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1
rief, linda. "Student Voices: We Are Doing Our Best, Sam, to Listen and Learn." Voices from the Middle 25, no. 1 (September 1, 2017): 95–97. http://dx.doi.org/10.58680/vm201729289.
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Linda Rief crafts this column, writing alongside her middle school students, to show the beauty and possibilities that lie within the words our students use to make sense of their world. In this issue, the Bowtie Boys (a group of 8th, 9th, and 10th graders) talk with Rief about advice they would give to teachers.
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Banzhaf, W. H. "Commentary: Actions Speak Louder than Words." Journal of Forestry 99, no. 5 (May 1, 2001): 1. http://dx.doi.org/10.1093/jof/99.5.1.
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Umarova, Saida. "USE OF OCCASIONAL WORDS AND OCCASIONAL PHRASES IN THE WORKS OF GAYBULLAHAS SALAM." INTERNATIONAL JOURNAL OF WORD ART 3, no. 3 (March 30, 2020): 30–34. http://dx.doi.org/10.26739/2181-9297-2020-3-4.
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This article takes into a а соnsiderable proportion of G’aybulla as-Salom for linguistics and his desire about mature and best linguists in our country. Also a lot of defects in linguistc, motion of professor G’aybulla as-Salom about this. The most important problem is this process an appropriate and necessary in Uzbekistan
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Zhou, Xuanxuan, and Yan Hua. "Culture-loaded Words and Translation Equivalence." Theory and Practice in Language Studies 11, no. 2 (February 1, 2021): 210. http://dx.doi.org/10.17507/tpls.1102.14.
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Language and culture have very close relationship and can’t dispense with each other. In translation practice, we must fully consider the cultural differences, and completely understand the meaning of the culture-loaded words, try our best to convey the information as much as possible, and realize translation equivalence and cultural exchange.
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Vianney, Jjukira, Immaculate Nakitende, Henry Kalema, Sylivia Namuleme, and John Kellett. "What words best capture the symptom of breathlessness in Uganda?" South Sudan Medical Journal 16, no. 1 (February 27, 2023): 16–19. http://dx.doi.org/10.4314/ssmj.v16i1.4.
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Introduction: Anecdotal experience suggests that Ugandan patients complain infrequently of breathlessness. The Luganda language, spoken in Uganda, does not have one word to express breathlessness but uses various phrases. Therefore, many of these patients may not be able to express what they feel when they are breathless by a single English word. Our aim was to determine the frequency and severity of the symptoms captured by a Luganda translation of the Dyspnea-12 (D12) questionnaire, a well validated measure of different dimensions of breathlessness.
Method: All alert non-pregnant adult patients fluent in Luganda who presented to the hospital’s combined outpatient and emergency department were asked to complete the D12 questionnaire.
Results: Out of 466 patients, 137 (29.4%) had at least one D12 symptom. Patients’ D12 responses expressed as a numerical severity score was only weakly associated with a respiratory rate >20 bpm. Two questions (“My breathing requires more work” and “My breathing is uncomfortable”) identified >90% of patients likely to be breathless.
Conclusion: For patients whose first language is Luganda a translation of the D12 questionnaire captures the symptom of dyspnoea.
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Syed Razi Muhammad. "Best Evidence Medical Education." JMMC 2, no. 1 (December 2, 2011): 1–2. http://dx.doi.org/10.62118/jmmc.v2i1.407.
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Best evidence Medical Practice (BEMP) has given much needed direction to medical practice in the last quarter of a century. I vividly remember that during my education in Dow Medical College in early 80s, and later during my practice in UK in mid 80s and part of mid 90s, we practiced what we saw or sometimes read. In other words, Medical practice was based on fixed opinions. From early 90s, it was obvious that most doctors, especially those working in teaching hospitals, were becoming more sensitive towards the need for evidence before accepting any change in their practice. This was made easy by publication of meta-analyses, review articles, editorials, guidelines and invited lectures on key issues in conferences and seminars. Today we see doctors and researchers seeking best available evidence in decision- making and problem solving. However, unfortunately I see little change in attitudes as far as medical education is concerned. We, as medical teachers still prefer to follow our believes and instincts, seriously follow the myth and stay away from following or even finding best evidence. Something we so eagerly do in our clinical practice and while conducting research. As Petersen has put it in the words of teachers of medicine "I know about medical education. I'm not going, to change."Why do these attitudes persist? What are the barriers to effective, evidence based medical education, and how may they be overcome1?When this problem is highlighted in informal meetings, various explanations are given. These include unlike clinical practice and research that teaching is an art and not a science and hence cannot be evidence driven". However there are research articles regularly published in journals of medical education and Meta analyses being conducted. Here I would like to give some examples od such research articles and meta analysis. I would like to particularly refer to a study conducted in Coventry hospital on final year medical students. It was found that those students who had interactive lectures performed significantly better in solving MCQs than those whose learning of the subject was through game playing2.
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Preston, Samuel H. "My Life in Words and Numbers." Annual Review of Sociology 46, no. 1 (July 30, 2020): 1–17. http://dx.doi.org/10.1146/annurev-soc-110619-031647.
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George Orwell reportedly said that “Autobiography is only to be trusted when it reveals something disgraceful.” I've done my best to eliminate any disgraceful episodes from the following account, thereby compromising its trustworthiness. What's left is a record of someone lucky enough to ( a) find a career as a demographer that was ideally suited to his affinity for writing and math and ( b) find institutions and individuals who vigorously and uncompromisingly supported the search for a better understanding of our social world. I can only hope that I have been equally supportive of students, colleagues, and institutions whose pathways I have crossed.
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Mahdi, Muhammad. "The Best Rules for Knowing the Inferior Hadith." Islamic Sciences Journal 12, no. 4 (March 17, 2023): 278–96. http://dx.doi.org/10.25130/jis.21.12.4.12.
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The inferior hadiths about the Prophet, may Allah’s prayers and peace be upon him, are numerous and not known by every person other than the people of the hadith and its bearers. It is possible to know the inferior hadiths by laying down clear rules taken from what scholars have deduced through their knowledge of the words of our master, the Messenger of Allah, may Allah’s prayers and peace be upon him. The deliberate inferiority on our master, the Messenger of Allah, may Allah bless him and grant him peace, is considered one of the greatest sins. Whoever narrates hadiths made out of negligence, this is a sinner because he is not certain of the hadith, rather he is a participant in the sin of the one who lied our master, the Messenger of Allah, may Allah prayers and peace be upon him.
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Kadriu, Arbana, Lejla Abazi, and Hyrije Abazi. "Albanian Text Classification: Bag of Words Model and Word Analogies." Business Systems Research Journal 10, no. 1 (April 1, 2019): 74–87. http://dx.doi.org/10.2478/bsrj-2019-0006.
