1,933 research outputs found
Hidden Trends in 90 Years of Harvard Business Review
In this paper, we demonstrate and discuss results of our mining the abstracts
of the publications in Harvard Business Review between 1922 and 2012.
Techniques for computing n-grams, collocations, basic sentiment analysis, and
named-entity recognition were employed to uncover trends hidden in the
abstracts. We present findings about international relationships, sentiment in
HBR's abstracts, important international companies, influential technological
inventions, renown researchers in management theories, US presidents via
chronological analyses.Comment: 6 pages, 14 figures, Proceedings of 2012 International Conference on
Technologies and Applications of Artificial Intelligenc
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
Automatically classifying electronic health records (EHRs) into diagnostic
codes has been challenging to the NLP community. State-of-the-art methods
treated this problem as a multilabel classification problem and proposed
various architectures to model this problem. However, these systems did not
leverage the superb performance of pretrained language models, which achieved
superb performance on natural language understanding tasks. Prior work has
shown that pretrained language models underperformed on this task with the
regular finetuning scheme. Therefore, this paper aims at analyzing the causes
of the underperformance and developing a framework for automatic ICD coding
with pretrained language models. We spotted three main issues through the
experiments: 1) large label space, 2) long input sequences, and 3) domain
mismatch between pretraining and fine-tuning. We propose PLMICD, a framework
that tackles the challenges with various strategies. The experimental results
show that our proposed framework can overcome the challenges and achieves
state-of-the-art performance in terms of multiple metrics on the benchmark
MIMIC data. The source code is available at https://github.com/MiuLab/PLM-ICDComment: Accepted to the ClinicalNLP 2022 worksho
HIRING TRANSFORMATIONAL LEADERS IN EDUCATION: LESSONS LEARNED FROM STRUCTURED EMPLOYMENT INTERVIEWS
AbstractIn the educational setting, hiring transformational leaders is essential to a schools’ success or failure. According to Burgess (2002), “transformational leadership is vital to school improvement initiatives” (p. 20). In this study, we examine Confucianism and country influence on structured employment interviews from both Western (United States) and Eastern cultures (Taiwan). Eastern cultures have certain values not prevalent in Western cultures that may reduce the use of transformational leadership questions in job interviews. Eastern cultures have higher levels of uncertainty avoidance, collectivism, and power distance. We examined questions asked in actual job interviews in Taiwan and the United States (N = 178). Additionally, we examined the three dimensions of interview structure including evaluation standardization, question sophistication, and questioning consistency. We found that the number of questions about transformational leadership were less common in Taiwan, with its lower selection ratios, and when question sophistication and consistency were higher. In the United States, we found that the number of questions about transformational leadership increased with selection ratio, question sophistication, and question consistency, but not in Taiwan. The results of this study have important implications to all workplace settings around the globe where it may be argued that it is advantageous to hire transformational leaders to improve any organization. However, the results of this study may have particular importance to the educational setting, in both China and the United States, and globally, where the need to attract and hire transformational leaders can be vital to a schools’ success (or failure). Key WordsLeadership, Employment Interviews, Transformational Leadership, Educatio
Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks
In this letter, we study the deployment of Unmanned Aerial Vehicle mounted
Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV
deployment problem as a user satisfaction maximization problem, that is,
maximizing the proportion of served ground users (GUs) that meet a given
minimum data rate requirement. We propose an interference-aware deployment
(IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed
algorithm can alleviate the problem of overlapping coverage between adjacent
UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel
interference between UAV-BSs will improve user satisfaction and ensure that
most GUs can achieve the minimum data rate requirement. Simulation results show
that our proposed IAD outperforms comparative methods by more than 10% in user
satisfaction in high-density environments.Comment: 5 pages, 3 figures, to appear in IEEE Wireless Communications Letter
Depression is a predictor for both smoking and quitting intentions among male coronary artery disease patients
Coronary artery disease (CAD) is the third most prominent cause of death globally, and smoking is the most common risk factors for CAD. However, few studies have examined both smoking and smoking cessation intentions in patients with CAD. The study aims to explore the predictors for smoking and quitting intentions among male CAD patients. This was a cross-sectional study. A total of 368 male CAD patients were recruited and classified into never smoked, quit smoking, and continuing to smoke three groups. Demographic information, level of nicotine dependence, carbon monoxide concentration, depression, and resilience were analyzed by using t-test, one- way analysis of variance (ANOVA), and least significant difference (LSD) post-hoc test and the multiple logistic regression analysis. The results revealed that among participants, 23.1% had never smoked, 40.5% had quit smoking, and 36.4% continued to smoke. Multiple logistic regression analysis revealed that age (OR=0.95, 95% CI=0.90–0.99), carbon monoxide (OR=1.74, 95% CI=1.51–2.01), and depression (OR=1.13, 95% CI=1.04–1.23) predicted participants who continued to smoke. Among the 134 participants who continued to smoke, 35.8% exhibited no intention to quit, and 64.2% planned to quit. Nicotine dependence (OR=0.79, 95% CI=0.66–0.94) and depression (OR=1.10, 95% CI=1.02–1.20) were significant predictors in participants who intended to quit smoking. The study demonstrates that depression is a significant predictor for both smoking and quitting intentions among male CAD patients
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