Identifying Computer-Translated Paragraphs using Coherence Features

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.Comment: 9 pages, PACLIC 201

Optimizing Boiler Efficiency by Data Mining Teciques: A Case Study

In a fertilizer plant, the steam boiler is the most important component. In order to keep the plant operating in the effective mode, the boiler efficiency must be observed continuously by several operators. When the trend of the boiler efficiency is going down, they may adjust the controlling parameters of the boiler to increase its efficiency. Since manual operation usually leads to unex-pectedly mistakes and hurts the efficiency of the system, we build an information system that plays the role of the operators in observing the boiler and adjusting the controlling parameters to stabilize the boiler efficiency. In this paper, we first introduce the architecture of the information system. We then present how to apply K-means and Fuzzy C-means algorithms to derive a knowledge base from the historical operational data of the boiler. Next, recurrent fuzzy neural network is employed to build a boiler simulator for evaluating which tuple of input values is the best optimal and then automatically adjusting controlling inputs of the boiler by the optimal val-ues. In order to prove the effectiveness of our system, we deployed it at Phu My Fertilizer Plant equipped with MARCHI boiler having capacity of 76-84 ton/h. We found that our system have improved the boiler efficiency about 0.28-1.12% in average and brought benefit about 57.000 USD/year to the Phu My Fertilizer Plant

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.Comment: Accepted to be Published in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) 2018, San Diego, US

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

Complete revascularization in coronary artery bypass grafting with coronary artery endarterectomy: updated findings from Vietnam

We examined the technique and early outcomes of coronary artery bypass graft surgery (CABG) with endarterectomy. In 2023, the single-center database identified 24 severe coronary disease patients undergoing CABG with coronary artery endarterectomy. The patients were in a selected cohort with a minimum of three grafts for the three main vessels. Patients’ mean age was 63.8 years. The mean number of grafts was 4.3. A coronary endarterectomy (CE) was performed on the right coronary artery in 45.8% of patients, the left anterior descending artery in 29.1%, the circumflex artery in 16.6%, and the diagonal artery in 29.1%. Aortic cross-clamp took 147.2 minutes, perfusion 180.9 minutes, mechanical ventilation 18.9 hours, and intensive care unit stay 4.8 days. Our in-hospital mortality rate was 8.3% with no technical complications. To achieve complete revascularization in patients with extensive coronary artery disease, CE should be considered an acceptable adjunct to CABG

Mapping for engagement: setting up a community based participatory research project to reach underserved communities at risk for Hepatitis C in Ho Chi Minh City, Vietnam

Background: Approximately 1. 07 million people in Vietnam are infected with hepatitis C virus (HCV). To address this epidemic, the South East Asian Research Collaborative in Hepatitis (SEARCH) launched a 600-patient cohort study and two clinical trials, both investigating shortened treatment strategies for chronic HCV infection with direct-acting antiviral drugs. We conducted ethnographic research with a subset of trial participants and found that the majority were aware of HCV infection and its implications and were motivated to seek treatment. However, people who inject drugs (PWID), and other groups at risk for HCV were under-represented, although injecting drug use is associated with high rates of HCV. Material and Methods: We designed a community-based participatory research (CBPR) study to engage in dialogues surrounding HCV and other community-prioritized health issues with underserved groups at risk for HCV in Ho Chi Minh City. The project consists of three phases: situation analysis, CBPR implementation, and dissemination. In this paper, we describe the results of the first phase (i.e., the situation analysis) in which we conducted desk research and organized stakeholder mapping meetings with representatives from local non-government and community-based organizations where we used participatory research methods to identify and analyze key stakeholders working with underserved populations. Results: Twenty six institutions or groups working with the key underserved populations were identified. Insights about the challenges and dynamics of underserved communities were also gathered. Two working groups made up of representatives from the NGO and CBO level were formed. Discussion: Using the information provided by local key stakeholders to shape the project has helped us to build solid relationships, give the groups a sense of ownership from the early stages, and made the project more context specific. These steps are not only important preliminary steps for participatory studies but also for other research that takes place within the communities

Meibomian gland lipid alterations and ocular surface sequela in Awat2 knockout murine model of meibomian gland dysfunction and evaporative dry eye disease

PurposeThere is an urgent need for animal models of meibomian gland dysfunction (MGD) and evaporative dry eye disease (EDED) to understand their pathophysiology and investigate novel therapeutics. This study sought to further define the acyl-CoA: wax alcohol acyltransferase 2 knockout (Awat2 KO) mouse as a model of EDED using a combination of novel clinical, biochemical, and biophysical endpoints.MethodsWildtype and Awat2 KO mice between 1 and 18 months of age were used. Ocular examinations and advanced imaging were performed. The lipidomic composition and in situ melting temperature of meibum were determined. qPCR was performed to define ocular surface gene and pro-inflammatory transcript expression. Dynamic contact angle goniometry was performed to assess the adherence capability of the ocular surface.ResultsAwat2 KO mice have mild, white, hyperreflective corneal opacities of the anterior stroma and significantly enlarged apical epithelial cells (P = 0.0004). In Awat2 KO meibum, wax esters were 9-10 times lower than in wildtype meibum. Additionally, meibum melting temperature increased from 32° to 47 °C (P < 0.0001), leading to impaired meibum secretion and dilation of the central duct. Awat2 KO corneal epithelia had significantly decreased mucin expression (Muc1 and Muc4, P = 0.0043) and increased interferon-γ production (P = 0.0303). Awat2 KO globes have a significantly shortened time of droplet adherence to their ocular surface (P = 0.0053), indicating a decreased tear film adherence capacity. Wildtype corneal epithelia does not express Awat2, indicating that the EDED phenotype is secondary to the loss of Awat2 from the meibomian glands.ConclusionsAwat2 KO mice recapitulate many of features of human MGD and EDED, representing a model to test novel therapeutics