further detail on the baseline development are given in [1].</p> <p><strong>Comments</strong></p> <p>SinA-C was initially gathered between 2015-2016 at the Columbia University Computer Music Centre (CCMC) in New York City, United States of America. The dataset was also included in the INTERSPEECH 2016 COMPARE challenge [2], and prior to that in 2015 a subset of the dataset was also exhibited as part of a graduate-school art exhibition.</p> <p><strong>Citing this corpus</strong></p> <p>When using the data set for your own research, and within publications, please cite this repository and [1].</p> <p><strong>Bibliography</strong></p> <p>[1] Baird, A., Coutinho, E., Hirschberg, J., & Schuller, B. W. (2019). Sincerity in Acted Speech: Presenting the Sincere Apology Corpus and Results. In <em>Interspeech</em> <em>2019</em>, in press.</p> <p>[2] Schuller, B. W., Steidl, S., Batliner, A., Hirschberg, J., Burgoon, J. K., Baird, A., Elkins, A. C., Zhang, Y., Coutinho, E., & Evanini, K. (2016). The INTERSPEECH 2016 Computational Paralinguistics Challenge: Deception, Sincerity & Native Language. In <em>Interspeech 2016</em>, 2001-2005.</p>
1,237 Annotated Developer Apologies from GitHub
<p><em><strong>Software Developer Apologies</strong></em></p> <p>This dataset contains 1,237 GitHub comments with apology annotations (apology vs not apology), released as part of the following publication:</p> <ul> <li>Benjamin S. Meyers. <a href="https://scholarworks.rit.edu/theses/11609/">Human Error Assessment in Software Engineering.</a> Rochester Institute of Technology. 2023. </li> </ul> <p><em><strong>Included Files</strong></em></p> <p>The "github_apologies.csv" file contains the full dataset of 1,237 GitHub comments with apology annotations. In total, there are 365 comments containing an apology (872 non apologies). The comments themselves are a subset of those included in <a href="../records/5603093">88.6 Million Developer Comments from GitHub</a>.</p> <p><em><strong>Annotation Details</strong></em></p> <p>Full details are provided in the above publication. We implemented a naive classifier (Precision: 41.7%, Recall: 99.7%, F1: 86.9%, Accuracy: 91.1%) using counts of apology lemmas. 91% of developer comments containing at least one apology lemma matched our manual annotations. Agreement between raters was almost perfect (Cohen's Kappa = 0.94).</p> <p><em><strong>CSV Fields</strong></em></p> <ul> <li><strong>ID</strong>: Unique identifier for the comment.</li> <li><strong>SOURCE</strong>: Whether this comment originates from a commit, issue, or pull request.</li> <li><strong>COMMENT_URL</strong>: The URL linking to the comment.</li> <li><strong>COMMENT_TEXT</strong>: The raw comment text.</li> <li><strong>NUM_APOLOGY_LEMMAS</strong>: The count of apology lemmas present in the comment.</li> <li><strong>CLASSIFIER_LABEL</strong>: The automatically assigned label ("Apology" or "Not Apology").</li> <li><strong>RATER_1_LABEL</strong>: The manually assigned label ("Apology" or "Not Apology") from Rater 1.</li> <li><strong>RATER_2_LABEL</strong>: The manually assigned label ("Apology" or "Not Apology") from Rater 2.</li> <li><strong>AGREED_LABEL</strong>: The agreed upon label ("Apology" or "Not Apology") after Rater 1 and Rater 12 resolved disagreements.</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
The data of Emoji Use, Empathy, Attribution of Responsibility, and Forgiveness in Apologies
<p>This is the data for paper: Can Emoji Promote Forgiveness? The Relationship between Emoji Use, Empathy, Attribution of Responsibility, and Forgiveness in Apologies. A total of 323 participants were recruited in that study, and a recall method (Study 1) and scenario simulation method (Study 2) were used to explore the effect of emoji use during apologies on forgiveness, and the mediating role of empathy and attribution of responsibility. The results showed that (a) people chose emoji that resembled real remorseful facial expressions when apologizing; (b) using emoji that expressed remorse when apologizing could promote forgiveness; and (c) empathy mediated the process of emoji promoting forgiveness, while attribution of responsibility did not play a mediating role.</p>
Emergency Medicine: Life-Saving Strategies and Techniques - Prehospital Emergency Care, Triage Systems, Advanced Card Apologies for the premature response.
<p><span>Emergency medicine is a critical field that plays a pivotal role in saving lives and providing essential care during life-threatening situations. This paper aims to explore the key components of emergency medicine, with a focus on prehospital emergency care, triage systems, and advanced cardiac apologies. Through an in-depth analysis of current practices, strategies, and techniques, this paper provides valuable insights into the life-saving measures employed in emergency medicine.</span></p>
Data from: Moral licensing, instrumental apology and insincerity aversion: taking Immanuel Kant to the lab
Open the record for dataset details and reuse information.
Do Apologies Promote the Reintegration of Former Combatants? Lessons from a Video Experiment in Colombia
Open the record for dataset details and reuse information.
Sorry Not Sorry: Apologizing and Its Effect on Discomfort During Dressing Removal
ClinicalTrials.gov study NCT03216629. IPD Sharing: NO. Countries: 1. Publications: 0.
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