Factors affecting the performance of private hight school teachers in Yangon (Khin Ohnmar Pa Pa Naing, 2022)

The objectives of this study are to examine the effects of leadership styles, job characteristics and personality traits on the performance of private high school teachers in Yangon and to determine the mediating effects of teachers’ commitment, involvement and satisfaction on the relationships between the influencing factors and the performance. The descriptive and analytical research methods were used to fulfil the objectives of the study. The primary data were collected by using structured questionnaire. The analysis was conducted based on the random sample of respondents of 300 teachers in private high schools in Yangon. According to this study, three factors in objective one are significant. For the leadership styles include autocratic and democratic leadership style, for the job characteristics include skill variety, task identity, task significance, autonomy and feedback and, for the personality traits include conscientiousness, agreeableness, openness, neuroticism and extraversion. In detail analysis shows that autocratic leadership style, neuroticism and extraversion are positive and significant effects on job performance. For the mediating analysis, in the leadership styles, commitment shows partial mediation effect in relationship between autocratic and performance, while no mediation effect for democratic leadership style. Regarding satisfaction, there is no mediation effect on relationship between autocratic and performance. Involvement indicates a partial mediation effect on relationship between two leadership styles, and performances. Concerning job characteristics, commitment, satisfaction and involvement indicate partial mediation effects in relationship between the skill variety, task significance and autonomy, and performances of teachers. The teachers’ commitment, satisfaction, and involvement show full mediation effects on relationship between feedback and performance. However, no mediation effect of involvement occurs in the relationship between task identity and task significance and performances. Concerning personality, commitment shows partial mediation effects on the relationship between conscientiousness and agreeableness, and the performance, however, no mediation occurs on relationship between neuroticism, openness and extraversion, and performance. Involvement shows partial mediation effects on relationship between conscientiousness, agreeableness and neuroticism, and performance while no mediation effect occurs in relationship between openness and extraversion, and performance. Satisfaction indicates partial mediation effects in relationship between conscientiousness, agreeableness and extraversion, and performance but no mediation effect in relationship between neuroticism and openness, and performance. Based on the findings, this study recommends that private high school administers should encourage school leaders create effective and efficient job performance through democratic leadership style. School administrators should design and implement job characteristics that can inspire and motivate to school teachers. In addition, policy makers or decision makers in education sector should recruit and nurture school teachers with about conscientiousness, agreeableness personality traits

A Large Vocabulary End-to-End Myanmar Automatic Speech Recognition

In recent years, sequence-to-sequence technology has become popular in automatic speech recognition area. This model replaces the classic complex pipeline with a single neural network architecture. This paper proposes the use of transformer- and conformer-based models on Myanmar automatic speech recognition system (UCSY-Myan-ASR). Classical hybrid long short-term memory (LSTM) and end-to-end models are presented and evaluated to improve error rates. The experiments were carried on the UCSY-82-hour speech corpus and evaluated in terms of syllable error rate (SER) and character error rate (CER). Using the Transformer approach, the best performance in the daily conversation domain reaches the SER of 9.6% and CER of 7.3%. When using the conformer model, the best performance in the news domain is 10.6% SER and 6.9% CER respectively

Improving the Performance of Low-resourced Speaker Identification with Data Preprocessing

Automatic speaker identification is done to tackle daily security problems. Speech data collection is an essential but very challenging task for under-resourced languages like Burmese. The speech quality is crucial to accurately recognize the speaker’s identity. This work attempted to find the optimal speech quality appropriate for Burmese tone to enhance identification compared with other more richy resourced languages on Mel-frequency cepstral coefficients (MFCCs). A Burmese speech dataset was created as part of our work because no appropriate dataset available for use. In order to achieve better performance, we preprocessed the foremost recording quality proper for not only Burmese tone but also for nine other Asian languages to achieve multilingual speaker identification. The performance of the preprocessed data was evaluated by comparing with the original data, using a time delay neural network (TDNN) together with a subsampling technique that can reduce time complexity in model training. The experiments were investigated and analyzed on speech datasets of ten Asian languages to reveal the effectiveness of the data preprocessing. The dataset outperformed the original dataset with improvements in terms of  equal error rate (EER). The evaluation pointed out that the performance of the system with the preprocessed dataset improved that of the original dataset

Syllable Pronunciation Features for Myanmar Grapheme to Phoneme Conversion

Grapheme-to-Phoneme (G2P) conversion is anecessary step for speech synthesis and speechrecognition. This paper addresses the problem ofgrapheme to phoneme conversion for the Myanmarlanguage. In our method, we propose four simpleMyanmar syllable pronunciation patterns as featuresthat can be used to augment the models in a ConditionalRandom Field (CRF) approach to G2P conversion. Ourresults show that our additional features are able toimprove a strong baseline model that does not includethem. We found that combination of all four featuresgave rise to the highest performancefor Myanmarlanguage G2P conversion

Improving the Performance of Low-resourced Speaker Identification with Data Preprocessing