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Abstract Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.
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Holtman, Jessica Nation. "Standing in the Way of Our Goals." Texas A&M Law Review 5, no. 3 (April 2018): 563–93. http://dx.doi.org/10.37419/lr.v5.i3.4.
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Currently in Texas, standing options for third-party nonparents seeking to file suits affecting the parent-child relationship (“SAPCRs”) are extremely limited. And, even though the standing options are codified, the evidence necessary to meet the threshold elements may be drastically different depending on the case’s location. These third parties, who have previously exercised parental responsibilities, must make showings to the court that most divorced parents could not make; and this is just for a chance to bring a claim in court. While this seems unfair, and Texas should absolutely resolve the split among its appellate courts, there is one extremely important part that has yet to be mentioned: the child. Standing determinations do not involve a best interest of the child inquiry; this must be changed. This Comment uses a San Antonio Court of Appeals case to highlight both the Texas appellate-court split and the lack of a best interest of the child consideration. Using the case’s facts, this Comment breaks down the general third-party standing option by venue to show just how different the requirements are depending on where in Texas the party resides. In most cases, the outcome spotlights that the child is the true victim of these standing limitations. Professor James Dwyer explains that relationships have a far greater impact on children than on adults. Courts should consider this when determining the best interest of the child. More importantly, the best interest of the child should be a primary consideration, not one that is considered after the plaintiff establishes standing. Allowing a third-party parent to file suit is highly likely to serve the child’s best interest. Once judges apply the best-interest standard at the appropriate stage, judges should then look through the child’s lens for making recommendations on the child’s behalf. Too few judges have said too few words about the lack of the best interest of the child consideration when issuing standing determinations. But with our nation’s evolving familial structure, the time has come to reconsider how courts determine who may bring suits affecting children.
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SCHERRER, YVES, and TOMAŽ ERJAVEC. "Modernising historical Slovene words." Natural Language Engineering 22, no. 6 (August 3, 2015): 881–905. http://dx.doi.org/10.1017/s1351324915000236.
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AbstractWe propose a language-independent word normalisation method and exemplify it on modernising historical Slovene words. Our method relies on character-level statistical machine translation (CSMT) and uses only shallow knowledge. We present relevant data on historical Slovene, consisting of two (partially) manually annotated corpora and the lexicons derived from these corpora, containing historical word–modern word pairs. The two lexicons are disjoint, with one serving as the training set containing 40,000 entries, and the other as a test set with 20,000 entries. The data spans the years 1750–1900, and the lexicons are split into fifty-year slices, with all the experiments carried out separately on the three time periods. We perform two sets of experiments. In the first one – a supervised setting – we build a CSMT system using the lexicon of word pairs as training data. In the second one – an unsupervised setting – we simulate a scenario in which word pairs are not available. We propose a two-step method where we first extract a noisy list of word pairs by matching historical words with cognate modern words, and then train a CSMT system on these pairs. In both sets of experiments, we also optionally make use of a lexicon of modern words to filter the modernisation hypotheses. While we show that both methods produce significantly better results than the baselines, their accuracy and which method works best strongly correlates with the age of the texts, meaning that the choice of the best method will depend on the properties of the historical language which is to be modernised. As an extrinsic evaluation, we also compare the quality of part-of-speech tagging and lemmatisation directly on historical text and on its modernised words. We show that, depending on the age of the text, annotation on modernised words also produces significantly better results than annotation on the original text.
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Mykytka, Iryna, and Isabel Balteiro. "Painting with words: describing women in photography." Feminismo/s, no. 38 (July 13, 2021): 149. http://dx.doi.org/10.14198/fem.2021.38.06.
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This study aims to identify and explore the linguistic devices used to describe women in photography, and the similarities and differences between women and men’s descriptions. Nowadays, digital photographs are often accompanied by text such as titles, descriptions, comments, or tags. Though the language used in social media has largely been explored in relation to different fields of knowledge and from different perspectives, to the best of our knowledge, there is no work dealing with the language used by professional photographers to describe women and men. In order to carry out the study, we have compiled two samples of the keywords used to describe images representing female and male figures; the source of such sample being an image stock used by professional photographers (Alamy). To fulfil our objective, descriptive adjectives were identified, analyzed, and compared and/or contrasted. The results show many similarities in the use of the descriptive adjectives for women and men’s images, and they also seem to suggest that women and women’s beauty in particular are described from the male perspective and their stereotypes in our society.
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Chen, Bi, Leilei Zhu, Daniel Kifer, and Dongwon Lee. "What Is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1007–12. http://dx.doi.org/10.1609/aaai.v24i1.7717.
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In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.
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Wilhelm, Jeffrey D. "Next Steps in the Journey: The Power of Teacher Inquiry: Developing a Critical Literacy for Teachers." Voices from the Middle 17, no. 2 (December 1, 2009): 36–39. http://dx.doi.org/10.58680/vm20099460.
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While developing critical literacy in our students is a priority for all of us, Wilhelm believes that we must develop a critical literacy of teaching, as well. He asserts that this can best be achieved by 1) teaching through an inquiry context, and 2) taking on a teacher-researcher stance to think both reflectively and reflexively about our teaching. In other words, we must always learn from our students how best to teach them.
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Enciso, Sarah, Carlson Milikin, and James Scofield O’Rourke. "Corporate culture and ethics: from words to actions." Journal of Business Strategy 38, no. 6 (November 20, 2017): 69–79. http://dx.doi.org/10.1108/jbs-01-2017-0001.
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Purpose Business organizations should strive to create ethical cultures to win consumer loyalty and thus safeguard long-term performance success. Management bears ultimate responsibility for promoting ethical behavior. By rewarding ethical behaviors and punishing transgressions, management will reinforce morally upright behavior and create a positive company culture. Successful promotion of corporate ethics, in turn, will boost employee morale, increase performance beyond bare minimums and retain employees in the long run. With a well-structured ethics code and strong reward system, management has all the tools necessary to create an ethical company culture. Design/methodology/approach This viewpoint paper, while advocating for a systematic approach to ethical behavior in a business organization, carefully reviews both well-established literature in this area as well as current best practices. The aim is to provide senior managers with a sense of how the best corporate ethics programs are organized and structured. Findings A successful corporate ethics program must involve all employees from executives to hourly wage workers, with each taking personal responsibility for his or her own performance and results. While no guarantees of success are offered, one reasonably certain path to failure is for an organization to post an ethics code and then ignore it. Ethics must be discussed, modified from time to time and actively integrated into the life of every organization that hopes to avoid ethical missteps. Originality/value This paper offers a fresh viewpoint on both the value and the organization of a potentially successful corporate ethics program. While time-honored ideas serve as the foundation for our discussion, a thorough review of current issues and best practices form the directional heading for the paper’s conclusions.
