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