Automatic speaker identification is done to tackle daily security problems. Speech data collection is an essential but very challenging task for under-resourced languages like Burmese. The speech quality is crucial to accurately recognize the speaker’s identity. This work attempted to find the optimal speech quality appropriate for Burmese tone to enhance identification compared with other more richy resourced languages on Mel-frequency cepstral coefficients (MFCCs). A Burmese speech dataset was created as part of our work because no appropriate dataset available for use. In order to achieve better performance, we preprocessed the foremost recording quality proper for not only Burmese tone but also for nine other Asian languages to achieve multilingual speaker identification. The performance of the preprocessed data was evaluated by comparing with the original data, using a time delay neural network (TDNN) together with a subsampling technique that can reduce time complexity in model training. The experiments were investigated and analyzed on speech datasets of ten Asian languages to reveal the effectiveness of the data preprocessing. The dataset outperformed the original dataset with improvements in terms of  equal error rate (EER). The evaluation pointed out that the performance of the system with the preprocessed dataset improved that of the original dataset

CX-072 (pacmilimab), a Probody® PD-L1 inhibitor, in advanced or recurrent solid tumors (PROCLAIM-CX-072): an open-label dose-finding and first-in-human study

Background: Probody® therapeutics are antibody prodrugs that are activated in the tumor microenvironment by tumor-associated proteases, thereby restricting the activity to the tumor microenvironment and minimizing ‘off-tumor’ toxicity. We report dose-escalation and single-agent expansion phase data from the first-in-human study of CX-072 (pacmilimab), a Probody checkpoint inhibitor directed against programmed death-ligand 1 (PD-L1). / Methods: In the dose-escalation phase of this multicenter, open-label study (NCT03013491), adults with advanced solid tumors (naive to programmed-death-1/PD-L1 or cytotoxic T-lymphocyte-associated antigen 4 inhibitors) were enrolled into one of seven dose-escalation cohorts, with pacmilimab administered intravenously every 14 days. The primary endpoints were safety and determination of the maximum tolerated dose (MTD). In the expansion phase, patients with one of six prespecified malignancies (triple-negative breast cancer [TNBC]; anal squamous cell carcinoma [aSCC]; cutaneous SCC [cSCC]; undifferentiated pleomorphic sarcoma [UPS]; small bowel adenocarcinoma [SBA]; and thymic epithelial tumor [TET]); or high tumor mutational burden (hTMB) tumors were enrolled. The primary endpoint was objective response (Response Evaluation Criteria In Solid Tumors v.1.1). / Results: An MTD was not reached with doses up to 30 mg/kg. A recommended phase 2 dose (RP2D) of 10 mg/kg was chosen based on pharmacokinetic and pharmacodynamic findings in the expansion phase. Ninety-eight patients enrolled in the expansion phase: TNBC (n=14), aSCC (n=14), cSCC (n=14), UPS (n=20), SBA (n=14), TET (n=8), and hTMB tumors (n=14). Of 114 patients receiving pacmilimab at the RP2D, grade ≥3 treatment-related adverse events (TRAEs) were reported in 10 patients (9%), serious TRAEs in six patients (5%), and treatment discontinuation due to TRAEs in two patients (2%). Grade ≥3 immune-related AEs occurred in two patients (rash, myocarditis). High PD-L1 expression (ie, >50% Tumor Proportion Score) was observed in 22/144 (19%) patients. Confirmed objective responses were observed in patients with cSCC (n=5, including one complete response), hTMB (n=4, including one complete response), aSCC (n=2), TNBC (n=1), UPS (n=1), and anaplastic thyroid cancer (n=1). / Conclusions: Pacmilimab can be administered safely at the RP2D of 10 mg/kg every 14 days. At this dose, pacmilimab had a low rate of immune-mediated toxicity and showed signs of antitumor activity in patients not selected for high PD-L1 expression. / Trial registration number: NCT03013491

Low back pain as the presenting sign in a patient with primary extradural melanoma of the thoracic spine - A metastatic disease 17 Years after complete surgical resection

Primary spinal melanomas are extremely rare lesions. In 1906, Hirschberg reported the first primary spinal melanoma, and since then only 40 new cases have been reported. A 47-year-old man was admitted suffering from low back pain, fatigue and loss of body weight persisting for three months. He had a 17-year-old history of an operated primary spinal melanoma from T7-T9, which had remained stable for these 17 years. Routine laboratory findings and clinical symptoms aroused suspicion of a metastatic disease. Multislice computed tomography and magnetic resonance imaging revealed stage-IV melanoma with thoracic, abdominal and skeletal metastases without the recurrence of the primary process. Transiliac crest core bone biopsy confirmed the diagnosis of metastatic melanoma. It is important to know that in all cases of back ore skeletal pain and unexplained weight loss, malignancy must always be considered in the differential diagnosis, especially in the subjects with a positive medical history. Patients who have back, skeletal, or joint pain that is unresponsive to a few weeks of conservative treatment or have known risk factors with or without serious etiology, are candidates for imaging studies. The present case demonstrates that complete surgical resection alone may result in a favourable outcome, but regular medical follow-up for an extended period, with the purpose of an early detection of a metastatic disease, is highly recommended

Automatic Speech Recognition on Spontaneous Interview Speech

This paper presents a spontaneous speechrecognition system for Myanmar language. Automaticspeech recognition (ASR) on some controlled speechhas achieved almost human performance. However, theperformance of spontaneous speech is drasticallydecreased due to the diversity of speaking styles, speakrate, presence of additive and non-linear distortion,accents and weakened articulation. In this study, webuilt a recognizer for Myanmar Interview speech byusing the classical Gaussian Mixture Model basedHidden Markov Model (HMM-GMM) approach. Weinvested that the effect of variation on acoustic featureand number of senones and Gaussian densities onMyanmar Interview speech. According to theseexperiments, we achieved the best Word Error Rate(WER) of 20.47%