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El-Razzaz, Mohammed, Mohamed Waleed Fakhr, and Fahima A. Maghraby. "Arabic Gloss WSD Using BERT." Applied Sciences 11, no. 6 (March 13, 2021): 2567. http://dx.doi.org/10.3390/app11062567.
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Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.
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Dhrolia, Murtaza, and Aasim Ahmad. "Problem to solution; from mammalian skin grafts to renal allograft rejection: a tale of 66 years of evolution of our understanding." Journal of the Pakistan Medical Association 73, no. 6 (May 15, 2023): 1255–65. http://dx.doi.org/10.47391/jpma.6600.
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The first successful kidney transplant was done in 1954, and it remains the best option for those with failed kidneys. However, the recipient’s immune system remains the most formidable barrier to transplantation, leading to rejection. Rejection continues to be the most important reason of graft malfunction and chronic renal allograft dysfunction and remains a challenge to date for successful transplant survival. The current narrative review was planned to find the best possible solution to the problem from among the different solutions presented in literature related to allograft rejection since 1954. Key Words: Evolution, Renal transplantation, Allograft rejection, Immunosuppressant.
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Lamsaf, Asmae, Mounir Ait Kerroum, Siham Boulaknadel, and Youssef Fakhri. "Recognition of Arabic handwritten words using convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 1148. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1148-1155.
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<span>A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.</span>
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Zdrazilova, Lenka, David M. Sidhu, and Penny M. Pexman. "Communicating abstract meaning: concepts revealed in words and gestures." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1752 (June 18, 2018): 20170138. http://dx.doi.org/10.1098/rstb.2017.0138.
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Abstract words refer to concepts that cannot be directly experienced through our senses (e.g. truth , morality ). How we ground the meanings of abstract words is one of the deepest problems in cognitive science today. We investigated this question in an experiment in which 62 participants were asked to communicate the meanings of words (20 abstract nouns, e.g. impulse ; 10 concrete nouns, e.g. insect ) to a partner without using the words themselves (the taboo task). We analysed the speech and associated gestures that participants used to communicate the meaning of each word in the taboo task. Analysis of verbal and gestural data yielded a number of insights. When communicating about the meanings of abstract words, participants' speech referenced more people and introspections. In contrast, the meanings of concrete words were communicated by referencing more objects and entities. Gesture results showed that when participants spoke about abstract word meanings their speech was accompanied by more metaphorical and beat gestures, and speech about concrete word meanings was accompanied by more iconic gestures. Taken together, the results suggest that abstract meanings are best captured by a model that allows dynamic access to multiple representation systems. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.
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Vetráb, Mercedes, and Gábor Gosztolya. "Using the Bag-of-Audio-Words approach for emotion recognition." Acta Universitatis Sapientiae, Informatica 14, no. 1 (August 1, 2022): 1–21. http://dx.doi.org/10.2478/ausi-2022-0001.
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Abstract The problem of varying length recordings is a well-known issue in paralinguistics. We investigated how to resolve this problem using the bag-of-audio-words feature extraction approach. The steps of this technique involve preprocessing, clustering, quantization and normalization. The bag-of-audio-words technique is competitive in the area of speech emotion recognition, but the method has several parameters that need to be precisely tuned for good efficiency. The main aim of our study was to analyse the effectiveness of bag-of-audio-words method and try to find the best parameter values for emotion recognition. We optimized the parameters one-by-one, but built on the results of each other. We performed the feature extraction, using openSMILE. Next we transformed our features into same-sized vectors with openXBOW, and finally trained and evaluated SVM models with 10-fold-crossvalidation and UAR. In our experiments, we worked with a Hungarian emotion database. According to our results, the emotion classification performance improves with the bag-of-audio-words feature representation. Not all the BoAW parameters have the optimal settings but later we can make clear recommendations on how to set bag-of-audio-words parameters for emotion detection tasks.
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Soler, Aina Garí, Matthieu Labeau, and Chloé Clavel. "The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations." Transactions of the Association for Computational Linguistics 12 (2024): 299–320. http://dx.doi.org/10.1162/tacl_a_00647.
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Abstract When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into subwords. What is the best way to represent these words with a single vector, and are these representations of worse quality than those of in-vocabulary words? We carry out an intrinsic evaluation of embeddings from different models on semantic similarity tasks involving OOV words. Our analysis reveals, among other interesting findings, that the quality of representations of words that are split is often, but not always, worse than that of the embeddings of known words. Their similarity values, however, must be interpreted with caution.
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McIntosh, Margaret E. "Word Roots in Geometry." Mathematics Teacher 87, no. 7 (October 1994): 510–15. http://dx.doi.org/10.5951/mt.87.7.0510.
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Vocabulary and word study are now considered to be within the purview of the mathematics classroom. But, as mathematics teachers, we rarely do a separate unit on “words.” Instead, we teach the words Students' learning logs helped me gauge their tnderstanding 510 our students need—triangles, quadratic equations, and so on—along with the regular unit of study. This article offers suggestions for a unit on word study in geometry. This unit lasts one week and is best presented as the first unit of the year.
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Wicaksana, Hilman Singgih, Retno Kusumaningrum, and Rahmat Gernowo. "Determining community happiness index with transformers and attention-based deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (June 1, 2024): 1753. http://dx.doi.org/10.11591/ijai.v13.i2.pp1753-1761.
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In the current digital era, evaluating the quality of people's lives and their happiness index is closely related to their expressions and opinions on Twitter social media. Measuring population welfare goes beyond monetary aspects, focusing more on subjective well-being, and sentiment analysis helps evaluate people's perceptions of happiness aspects. Aspect-based sentiment analysis (ABSA) effectively identifies sentiments on predetermined aspects. The previous study has used Word-to-Vector (Word2Vec) and long short-term memory (LSTM) methods with or without attention mechanism (AM) to solve ABSA cases. However, the problem with the previous study is that Word2Vec has the disadvantage of being unable to handle the context of words in a sentence. Therefore, this study will address the problem with bidirectional encoder representations from transformers (BERT), which has the advantage of performing bidirectional training. Bayesian optimization as a hyperparameter tuning technique is used to find the best combination of parameters during the training process. Here we show that BERT-LSTM-AM outperforms the Word2Vec-LSTM-AM model in predicting aspect and sentiment. Furthermore, we found that BERT is the best state-of-the-art embedding technique for representing words in a sentence. Our results demonstrate how BERT as an embedding technique can significantly improve the model performance over Word2Vec.
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Elder, Alexis M. "What Words Can’t Say." Journal of Information, Communication and Ethics in Society 16, no. 1 (March 12, 2018): 2–15. http://dx.doi.org/10.1108/jices-08-2017-0050.
