THE CUISINE OF THE EDE PEOPLE IN KMRONG PRŎNG VILLAGE, BUON MA THUOT (DAK LAK PROVINCE)

The Ede are a Malayo-Polynesian language-speaking ethnic group residing primarily in Dak Lak, Dak Nong, Phu Yen, and Khanh Hoa provinces. They have a rich and unique tangible and intangible culture. From 2017 to 2020, the authors carried out many field trips to Kmrong Prŏng A and Kmrong Prŏng B villages (Ea Tu Commune, Buon Ma Thuot) to research, collect, and inventory the material and spiritual cultural heritage of the Ede for conservation and exhibition at the Dak Lak provincial museum. In this article, we introduce some traditional dishes of the Ede in Krmong Prŏng village, research and evaluate traditional aspects of their cuisine, and propose some recommendations and solutions to preserve and promote the unique Ede cuisine in the current period of development and integration

Differentiable Bayesian Structure Learning with Acyclicity Assurance

Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achieving promising outcomes, most of these methods are still struggling to ensure that the graphs generated from the latent space are acyclic by minimizing a defined score. There has also been another trend of permutation-based approaches, which concern the search for the topological ordering of the variables in the directed acyclic graph in order to limit the search space of the graph. In this study, we propose an alternative approach for strictly constraining the acyclicty of the graphs with an integration of the knowledge from the topological orderings. Our approach can reduce inference complexity while ensuring the structures of the generated graphs to be acyclic. Our empirical experiments with simulated and real-world data show that our approach can outperform related Bayesian score-based approaches.Comment: Accepted as a regular paper (9.37%) at the 23rd IEEE International Conference on Data Mining (ICDM 2023

Tree diversity and species composition of tropical dry forests in Vietnam's Central Highlands Region

Abstract Tree species inventories, particularly of poorly known dry forests, are necessary to protect and restore them in degraded landscapes. The present research has been conducted to compare taxonomic diversity and community composition in four dry forests (DF) categories with different standing volume levels: very low (DFV), low (DFP), medium (DFM) and high (DFR). This quantitative assessment of taxonomic diversity, forest structure and species composition were obtained from 103 sample plots (0.1 ha each). The regeneration potential of trees was assessed in 515 subplots (4 m × 4 m) located within the 103 plots. A total of 1,072 trees representing 87 species belonging to 37 families were recorded in 10.3 ha of total sampled area. The ranges of diversity indices observed in the four forest types were: Margalef's (5.44–8.43), Shannon-Wiener (1.80–2.29), Simpson diversity (0.76–0.87) and evenness (0.32–0.35). The regeneration potential of rare and threatened species Dalbergia oliveri, Hopea recopei, Dalbergia bariensis, Sindora siamensis, Parashorea stellata was observed to be poor. Conversely, Cratoxylon formosum, Shorea obtusa, Dipterocarpus tuberculatus, Dipterocarpus obtusifolius, Terminalia alata, Shorea siamensis and Xylia xylocarpa were the most dominant species at the seedling and sapling stage, showing a strong potential for regeneration. Overall, this study provides useful information on tree species diversity and composition for tropical dry forests which can be used as baseline data to develop incoming plans for forest management and conservation in Vietnam's Central Highlands Region

HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System

Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extraction from scanned documents. However, there exists no solution to provide reliable confidence score for current state-of-the-art deep-learning-based information extractors. In this paper, we propose a complete and novel architecture to measure confidence of current deep learning models in document information extraction task. Our architecture consists of a Multi-modal Conformal Predictor and a Variational Cluster-oriented Anomaly Detector, trained to faithfully estimate its confidence on its outputs without the need of host models modification. We evaluate our architecture on real-wold datasets, not only outperforming competing confidence estimators by a huge margin but also demonstrating generalization ability to out-of-distribution data.Comment: Document Intelligence @ KDD 2021 Worksho

AI-assisted Learning for Electronic Engineering Courses in High Education

This study evaluates the efficacy of ChatGPT as an AI teaching and learning support tool in an integrated circuit systems course at a higher education institution in an Asian country. Various question types were completed, and ChatGPT responses were assessed to gain valuable insights for further investigation. The objective is to assess ChatGPT's ability to provide insights, personalized support, and interactive learning experiences in engineering education. The study includes the evaluation and reflection of different stakeholders: students, lecturers, and engineers. The findings of this study shed light on the benefits and limitations of ChatGPT as an AI tool, paving the way for innovative learning approaches in technical disciplines. Furthermore, the study contributes to our understanding of how digital transformation is likely to unfold in the education sector

