Using Machine Learning To Measure Sentiment During The Covid-19 Pandemic

Social media has created an unprecedented way for individuals to share their concerns, fears, optimism, and happiness, for example, in ways that were not even conceivable some 20 years ago. Extensive data from these social media platforms, such as Twitter, makes it an invaluable resource for opinion mining and sentiment analysis. Starting in December 2019, the corona virus pandemic has had devastating consequences all over the planet, sparing no country. Health, social, and economic tolls associated with the pandemic has generated intense emotions and spread fear in people of all ages, genders, and races. During these difficult times, many have shared their feelings and opinions on many aspects of their lives via Twitter. In this project we use machine learning to measure subjectivity polarity in COVID-19 related tweets, labelling it as positive, negative, and neutral, depending upon the vocabulary encountered in the tweets. Our work focused on a detailed study of the distribution of opinions among the primary U.S. states. We also tested the relationship between the sentiment scores and the cases of COVID-19 in the United States, establishing a link between the sentiment scores, the reported cases and the death toll. The findings may assist with implementing legislation related to COVID-19, act as a reference for scientific work, inform and educate the public on critical pandemic-related issues.https://ir.library.illinoisstate.edu/urs2021it/1002/thumbnail.jp

Study the appropriate conditions to obtain germinated brown rice with high biological activity

ABSTRACT – QMFS 2019Germinated brown rice strains contain more bioactive substances than germinated regular rice ones, however germination conditions play an important role in the activity and the content of those substances. The proper germination process provides the optimized active ingredients from rice that can be used for the production of nutritious beverages. In this study, we investigated the effects of pH, temperature and incubation time in microaerobic culture condition on the change of bioactive substances in AnhDao brown rice. The optimal germination condition with pH at 3, temperature of 35 0C and time for 36h release 109.11U/g of the α-amylase activity, 17.22(U/g) of the enzyme glutamate decacboxylase (GAD), 1.38(U/g) of protease, 231.76mg/100g of GABA content and 21.9 (mgGAE/100g) of polyphenol from germinated AnhDao brown rice. In nutrient evaluation, germinated AnhDao brown rice contains 65.53% of starch, 2.49% of lipid, 9.13%of protein, 2.04% of reducing sugar, and 1.26% of ash.Key words: Germinated brown rice, bioactive substances, α-amylase, protease, glutamate decacboxylase (GAD), Gamma aminobutyric acid (GABA)

Selection of Exercises to Improve the Effectiveness of Physical Education under the Project of Elective Sports Cockpitching for Female Students K2023 Hanoi Metropolitan University

The interview results have selected 36 exercises belonging to 5 exercise groups to improve the effectiveness of physical education according to the project for female Hanoi Metropolitan University students studying the optional sport of shuttlecock, including Group 1: Exercises to develop strength quickly has 7 exercises; Group 2: Strength development exercises have 6 exercises; Group 3: Exercises to develop endurance has 5 exercises; Group 4: Exercises to develop flexibility and coordination has 5 exercises; Group 5: Exercises with the bridge has 13 exercises with an agreement level of > 70% or more and an average score of > 3.41 - 4.20 points. &nbsp

Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most vital factors in deploying the deep learning model, especially in human-related or safety applications. In this paper, we present a novel Fairness Continual Learning approach to the semantic segmentation problem. In particular, under the fairness objective, a new fairness continual learning framework is proposed based on class distributions. Then, a novel Prototypical Contrastive Clustering loss is proposed to address the significant challenges in continual learning, i.e., catastrophic forgetting and background shift. Our proposed loss has also been proven as a novel, generalized learning paradigm of knowledge distillation commonly used in continual learning. Moreover, the proposed Conditional Structural Consistency loss further regularized the structural constraint of the predicted segmentation. Our proposed approach has achieved State-of-the-Art performance on three standard scene understanding benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness of the segmentation model

Identifying Key Factors influencing cost variation of construction projects in Ho Chi Minh City

Cost variation is one of the most concerns of the stakeholders in the construction projects, because it always presents in every construction projects, especially in Vietnam. However, the factors that make the project cost variation are not easy to identify. This study aimed to identify the key factors that influence to the project cost variation. A survey questionnaire was used to collect data from construction practitioners. The analysis results show that there are five groups of factors that influence to project cost variation: Government policies, nature, resources, fraud and theft, and ability of stakeholders. Recommendations are also mentioned to deal with the project cost variation

Efficient Finetuning Large Language Models For Vietnamese Chatbot

Large language models (LLMs), such as GPT-4, PaLM, and LLaMa, have been shown to achieve remarkable performance across a variety of natural language tasks. Recent advancements in instruction tuning bring LLMs with ability in following user's instructions and producing human-like responses. However, the high costs associated with training and implementing LLMs pose challenges to academic research. Furthermore, the availability of pretrained LLMs and instruction-tune datasets for Vietnamese language is limited. To tackle these concerns, we leverage large-scale instruction-following datasets from open-source projects, namely Alpaca, GPT4All, and Chat-Doctor, which cover general domain and specific medical domain. To the best of our knowledge, these are the first instructional dataset for Vietnamese. Subsequently, we utilize parameter-efficient tuning through Low-Rank Adaptation (LoRA) on two open LLMs: Bloomz (Multilingual) and GPTJ-6B (Vietnamese), resulting four models: Bloomz-Chat, Bloomz-Doctor, GPTJ-Chat, GPTJ-Doctor.Finally, we assess the effectiveness of our methodology on a per-sample basis, taking into consideration the helpfulness, relevance, accuracy, level of detail in their responses. This evaluation process entails the utilization of GPT-4 as an automated scoring mechanism. Despite utilizing a low-cost setup, our method demonstrates about 20-30\% improvement over the original models in our evaluation tasks.Comment: arXiv admin note: text overlap with arXiv:2304.08177, arXiv:2303.16199 by other author