A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Magnetic nanoparticles embedded in microlasers for controlled transport in different sensing media

In recent years, whispering gallery mode microlasers have attracted tremendous interest in sensing due to their ultra-high sensitivity at atomic levels. However, due to the non-magnetic properties, it is difficult to locate the microlasers at hard-to-reach positions, thus, limiting their sensing potential in many in-vitro and in-vivo applications. In this work, we report magnetic microlasers fabricated by encapsulating Ni0.2Zn0.8Fe2O4 magnetic nanoparticles (MNPs) within their cavity made of bovine serum albumin. The presence of MNPs allows the transportable actuation of the magnetic microlasers while maintaining lasing emission characteristics. Microlasers with various concentrations of MNPs are investigated to identify the optimum concentration that can balance a good magnetization, a low lasing threshold, and a high quality (Q) factor. These magnetic microlasers can be employed for sensing applications where sensors need to be navigated through different sensing media. As a proof of concept, we observed a clear shift of lasing wavelength of a magnetic microlaser while dragging it through different adjacent media by magnetic navigation. This result demonstrates the potential applications of magnetic microlasers for future biological and chemical applications.11Nsciescopu

Morbidity and Mortality Patterns in Children Admitted to Hospital in Thai Binh, Vietnam: A Five-year Descriptive Study with a Focus on Infectious Diseases

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Portable and non-invasive blood glucose monitoring over a prolonged period using whispering gallery modes at 2.4 GHz

Invasive measurement of blood glucose is not appropriate for everyone, particularly the patients with leukemia. Here, we demonstrate how the blood glucose can be non-invasively monitored over a prolonged period in the absence of any expensive equipment. Method: A portable and non-invasive glucose sensor capable of monitoring blood glucose at real-time has been successfully constructed and tested in the absence of any vector network analyzer. Using vacuum suction, the sensor head of the proposed non-invasive glucose sensor forms a whispering gallery resonator out of a skin tissue on an arm during the measurement process. The architecture of the proposed glucose sensor is equipped with standard components, including a WiFi transmitter, an RSSI sensor and a microcontroller based computer display. Results: Using the proposed glucose sensor, a healthy volunteer has been his blood glucose levels monitored over 72 minutes after consuming a loaf of bread and a cup of cow milk. The measured blood glucose rose shortly after the meal until it peaked at 40 minutes and finally fell to the initial value at around 72 minutes. Conclusion: The overall results were in general consistent with the expected results. The proposed glucose sensor is expected to be instrumental for the individuals who dislike the traditional lancets