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Purpose This paper aims to survey the moral psychology of emoji, time-restricted messaging and other non-verbal elements of nominally textual computer-mediated communication (CMC). These features are increasingly common in interpersonal communication. Effects on both individual well-being and quality of intimate relationships are assessed. Results of this assessment are used to support ethical conclusions about these elements of digital communication. Design/methodology/approach Assessment of these non-verbal elements of CMC is framed in light of relevant literature from a variety of fields, including neuroscience, behavioral economics and social psychology. The resulting ethical analysis is informed by both Aristotelian and Buddhist virtue ethics. Findings This paper finds that emoji and other nonverbal elements of CMC have positive potential for individual well-being and interpersonal communication. They can be used to focus and direct attention, express and acknowledge difficult emotions and increase altruistic tendencies. Research limitations/implications This paper is conceptual, extrapolating from existing literature to investigate possibilities rather than reporting on novel experiments. It is not intended to substitute for empirical research on use patterns and their effects. But by identifying positive potential, it can help both users and designers to support individual and relational well-being. Practical implications The positive effects identified here can be incorporated into both design and use strategies for CMC. Social implications Situating ethical analysis of these trending technologies within literature from the social sciences on the effects of stylized faces, disappearing messages and directed attention can help us both understand their appeal to users and best practices for using them to enrich our social lives. Originality/value The paper uses empirically informed moral psychology to understand a deceptively trivial-looking phenomenon with wide-ranging impacts on human psychology and relationships.
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Doval, Yerai, Jesús Vilares, and Carlos Gómez-Rodríguez. "Towards Robust Word Embeddings for Noisy Texts." Applied Sciences 10, no. 19 (October 1, 2020): 6893. http://dx.doi.org/10.3390/app10196893.
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Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce the concept of bridge-words, which are artificial words added to the model to strengthen the similarity between standard words and their noisy variants. Our new embeddings outperform baseline models on noisy texts on a wide range of evaluation tasks, both intrinsic and extrinsic, while retaining a good performance on standard texts. To the best of our knowledge, this is the first explicit approach at dealing with these types of noisy texts at the word embedding level that goes beyond the support for out-of-vocabulary words.
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Stanchev, Lubomir. "Measuring the Strength of the Semantic Relationship Between Words." International Journal on Artificial Intelligence Tools 24, no. 02 (April 2015): 1540011. http://dx.doi.org/10.1142/s0218213015400114.
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We propose a novel way for extracting the strength of the semantic relationship between words from semi-structured sources, such as WordNet. Unlike existing approaches that only explore the structured information (e.g., the hypernym relationship in WordNet), we present a framework that allows us to utilize all available information, including natural text descriptions. Our approach constructs a similarity graph that stores the strength of the semantic relationship between words. Specifically, an edge between two words describes the probability that someone who is interested in resources about the first word will be also interested in resources about the second word. Note that the graph is asymmetric because the probability that someone is interested in the second word given that they are interested in the first word is not the same as the probability that they are interested in the first word given that they are interested in the second word. The similarity between any two words in the graph can be computed as a function of the directed paths between the two nodes in the graph that represent the words. We evaluate the quality of the data in the similarity graph by comparing the similarity of pairs of words using our software that uses the graph with results of studies that are performed with human subjects. To the best of our knowledge, our software produces better correlation with the results of both the Miller and Charles study and the WordSimilarity-353 study than any other published research. We also present an extended evaluation section that describes how the different heuristics that we use affect the correlation score.
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Hassan, Ahmed, Amjad Abu-Jbara, Wanchen Lu, and Dragomir Radev. "A Random Walk–Based Model for Identifying Semantic Orientation." Computational Linguistics 40, no. 3 (September 2014): 539–62. http://dx.doi.org/10.1162/coli_a_00192.
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Automatically identifying the sentiment polarity of words is a very important task that has been used as the essential building block of many natural language processing systems such as text classification, text filtering, product review analysis, survey response analysis, and on-line discussion mining. We propose a method for identifying the sentiment polarity of words that applies a Markov random walk model to a large word relatedness graph, and produces a polarity estimate for any given word. The model can accurately and quickly assign a polarity sign and magnitude to any word. It can be used both in a semi-supervised setting where a training set of labeled words is used, and in a weakly supervised setting where only a handful of seed words is used to define the two polarity classes. The method is experimentally tested using a gold standard set of positive and negative words from the General Inquirer lexicon. We also show how our method can be used for three-way classification which identifies neutral words in addition to positive and negative words. Our experiments show that the proposed method outperforms the state-of-the-art methods in the semi-supervised setting and is comparable to the best reported values in the weakly supervised setting. In addition, the proposed method is faster and does not need a large corpus. We also present extensions of our methods for identifying the polarity of foreign words and out-of-vocabulary words.
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Rantala, Leevi, and Mika Mäntylä. "Predicting technical debt from commit contents: reproduction and extension with automated feature selection." Software Quality Journal 28, no. 4 (July 4, 2020): 1551–79. http://dx.doi.org/10.1007/s11219-020-09520-3.
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AbstractSelf-admitted technical debt refers to sub-optimal development solutions that are expressed in written code comments or commits. We reproduce and improve on a prior work by Yan et al. (2018) on detecting commits that introduce self-admitted technical debt. We use multiple natural language processing methods: Bag-of-Words, topic modeling, and word embedding vectors. We study 5 open-source projects. Our NLP approach uses logistic Lasso regression from Glmnet to automatically select best predictor words. A manually labeled dataset from prior work that identified self-admitted technical debt from code level commits serves as ground truth. Our approach achieves + 0.15 better area under the ROC curve performance than a prior work, when comparing only commit message features, and + 0.03 better result overall when replacing manually selected features with automatically selected words. In both cases, the improvement was statistically significant (p < 0.0001). Our work has four main contributions, which are comparing different NLP techniques for SATD detection, improved results over previous work, showing how to generate generalizable predictor words when using multiple repositories, and producing a list of words correlating with SATD. As a concrete result, we release a list of the predictor words that correlate positively with SATD, as well as our used datasets and scripts to enable replication studies and to aid in the creation of future classifiers.
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ZHANG, WEN, TAKETOSHI YOSHIDA, and XIJIN TANG. "DISTRIBUTION OF MULTI-WORDS IN CHINESE AND ENGLISH DOCUMENTS." International Journal of Information Technology & Decision Making 08, no. 02 (June 2009): 249–65. http://dx.doi.org/10.1142/s0219622009003399.
