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)

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

Identifying causes of material wastage on construction sites in Vietnam

The wastage of materials on construction sites caused unnecessary losses to contractors. Curbing material waste is important to save costs for contractors and then to improve their competitive advantages in the construction market. Identifying causes plays a key role in mitigation of material waste on building sites. This study is part of an ongoing research aiming to identify major causes of material waste and to reckon the percentage of material waste on building sites in Vietnam. Literature reviews and a pilot survey provided thirty-five preliminary factors. Thirteen factors resulted from statistical techniques (i.e., mean scores and one sample t-test) were considered as major causes of material waste at site through a questionnaire survey of construction professionals, who worked for medium or large construction firms. The top-five main causes are: (1) incapable workers and site engineers; (2) changes to design; (3) late information about types and sizes of products to the contractor; (4) incapable storekeepers; and (5) waste from inappropriate construction processes. It is concluded that there is a need for enhancing the capability of workers and site engineers for mitigating the material wastage at site related to operation problems. The findings of this research can be used as a guideline to overcome material waste in the VCI as well as in other developing countries. The results of this study may expect to be useful not only to practitioners and researchers in Vietnam but also to participants in other developing countries

FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding

Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defined and addressed. In addition, fairness is one of the most critical aspects when deploying the segmentation models into human-related real-world applications, e.g., autonomous driving, as any unfair predictions could influence human safety. In this paper, we propose a novel Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In particular, from the proposed formulated fairness objective, a new adaptation framework will be introduced based on the fair treatment of class distributions. Moreover, to generally model the context of structural dependency, a new conditional structural constraint is introduced to impose the consistency of predicted segmentation. Thanks to the proposed Conditional Structure Network, the self-attention mechanism has sufficiently modeled the structural information of segmentation. Through the ablation studies, the proposed method has shown the performance improvement of the segmentation models and promoted fairness in the model predictions. The experimental results on the two standard benchmarks, i.e., SYNTHIA $\to$ Cityscapes and GTA5 $\to$ Cityscapes, have shown that our method achieved State-of-the-Art (SOTA) performance.Comment: Accepted to CVPR'2