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

Using Online Games in Teaching: A Bibliometric Analysis

This paper aims to study an overview of the using online games in teaching based on the Scopus data source from January 2016 to July 2023. The PRISMA model is used to guide the selection of articles. After selection and consideration, 19 scientific articles were included for analysis. The author analyzes information such as number of research articles, country, author, citations, keywords. The results showed that the number of research articles on the use of online games in teaching is increasing, in which the article “Using online game-based platforms to improve student performance and engagement in histology teaching" of the authors Felszeghy S. et al. (2019) most influential with a citation index of 81. Student, learning, online, games are keywords that often appear in the articles analyzed. Therefore, through systematic review research to help educational researchers, teachers identify important information about the use of online games in teaching so that they can guide future studies

Influence maximization under fairness budget distribution in online social networks

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community's elements is selected into a seed set holding these bounds. Nevertheless, resolving this problem encounters two main challenges: strongly influential seed sets might not adhere to the fairness constraint, and it is an NP-hard problem. To address these shortcomings, we propose three algorithms (FBIM1, FBIM2, and FBIM3). These algorithms combine an improved greedy strategy for selecting seeds to ensure maximum coverage with the fairness constraints by generating sampling through a Reverse Influence Sampling framework. Our algorithms provide a (1/2 - epsilon)-approximation of the optimal solution, and require O(kT log ((8 + 2 epsilon)n ln + 2/delta + ln(nk)/epsilon(2))), O(kT log n/epsilon(2)k), and O(T/epsilon log k/epsilon log n/epsilon(2)k) complexity, respectively. We conducted experiments on real social networks. The result shows that our proposed algorithms are highly scalable while satisfying theoretical assurances, and that the coverage ratios with respect to the target communities are larger than those of the state-of-the-art alternatives; there are even cases in which our algorithms reaches 100% coverage with respect to target communities. In addition, our algorithms are feasible and effective even in cases involving big data; in particular, the results of the algorithms guarantee fairness constraints.Web of Science1022art. no. 418

Efficient streaming algorithms for maximizing monotone DR-submodular function on the integer lattice

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of publications show that the DR-submodular function has wide applications in optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. In this research, we propose two main streaming algorithms for the problem of maximizing a monotone DR-submodular function under cardinality constraints. Our two algorithms, which are called StrDRS1 and StrDRS2, have (1/2 - epsilon) , (1 - 1 /e - epsilon) of approximation ratios and O(n/epsilon log(log B/epsilon ) log k), O(n/epsilon log B), respectively. We conducted several experiments to investigate the performance of our algorithms based on the budget allocation problem over the bipartite influence model, an instance of the monotone submodular function maximization problem over the integer lattice. The experimental results indicate that our proposed algorithms not only provide solutions with a high value of the objective function, but also outperform the state-of-the-art algorithms in terms of both the number of queries and the running time.Web of Science1020art. no. 377

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

A contactless single-step process for simultaneous nanoscale patterning and cleaning of large-area graphene

The capability to structure two-dimensional materials (2DMs) at the nanoscale with customizable patterns and over large areas is critical for a number of emerging applications, from nanoelectronics to 2D photonic metasurfaces. However, current technologies, such as photo- and electron-beam lithography, often employing masking layers, can significantly contaminate the materials. Large-area chemical vapour deposition-grown graphene is known to have non-ideal properties already due to surface contamination resulting from the transferring process. Additional contamination through the lithographic process might thus reduce the performance of any device based on the structured graphene. Here, we demonstrate a contactless chemical-free approach for simultaneous patterning and cleaning of self-supporting graphene membranes in a single step. Using energetic ions passing through a suspended mask with pre-defined nanopatterns, we deterministically structure graphene with demonstrated feature size of 15 nm, approaching the performance of small-area focused ion beam techniques and extreme ultraviolet lithography. Our approach, however, requires only a broad beam, no nanoscale beam positioning and enables large area patterning of 2DMs. Simultaneously, in regions surrounding the exposed areas, contaminations commonly observed on as-grown graphene targets, are effectively removed. This cleaning mechanism is attributed to coupling of surface diffusion and sputtering effects of adsorbed surface contaminants. For applications using 2DMs, this simultaneous patterning and cleaning mechanism may become essential for preparing the nanostructured materials with improved cleanliness and hence, quality

Multiple benefit thresholds problem in online social networks: An algorithmic approach

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product's information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T = {T-1, T-2, ..., T-k}, Ti > 0, the issue aims to seek the seed sets S-1, S-2, ..., Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T-1, T-2, ..., T-k, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.Web of Science106art. no. 87