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As a hybrid of N-gram in natural language processing and collocation in statistical linguistics, multi-word is becoming a hot topic in area of text mining and information retrieval. In this paper, a study concerning distribution of multi-words is carried out to explore a theoretical basis for probabilistic term-weighting scheme. Specifically, the Poisson distribution, zero-inflated binomial distribution, and G-distribution are comparatively studied on a task of predicting probabilities of multi-words' occurrences using these distributions, for both technical multi-words and nontechnical multi-words. In addition, a rule-based multi-word extraction algorithm is proposed to extract multi-words from texts based on words' occurring patterns and syntactical structures. Our experimental results demonstrate that G-distribution has the best capability to predict probabilities of frequency of multi-words' occurrence and the Poisson distribution is comparable to zero-inflated binomial distribution in estimation of multi-word distribution. The outcome of this study validates that burstiness is a universal phenomenon in linguistic count data, which is applicable not only for individual content words but also for multi-words.
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Durand, Jean-Pierre. "Can we make our own history?" Capital & Class 21, no. 2 (July 1997): 143–58. http://dx.doi.org/10.1177/030981689706200107.
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Can we still make our own history? Continuing our series of POLEMICS, Jean-Pierre Durand considers the continuing relevance of Marx's concept of the dialectic for explanations of contemporary social and economic changes. In particular, he argues that while Marx's dialectic is crucial for explanations of social contradictions and crises today, it is not an adequate guide to social and political action. THE LEADING WORK OF MARX has a sub-title: A Critique of Political Economy. I believe that Marx was one of the best thinkers of capitalism. Capitalism is not dead, even though it has changed greatly in a century and a half. I shall therefore show, in the second part of this paper, how much the discoveries of Marx can still be of use to us and how they remain essential for a scientific approach towards contemporary capitalism. On the other hand Marx, supported by Lenin and others of his spiritual offspring, has led the revolution he advocated and the societies in which capitalism is developing… towards capitalism itself. I shall not dwell here on the specificities of those peasant, or simply rural, societies, nor in fact on the history of real socialism, but merely on the question of Marx as militant: in other words can one be both a militant and a scholar and, in particular, both a good militant and a good scholar?
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كامل كريم, زينب. "ما تفرد استعماله من الفاظ في القرآن الكريم." Journal of the College of Islamic Sciences 69 (March 30, 2022): 29–66. http://dx.doi.org/10.51930/jcois.21.69.0029.
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We are aware that we cannot reach a correct level of understanding of the Arabic language without using the Quran as our main source in our research. Since the word of the Quran that put an end to all words which kneeled before it all the phrases that leads to the showcasing of the Arabic language in its best self. Arab scientist realized this truth and gave all their attention to the singular and created from it an important subject for their research in the origins of the language and the science behind. Thus, since they dealt with the structure and studied the significance of the context through the use of vocabulary. Therefore, researching Islamic words or the “surat” Mentioned in the Qur’an was necessary for every linguist in every era, and studying Islamic words is the first successful experiment of the scientific term in the history of ancient Arabic. Key words: The words of the Qur’an, the uniqueness of the Qur’anic uses, the meanings of the Qur’anic vocabulary.
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كامل, زينب. "ما تفرد استعماله من الفاظ في القرآن الكريم." Journal of the College of Islamic Sciences 69 (March 30, 2022): 29–66. http://dx.doi.org/10.51930/jcois.21.69.29.
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We are aware that we cannot reach a correct level of understanding of the Arabic language without using the Quran as our main source in our research. Since the word of the Quran that put an end to all words which kneeled before it all the phrases that leads to the showcasing of the Arabic language in its best self. Arab scientist realized this truth and gave all their attention to the singular and created from it an important subject for their research in the origins of the language and the science behind. Thus, since they dealt with the structure and studied the significance of the context through the use of vocabulary. Therefore, researching Islamic words or the “surat” Mentioned in the Qur’an was necessary for every linguist in every era, and studying Islamic words is the first successful experiment of the scientific term in the history of ancient Arabic. Key words: The words of the Qur’an, the uniqueness of the Qur’anic uses, the meanings of the Qur’anic vocabulary.
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Stuckert, Jonathan D. "Forgive our presumption: a difficult reading of Matthew 23:1-3." Perichoresis 16, no. 3 (July 1, 2018): 3–15. http://dx.doi.org/10.2478/perc-2018-0013.
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Abstract In Matthew 23:1-3, Jesus commands His disciples and the crowd to listen to the scribes and Pharisees even while not imitating their actions. Many modern interpreters have lessened the force of Matthew 23:1-3 by an assumption of irony on the part of Jesus. We presume that God could never ordain this for His people. However, this easier reading may not be the best reading. A more straightforward interpretation, but one that is difficult to hear, suggests that at times we may need to sit under bad leadership as means of receiving God’s Word. Pre-critical and modern interpreters provide an understanding of the words of Jesus that is consistent with a theology of God’s providence in times of transition and bad leadership. In addition, there are instances of His direction in both the Old and New Testaments that reinforce this challenging path. It is through this more faithful stance that we grow and flourish in difficult times.
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Fuad, Ahlam, Amany bin Gahman, Rasha Alenezy, Wed Ateeq, and Hend Al-Khalifa. "Qillah: A Morphological Extension for Identifying Plural-of-Paucity Arabic Words." International Journal of Asian Language Processing 30, no. 03 (September 2020): 2050013. http://dx.doi.org/10.1142/s2717554520500137.
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Plural of paucity is one type of broken plural used in the classical Arabic. It is used when the number of people or objects ranges from three to 10. Based on our evaluation of four current state-of-the-art Arabic morphological analyzers, there is a lack of identification of broken plural words, specifically the plural of paucity. Therefore, this paper presents Qillah (paucity), a morphological extension that is built on top of other morphological analyzers and uses a hybrid rule-based and lexicon-based approach to enhance the identification of plural of paucity. Two versions of the Qillah were developed, one is based on FARASA morphological analyzer and the other is based on CALIMA Star analyzer, as these are some of the best-performing morphological analyzers. We designed two experiments to evaluate the effectiveness of our proposed solution based on a collection of 402 different Arabic words. The version based on CALIMA Star achieved a maximum accuracy of 93% in identifying the plural-of-paucity words compared to the baselines. It also achieved a maximum accuracy of 98% compared to the baselines in identifying the plurality of the words.
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Xue, Fuzhao, Aixin Sun, Hao Zhang, and Eng Siong Chng. "GDPNet: Refining Latent Multi-View Graph for Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14194–202. http://dx.doi.org/10.1609/aaai.v35i16.17670.
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Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. Our code is available at https://github.com/XueFuzhao/GDPNet.
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Yeo, George SH, and May Li Lim. "Maternal and Fetal Best Interests in Day-to-Day Obstetrics." Annals of the Academy of Medicine, Singapore 40, no. 1 (January 15, 2011): 43–49. http://dx.doi.org/10.47102/annals-acadmedsg.v40n1p43.
