Head and neck masses are space-occupying lesions that can compress the airway and esophagus and may affect nerves and blood vessels. Available public datasets primarily focus on malignant lesions and often overlook other space-occupying conditions in this region. To address this gap, we introduce MasHeNe, an initial dataset of 3,779 contrast-enhanced CT slices that includes both tumors and cysts with pixel-level annotations. We also establish a benchmark using standard segmentation baselines and report common metrics to enable fair comparison. In addition, we propose the Windowing-Enhanced Mamba with Frequency integration (WEMF) model. WEMF applies tri-window enhancement to enrich the input appearance before feature extraction. It further uses multi-frequency attention to fuse information across skip connections within a U-shaped Mamba backbone. On MasHeNe, WEMF attains the best performance among evaluated methods, with a Dice of 70.45%, IoU of 66.89%, NSD of 72.33%, and HD95 of 5.12 mm. This model indicates stable and strong results on this challenging task. MasHeNe provides a benchmark for head-and-neck mass segmentation beyond malignancy-only datasets. The observed error patterns also suggest that this task remains challenging and requires further research. Our dataset and code are available at https://github.com/drthaodao3101/MasHeNe.git.

This work explores the journey towards achieving Bahnaric-Vietnamese translation for the sake of culturally bridging the two ethnic groups in Vietnam. However, translating from Bahnaric to Vietnamese also encounters some difficulties. The most prominent challenge is the lack of available original Bahnaric resources source language, including vocabulary, grammar, dialogue patterns and bilingual corpus, which hinders the data collection process for training. To address this, we leverage a transfer learning approach using sequence-to-sequence pre-training language model. First of all, we leverage a pre-trained Vietnamese language model to capture the characteristics of this language. Especially, to further serve the purpose of machine translation, we aim for a sequence-to-sequence model, not encoder-only like BERT or decoder-only like GPT. Taking advantage of significant similarity between the two languages, we continue training the model with the currently limited bilingual resources of Vietnamese-Bahnaric text to perform the transfer learning from language model to machine translation. Thus, this approach can help to handle the problem of imbalanced resources between two languages, while also optimizing the training and computational processes. Additionally, we also enhanced the datasets using data augmentation to generate additional resources and defined some heuristic methods to help the translation more precise. Our approach has been validated to be highly effective for the Bahnaric-Vietnamese translation model, contributing to the expansion and preservation of languages, and facilitating better mutual understanding between the two ethnic people.

This research presents an enhanced approach for precise segmentation of brain tumor masses in magnetic resonance imaging (MRI) using an advanced 3D-UNet model combined with a Context Transformer (CoT). By architectural expansion CoT, the proposed model extends its architecture to a 3D format, integrates it smoothly with the base model to utilize the complex contextual information found in MRI scans, emphasizing how elements rely on each other across an extended spatial range. The proposed model synchronizes tumor mass characteristics from CoT, mutually reinforcing feature extraction, facilitating the precise capture of detailed tumor mass structures, including location, size, and boundaries. Several experimental results present the outstanding segmentation performance of the proposed method in comparison to current state-of-the-art approaches, achieving Dice score of 82.0%, 81.5%, 89.0% for Enhancing Tumor, Tumor Core and Whole Tumor, respectively, on BraTS2019.

Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnamese dataset for detecting abusive content in Vietnamese narrative texts. We sourced these texts from VnExpress, Vietnam's popular online newspaper, where readers often share stories containing abusive content. Identifying and categorizing abusive spans in these texts posed significant challenges during dataset creation, but it also motivated our research. We experimented with lightweight baseline models by freezing PhoBERT and XLM-RoBERTa and using their hidden states in a BiLSTM to assess the complexity of the dataset. According to our experimental results, PhoBERT outperforms other models in both labeled and unlabeled abusive span detection tasks. These results indicate that it has the potential for future improvements.

Explaining the generalization ability of machine learning methods (that is, they can provide a close fit to new, unseen data) lies at the heart of theoretical machine learning. The main direction for this research topic is to bound the difference between the expected loss (population loss) and the empirical loss (training loss). This is known as generalization bound, which has been studied extensively in various settings (Freund et al., 2004;Zou et al.,2009;Agarwal and Duchi,2012;Cuong et al.,2013;Bartlett et al.,2017; Golowich et al., 2018; Lugosi and Neu, 2022). In the last decade, deep neural networks have become the central attention of the machine learning community due to their remarkable success in solving complex tasks that are considered to be challenging for existing machine learning methods. For example, in computer vision, tasks like image classification, facial recognition, and object detection have significant progress by applying deep neural networks (Krizhevsky et al., 2012). In natural language processing, deep learning models have become state-of-the-art in language translation, sentiment analysis, and chatbots (Vaswani et al., 2017).

Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed. Muscle mass must be recovered through physiotherapy before patient discharge and ultrasound imaging is frequently used to assess the recovery process by measuring the muscle size over time. However, these manual measurements are subject to large variability, particularly since the scans are typically acquired on different days and potentially by different operators. In this paper, we propose a self-supervised contrastive learning approach to automatically retrieve similar ultrasound muscle views at different scan times. Three different models were compared using data from 67 patients acquired in the ICU. Results indicate that our contrastive model outperformed a supervised baseline model in the task of view retrieval with an AUC of 73.52% and when combined with an automatic segmentation model achieved 5.7% 0.24% error in crosssectional area. Furthermore, a user study survey confirmed the efficacy of our model for muscle view retrieval.

We are looking for qualified Computer Vision Research Engineers for eKYC project, who will help us build up our digital identity verification products. We are an AI Fintech company specialized in assessing credit profiles of consumers in emerging markets combining pioneering AI with large alternative data sources. In 2020 we reached our ambitious milestone of credit profiling 1 billion consumers spanning 4 countries - Vietnam, Indonesia, India & the Philippines - and building a platform for the wider industry and the financial services industry, in particular, to provide the "un & under" served access to credit. At the core of this initiative has been our strict and unwavering adherence to the norms of consumer data privacy and consumer data rights. But we're not satisfied as we embark on the next leg of our journey to deliver 100 million credit lines to consumers in the markets where we operate.

All flames are extinguished as early as possible, or fire services have to deal with major conflagrations. This leads to the fact that the quality of fire extinguishers has become a very sensitive and important issue in firefighting. Inspired by the development of automatic fire fighting systems, this paper proposes key specifications based on the standard of fire extinguishers that is ISO 7165:2009 and ISO 11601:2008, and feasible solutions to design a mobile manipulator for automatically evaluating the quality or, more specifically, power of fire extinguishers. In addition, a part of the mechanical design is also discussed. Keywords: Portable fire extinguishers, wheeled fire extinguishers, fire test.

Minh-Hoang Tran,1 Ngoc Quy Nguyen,2 Hong Tham Pham1,3 1Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam; 2Institute of Environmental Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 3Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Correspondence: Hong Tham Pham, Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, Tel 84 919 559 085, Email [email protected] Abstract: Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague.

Artificial intelligence (AI) and machine learning (ML) have become more popular in Vietnam with a large proportion of young people having dabbled in these fields after realizing their potential. CoderSchool, a startup in virtual programming and education in Vietnam, has recently received a U$$2.6 million investment in the pre-Series A fund rounds to expand their scope. In response to the Industrial Revolution 4.0, the needs for workers in technology have tremendously increased. A lot of young people have left their comfort zone and entered the AI and ML fields. Nguyen The Chinh, 35, is a former manager in the technical department of a multinational corporation. He switched to AI and ML and signed up for a three-month bootcamp course.