Multi-dimensional data refining strategy for effective fine-tuning LLMs

Data is a cornerstone for fine-tuning large language models, yet acquiring suitable data remains challenging. Challenges encompassed data scarcity, linguistic diversity, and domain-specific content. This paper presents lessons learned while crawling and refining data tailored for fine-tuning Vietnamese language models. Crafting such a dataset, while accounting for linguistic intricacies and striking a balance between inclusivity and accuracy, demands meticulous planning. Our paper presents a multidimensional strategy including leveraging existing datasets in the English language and developing customized data-crawling scripts with the assistance of generative AI tools. A fine-tuned LLM model for the Vietnamese language, which was produced using resultant datasets, demonstrated good performance while generating Vietnamese news articles from prompts. The study offers practical solutions and guidance for future fine-tuning models in languages like Vietnamese

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)

An Assessment of the Values of French Colonial Townhouses in Hanoi Towards A More Sustainable Conservation

As the capital city of French Indochina, Hanoi was well planned by the French and immensely invested in the construction of public buildings as well as houses. In addition to public buildings and villas designed in French colonial styles that shaped the so-called distinctive architectural heritage in Hanoi throughout the colonial years, a large number of townhouses built during 1920 - 1950 which formed the cityscape of Hanoi in the first half of the 20th century should be noted. After nearly 70 years since the French army withdrew from the city, the number of French townhouses has considerably decreased. The remaining houses have shown that this is a real “treasure” that needs to be conserved because of their important values, not only in terms of urban architecture but also in cultural and historical aspects. However, a fact requiring special attention is that French townhouses in Hanoi - unlike French public buildings and villas - have not yet been recognised as heritage so that they can be kept to avoid the risk of deterioration or demolition under the impact of rapid urbanisation in the market economy. One of the main reasons for this negative urban development is that there has been no concrete or comprehensive rating system to assess the values of those townhouses which will closely correspond to their characteristics and contexts. Therefore, the authors - based on site surveys and by applying some appropriate methods such as expert consultations and case studies - have developed a full set of criteria to help evaluate those remaining townhouses as accurately as possible. This system can be used as a basis for a systematic assessment and classification towards a more effective conservation and even promoting the values of those townhouses with regard to the development of a modern society and in consideration of sustainable heritage conservation as a mainstream in the world.

Mangrove Mapping and Above-Ground Biomass Change Detection using Satellite Images in Coastal Areas of Thai Binh Province, Vietnam

Mangroves are recognized as a highly valuable resource due to their provision of multiple ecosystem services. Therefore, mangrove ecosystems mapping and monitoring is a crucial objective, especially for tropical regions. Thai Binh province is one of the most important mangrove ecosystems in Vietnam. The mangrove ecosystem in this province has faced threats of deforestation from urban development, land reclamation, tourism activities, and natural disasters. Recently, to maintain the fundamental functions of the ecosystems, a large mangrove area was planted in Thai Binh. The aim of this research is to detect the change in the mangrove areas and to create an aboveground biomass map for mangrove forests in Thai Binh province. Landsat and Sentinel-2 satellite images from 1998 to 2018 were analysed using the supervised classification method to detect mangrove area change. Mangrove Above-ground Biomass (AGB) was estimated using linear regression between vegetation indices and field AGB survey. The accuracy assessment for the classified images of 1998, 2003 and 2007, 2013 and 2018 are 93%, 86%, 96%, 94% and 91% respectively with kappa of 0.8881, 0.7953, 0.9357, 0.9114 and 0.8761. The mangrove cover in the study area was estimated at 5874.93 ha in 1998. This figure decreased significantly to 4433.85 ha in 2007, before recovery began to take place in the study area, which was estimated at 6587.88 ha in 2018. In 1998, the average AGB in this study area was 22.57 ton/ha, and in 2018 it was 37.74 ton/ha with a standard error of 12.41 ton/ha and the root mean square error (RMSE) was ±12.08 ton/ha