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In medicine, it is the physician’s obligation to promote and protect the patient’s interest. In obstetrics, the ethical principles of beneficence and autonomy provide the fundamental framework which guides the management of all pregnant patients. As there is the need for consideration of the fetus, autonomy can become a complex issue giving rise to what is sometimes called “maternal-fetal conflict.” In this paper, we aim to discuss some scenarios we encounter in our day-to-day obstetric practice such as pre-eclampsia, fetal growth restriction and labour induction when the best interests of the mother and fetus may be conflicted. We hope to illustrate that logical consideration for maternal and fetal best interests is only possible when there is adequate knowledge to support linical practice. Certainly, with the rapid availability of newer knowledge and technology, it is the duty of the physician to be educated continuously so as to protect the patient from harm. Key words: Autonomy, Beneficence, Conflict, Ethics, Pregnant
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Reyaz, Saika, Fozia Rehman, Shagufta Rather, and Sheikh Javeed Sultan. "Bardet–Biedl syndrome: Case report from a tertiary-care hospital in Srinagar, India." Our Dermatology Online 14, no. 1 (January 2, 2023): 88–91. http://dx.doi.org/10.7241/ourd.20231.19.
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Bardet–Biedl syndrome (BBS) is a rare autosomal recessive ciliopathic disorder affecting multiple organ systems. The main clinical features are marked central obesity, retinal dystrophy, polydactyly, mental retardation, hypogonadism, and renal dysfunction. It affects both males and females. Herein, we report an interesting case of BBS with features of the BBS yet without cone-rod dystrophy, which is considered one of the hallmark features of this condition. To the best of our knowledge, this is the first report of Bardet–Biedl syndrome with the absence of cone-rod dystrophy and a normal fundus examination on the Indian subcontinent. Key words: Bardet–Biedl syndrome; Ciliopathic disorder; Polydactyly
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CHOLAKOV, KOSTADIN. "Lexical acquisition and semantic space models: Learning the semantics of unknown words." Natural Language Engineering 20, no. 4 (March 5, 2013): 537–55. http://dx.doi.org/10.1017/s1351324913000053.
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AbstractIn recent studies it has been shown that syntax-based semantic space models outperform models in which the context is represented as a bag-of-words in several semantic analysis tasks. This has been generally attributed to the fact that syntax-based models employ corpora that are syntactically annotated by a parser and a computational grammar. However, if the corpora processed contain words which are unknown to the parser and the grammar, a syntax-based model may lose its advantage since the syntactic properties of such words are unavailable. On the other hand, bag-of-words models do not face this issue since they operate on raw, non-annotated corpora and are thus more robust. In this paper, we compare the performance of syntax-based and bag-of-words models when applied to the task of learning the semantics of unknown words. In our experiments, unknown words are considered the words which are not known to the Alpino parser and grammar of Dutch. In our study, the semantics of an unknown word is defined by finding its most similar word incornetto, a Dutch lexico-semantic hierarchy. We show that for unknown words the syntax-based model performs worse than the bag-of-words approach. Furthermore, we show that if we first learn the syntactic properties of unknown words by an appropriate lexical acquisition method, then in fact the syntax-based model does outperform the bag-of-words approach. The conclusion we draw is that, for words unknown to a given grammar, a bag-of-words model is more robust than a syntax-based model. However, the combination of lexical acquisition and syntax-based semantic models is best suited for learning the semantics of unknown words.
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Gavrilova, I. A., Y. V. Kudryavtseva, and I. A. Lekomtseva. "Our experience with toric intraocular lenses implantation." Reflection, no. 1 (September 20, 2023): 20–23. http://dx.doi.org/10.25276/2686-6986-2023-1-20-23.
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Рurpose. To analyze the results of cataract phacoemulsification with implantation of toric intraocular lenses. Methods. We conducted a retrospective analysis of the results of cataract phacoemulsification with concomitant regular corneal astigmatism of 1.5 D or more. 32 patients (42 eyes) were implanted with toric intraocular lenses: 11 MX 60T lenses (BAUSCH+LOMB) and 31 Torica aAY lenses (HumanOptics). Results. Intraocular lens rotation less than 5 ° was recorded in 40 eyes (95.2 %). In 2 patients, the deviation of Torica aAY lenses was 10–15 °, which required their surgical reposition. All patients achieved an increase in uncorrected visual acuity (UCVA) and best corrected visual acuity (BCVA). UCVA after surgery increased by an average of 0.5 (from 0.13±0.03 to 0.63±0.08), and then remained stable. BCVA increased by 0.33 (from 0.37±0.06 to 0.7±0.06) in the early postoperative period, and by another 0.09 (0.79±0.04) in the late period. Mean value of the spherical component of refraction decreased in the early postoperative period by 4.1 D (from 5.05±0.45 to 0.94±0.22), in the late period a slight increase by 0.09 D was recorded. The value of the cylindrical component decreased after the operation by an average of 2.24 D (from 3.31±0.22 to 1.07±0.37), further a decrease by another 0.1 D was observed. Conclusion. The implantation of toric intraocular lenses is a relatively inexpensive, technically uncomplicated, effective and safe method of cataract surgery and associated corneal astigmatism correction. Key words: phacoemulsification; astigmatism; toric intraocular lens.
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RILOFF, ELLEN, and JESSICA SHEPHERD. "A corpus-based bootstrapping algorithm for Semi-Automated semantic lexicon construction." Natural Language Engineering 5, no. 2 (June 1999): 147–56. http://dx.doi.org/10.1017/s1351324999002235.
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Many applications need a lexicon that represents semantic information but acquiring lexical information is time consuming. We present a corpus-based bootstrapping algorithm that assists users in creating domain-specific semantic lexicons quickly. Our algorithm uses a representative text corpus for the domain and a small set of ‘seed words’ that belong to a semantic class of interest. The algorithm hypothesizes new words that are also likely to belong to the semantic class because they occur in the same contexts as the seed words. The best hypotheses are added to the seed word list dynamically, and the process iterates in a bootstrapping fashion. When the bootstrapping process halts, a ranked list of hypothesized category words is presented to a user for review. We used this algorithm to generate a semantic lexicon for eleven semantic classes associated with the MUC-4 terrorism domain.
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Burton, Allen W. "Urine Drug Testing: Current Recommendations and Best Practices." July 2012 3S;15, no. 3S;7 (July 14, 2012): ES119—ES133. http://dx.doi.org/10.36076/ppj.2012/15/es119.
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Background: The precise role of urine drug testing (UDT) in the practice of pain medicine is currently being defined. Confusion exists as to best practices, and even to what constitutes standard of care. A member survey by our state pain society revealed variability in practice and a lack of consensus. Objective: The authors sought to further clarify the importance of routine UDT as an important part of an overall treatment plan that includes chronic opioid prescribing. Further, we wish to clarify best practices based on consensus and data where available. Methods: A 20-item membership survey was sent to Texas Pain Society members. A group of chronic pain experts from the Texas Pain Society undertook an effort to review the best practices in the literature. The rationale for current UDT practices is clarified, with risk management strategies outlined, and recommendations for UDT outlined in detail. A detailed insight into the limitations of point-of-care (enzyme-linked immunosorbent assay, test cups, test strips) versus the more sensitive and specific laboratory methods is provided. Limitations: Our membership survey was of a limited sample size in one geographic area in the United States and may not represent national patterns. Finally, there is limited data as to the efficacy of UDT practices in improving compliance and curtailing overall medication misuse. Conclusions: UDT must be done routinely as part of an overall best practice program in order to prescribe chronic opioid therapy. This program may include risk stratification; baseline and periodic UDT; behavioral monitoring; and prescription monitoring programs as the best available tools to monitor chronic opioid compliance. Key words: Urine drug screening, urine toxicology screening, urine drug testing, chronic pain, addiction, forensic testing
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Yan, Bin, and Mingtao Pei. "Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2982–90. http://dx.doi.org/10.1609/aaai.v36i3.20204.
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In this paper, we propose a vision-language pre-training model, Clinical-BERT, for the medical domain, and devise three domain-specific tasks: Clinical Diagnosis (CD), Masked MeSH Modeling (MMM), Image-MeSH Matching (IMM), together with one general pre-training task: Masked Language Modeling (MLM), to pre-train the model. The CD task helps the model to learn medical domain knowledge by predicting disease from radiographs. Medical Subject Headings (MeSH) words are important semantic components in radiograph reports, and the MMM task helps the model focus on the prediction of MeSH words. The IMM task helps the model learn the alignment of MeSH words with radiographs by matching scores obtained by a two-level sparse attention: region sparse attention and word sparse attention. Region sparse attention generates corresponding visual features for each word, and word sparse attention enhances the contribution of images-MeSH matching to the matching scores. To the best of our knowledge, this is the first attempt to learn domain knowledge during pre-training for the medical domain. We evaluate the pre-training model on Radiograph Diagnosis and Reports Generation tasks across four challenging datasets: MIMIC-CXR, IU X-Ray, COV-CTR, and NIH, and achieve state-of-the-art results for all the tasks, which demonstrates the effectiveness of our pre-training model.
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Fernández-Martínez, Fernando, Cristina Luna-Jiménez, Ricardo Kleinlein, David Griol, Zoraida Callejas, and Juan Manuel Montero. "Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension." Applied Sciences 12, no. 3 (February 3, 2022): 1610. http://dx.doi.org/10.3390/app12031610.
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Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user’s utterance into one of several predefined classes (intents) that help to understand the user’s current goal. Then, the most adequate response can be provided accordingly. Intent recognizers also often appear as a form of joint models for performing the natural language understanding and dialog management tasks together as a single process, thus simplifying the set of problems that a conversational system must solve. This happens to be especially true for frequently asked question (FAQ) conversational systems. In this work, we first present an exploratory analysis in which different deep learning (DL) models for intent detection and classification were evaluated. In particular, we experimentally compare and analyze conventional recurrent neural networks (RNN) and state-of-the-art transformer models. Our experiments confirmed that best performance is achieved by using transformers. Specifically, best performance was achieved by fine-tuning the so-called BETO model (a Spanish pretrained bidirectional encoder representations from transformers (BERT) model from the Universidad de Chile) in our intent detection task. Then, as the main contribution of the paper, we analyze the effect of inserting unseen domain words to extend the vocabulary of the model as part of the fine-tuning or domain-adaptation process. Particularly, a very simple word frequency cut-off strategy is experimentally shown to be a suitable method for driving the vocabulary learning decisions over unseen words. The results of our analysis show that the proposed method helps to effectively extend the original vocabulary of the pretrained models. We validated our approach with a selection of the corpus acquired with the Hispabot-Covid19 system obtaining satisfactory results.
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Romans, Jay, and Jeff Tobaben. "Our take: building engagement cultures." Strategic HR Review 15, no. 2 (April 11, 2016): 76–82. http://dx.doi.org/10.1108/shr-02-2016-0010.
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Purpose Leadership is about mobilizing self and others to attain a common purpose. The actions chosen to accomplish this are guided by a set of core values. Yet many companies lack the investment in establishing a common purpose and set of core values. Effective leaders use the principle of purpose to establish a direction that will motivate and capture the energy of those needed for success. In other words, they build engagement. The key to having an engagement culture is the top team. Organizations that have strong engagement cultures are populated with senior leaders that make everyday – and everyone – count daily. The purpose of this paper is to discuss the four key principles that must be deployed to build an engagement culture. Design/methodology/approach There are a wide variety of engagement/culture surveys on the market today, but the real key to having an engagement culture is the top team. Effective leaders constantly seek out new ways to improve. They use the principle of purpose to establish a direction that will motivate and capture the energy of those needed for success. They demonstrate stewardship, provide feedback and consistently inquire into the quality of their plans, as well as the effectiveness of their organizations and their own leadership. The approach of this paper requires four key principles to be deployed to build an engagement culture: align the top team through the development of a core purpose; senior leaders embrace stewardship; senior leaders practice feedback; and senior leaders model caring and listening. Findings To build a culture of engagement, leaders must require that developing engaged work teams be a part of the business strategy. This means engagement becomes the way the company operates, not something done in addition to the company’s work. In other words, it is a fundamental tenet of the company that business performance is achieved by leveraging the organization’s human capital. High-performing leaders make employee engagement non-negotiable, by establishing two things: importance and accountability. Organizations that have strong engagement cultures are populated with senior leaders that make everyday – and everyone – count daily. If an organization is to build a culture of engagement, then it must first be modeled by leaders. These leaders are not afraid of tough questions, comments or challenges. They seek out those who will engage in the tough conversations to build their capability and that of the organization. Originality/value High-performing leaders make employee engagement non-negotiable, by establishing two things: importance and accountability. The approach of this paper involves leveraging the talents of the organization’s employees and engaging them to produce stronger business performance. The world’s best-in-class organizations understand that human capital yields organic growth and creates brand loyalty. When senior leaders align their top team through the development of an engaged workforce, we then see an engagement culture emerge that drives business performance and creates a true competitive advantage for the organization.
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Chase, Andrea, Vidya Sreenivasan, Dorothyann Curran, Monica Robichaud, Lorraine Maddigan, and Tory Bowman. "Poster (Clinical/Best Practice Implementation) ID 1984775." Topics in Spinal Cord Injury Rehabilitation 29, suppl (September 1, 2023): 183. http://dx.doi.org/10.46292/sci23-1984775s.
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Background/Objectives Monitoring skin integrity is a critical issue for patients with spinal cord injury. Damage to the skin can go unnoticed due to sensory loss or diminution and can result in pressure injuries or wounds that can be difficult to heal. A team of inpatient clinicians and people with lived experience worked together to create a ‘SkIn-fo-Graphic’ that would be used to teach all new inpatients how to do a full body skin check. Methods A full picture of the body, contributed by Spinal Cord Injury Ontario (SCIO), was marked with the names of specific bony prominences and areas which should be viewed daily to ensure a skin check is complete. Staff at our Centre modified the graphic and created step-by-step instructions. Patients provided feedback on terminology and placement of words/ arrows for clarity. Further refinement was completed by SCIO and clinical staff to create the final tool and instructions. Results A graphic was developed iteratively by a community organization, physicians, allied health professionals and patients to provide a tool with instructions that can be used by both clinical staff (to teach daily skin check) and patients (as a reference for doing their own checks). A QR Code link was also created to directly link patients to more in-depth skin education on the community partner website. Conclusion Engaging all stakeholders in the development of a key tool for instruction of skin check in patients with spinal cord injury is important to ensure complete clarity and utility.
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Brand, Sophie, and Mirjam Ernestus. "Listeners’ processing of a given reduced word pronunciation variant directly reflects their exposure to this variant: Evidence from native listeners and learners of French." Quarterly Journal of Experimental Psychology 71, no. 5 (January 1, 2018): 1240–59. http://dx.doi.org/10.1080/17470218.2017.1313282.
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In casual conversations, words often lack segments. This study investigates whether listeners rely on their experience with reduced word pronunciation variants during the processing of single segment reduction. We tested three groups of listeners in a lexical decision experiment with French words produced either with or without word-medial schwa (e.g., /ʀvy/ and /ʀvy/ for revue). Participants also rated the relative frequencies of the two pronunciation variants of the words. If the recognition accuracy and reaction times (RTs) for a given listener group correlate best with the frequencies of occurrence holding for that given listener group, recognition is influenced by listeners’ exposure to these variants. Native listeners’ relative frequency ratings correlated well with their accuracy scores and RTs. Dutch advanced learners’ accuracy scores and RTs were best predicted by their own ratings. In contrast, the accuracy and RTs from Dutch beginner learners of French could not be predicted by any relative frequency rating; the rating task was probably too difficult for them. The participant groups showed behaviour reflecting their difference in experience with the pronunciation variants. Our results strongly suggest that listeners store the frequencies of occurrence of pronunciation variants, and consequently the variants themselves.
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Elbow, Peter. "The Music of Form: Rethinking Organization in Writing." College Composition & Communication 57, no. 4 (June 1, 2006): 620–66. http://dx.doi.org/10.58680/ccc20065062.
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Written words are laid out in space and exist on the page all at once, but a reader can only read a few words at a time. For readers, written words are trapped in the medium of time. So how can we best organize writing for readers? Traditional techniques of organization tend to stress the arrangement of parts in space and certain metadiscoursal techniques that compensate for the problem of time. In contrast, I’ll describe five ways to organize written language that harness or bind time. In effect, I’m exploring form as a source of energy. More broadly, I’m implying that our concept itself of “organization” is biased toward a picture of how objects are organized in space and neglects the story of how events are organized in time.
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McGregor, Karla K., Timothy Arbisi-Kelm, Bogi Perelmutter, and Jacob Oleson. "Favorite Words as a Window onto the Aesthetic Function of Language." American Speech 94, no. 3 (August 1, 2019): 380–96. http://dx.doi.org/10.1215/00031283-7603218.
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This article characterizes the aesthetic properties of English words. One thousand adult speakers of American English reported their favorite words and justified their selections. Each word was coded for phonological characteristics, valence, and frequency of occurrence and compared with words from a corpus of everyday English from Reader’s Digest. The participants’ stated reasons for their selections were categorized as utilitarian (meaning, use) or aesthetic (form). Compared to the word types in the Reader’s Digest corpus, the favorite words were longer and lower in frequency of occurrence. They were less likely to include a sonorant consonant but more likely to include a repeated consonant. Consonant clusters were sparser among the favorite words. The majority of people justified their favorite words on utilitarian grounds, suggesting some difficulty considering form apart from meaning. The best predictors of aesthetic justifications were lower valence, lower frequency, longer length, higher sonority, and a higher density of lax vowels. People value words for the work they do—the meanings they convey. Nevertheless, people can appreciate words as objects, and when they do, novelty and musicality are privileged. This study informs our understanding of the aesthetic function of language and situates that function into a broader consideration of aesthetic appreciation.
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Redwan, Sadi M., Md Rashed-Al-Mahfuz, and Md Ekramul Hamid. "Recognizing Command Words using Deep Recurrent Neural Network for Both Acoustic and Throat Speech." European Journal of Information Technologies and Computer Science 3, no. 2 (May 22, 2023): 7–13. http://dx.doi.org/10.24018/compute.2023.3.2.88.
Full textAbstract:
The importance of speech command recognition in a human-machine interaction system is increased in recent years. In this study, we propose a deep neural network-based system for acoustic and throat command speech recognition. We apply a preprocessed pipeline to create the input of the deep learning model. Firstly, speech commands are decomposed into components using well-known signal decomposition techniques. The Mel-frequency cepstral coefficients (MFCC) feature extraction method is applied to each component of the speech commands to obtain the feature inputs for the recognition system. At this stage, we apply and compare performance using different speech decomposition techniques such as wavelet packet decomposition (WPD), continuous wavelet transform (CWT), and empirical mode decomposition (EMD) in order to find out the best technique for our model. We observe that WPD shows the best performance in terms of classification accuracy. This paper investigates long short-term memory (LSTM)-based recurrent neural network (RNN), which is trained using the extracted MFCC features. The proposed neural network is trained and tested using acoustic speech commands. Moreover, we also train and test the proposed model using a throat mic. speech commands as well. Lastly, the transfer learning technique is employed to increase the test accuracy for throat speech recognition. The weights of the model train with the acoustic signal are used to initialize the model used for throat speech recognition. Overall, we have found significant classification accuracy for both acoustic and throat command speech. We obtain LSTM is much better than the GMM-HMM model, convolutional neural networks such as CNN-tpool2 and residual networks such as res15 and res26 with an accuracy score of over 97% on Google’s Speech Commands dataset and we achieve 95.35% accuracy on our throat speech data set using the transfer learning technique.
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Jain, Goonjan, and D. K. Lobiyal. "Word sense disambiguation using implicit information." Natural Language Engineering 26, no. 4 (September 13, 2019): 413–32. http://dx.doi.org/10.1017/s1351324919000421.
Full textAbstract:
AbstractHumans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.