Classification of Vascular Plants in Vietnam According to Modern Classification Systems

Vietnam is extremely rich in biodiversity, with a remarkable range of habitats and more than 13,500 species of vascular plants recorded for the flora of Vietnam. This number represents about 3 to 5% of the world’s diversity of vascular plants. Over the past 30 years, there were two important documents on the vascular plants of Vietnam published, An Illustrated Flora of Vietnam (IFV) and Checklist of Plant Species of Vietnam (CPSV). During the past half century, the advent of molecular phylogenetics has witnessed dramatic changes in the classifications of vascular plants, and some modern classification systems of vascular plants have been established, e.g., PPG I, GPG, and APG. However, the vascular plants of Vietnam have not yet been classified according to these modern classification systems. In this paper, we present the history of the classification of vascular plants in Vietnam, compare the circumscription of all families of vascular plants occurring within Vietnam in IFV, CPSV, and the modern classification systems when applicable, and summarize familial assignments of all controversial genera in the different classifications. Furthermore, we also arrange the 37 families of lycophytes and ferns occurring within Vietnam according to the latest classification system (PPG I) and the 8 families of gymnosperms according to the latest Christenhusz’s system (GPG). The 246 families of angiosperms are arranged according to the fourth edition of the latest Angiosperm Phylogeny Group (APG IV). These results are the foundation stones and would be helpful for future research on the flora of Vietnam and the arrangement of plant collections in Vietnamese herbaria based on the updated classifications

Excellent Fireproof Characteristics and High Thermal Stability of Rice Husk-Filled Polyurethane with Halogen-Free Flame Retardant

The thermal stabilities, flame retardancies, and physico-mechanical properties of rice husk-reinforced polyurethane (PU–RH) foams with and without flame retardants (FRs) were evaluated. Their flammability performances were studied by UL94, LOI, and cone calorimetry tests. The obtained results combined with FTIR, TGA, SEM, and XPS characterizations were used to evaluate the fire behaviors of the PU–RH samples. The PU–RH samples with a quite low loading (7 wt%) of aluminum diethylphosphinate (OP) and 32 wt% loading of aluminum hydroxide (ATH) had high thermal stabilities, excellent flame retardancies, UL94 V-0 ratings, and LOIs of 22%–23%. PU–RH did not pass the UL94 HB standard test and completely burned to the holder clamp with a low LOI (19%). The cone calorimetry results indicated that the fireproof characteristics of the PU foam composites were considerably improved by the addition of the FRs. The proposed flame retardancy mechanism and cone calorimetry results are consistent. The comprehensive FTIR spectroscopy, TG, SEM, and XPS analyses revealed that the addition of ATH generated white solid particles, which dispersed and covered the residue surface. The pyrolysis products of OP would self-condense or react with other volatiles generated by the decomposition of PU–RH to form stable, continuous, and thick phosphorus/aluminum-rich residual chars inhibiting the transfer of heat and oxygen. The PU–RH samples with and without the FRs exhibited the normal isothermal sorption hysteresis effect at relative humidities higher than 20%. At lower values, during the desorption, this effect was not observed, probably because of the biodegradation of organic components in the RH. The findings of this study not only contribute to the improvement in combustibility of PU–RH composites and reduce the smoke or toxic fume generation, but also solve the problem of RHs, which are abundant waste resources of agriculture materials leading to the waste disposal management problems

Table_2_Anti-ultraviolet, antibacterial, and biofilm eradication activities against Cutibacterium acnes of melanins and melanin derivatives from Daedaleopsis tricolor and Fomes fomentarius.DOCX

Fomes fomentarius and Daedaleopsis tricolor produced significant amounts of water-insoluble melanins, and our previous study successfully enhanced their water solubility by arginine modification. This research aimed to investigate the anti-ultraviolet, antibacterial, and biofilm eradication activities of both the melanins and arginine-modified melanin (melanin derivatives) from these two fungi against an acne-causing bacterium (Cutibacterium acnes). Apart from these, the cytotoxicity of the melanins and melanin derivatives on human skin cells was also evaluated. Melanin derivatives of both two fungi showed significantly higher antibacterial and biofilm eradication activities compared with their original forms. Specifically, the MIC50 values of the melanin derivatives (1,000 μg/mL) are the same as those of erythromycin. Regarding biofilm eradication capacity, the MBEC50 value of D. tricolor melanin derivative (250 μg/mL) was just half of both erythromycin and F. fomentarius melanin derivative. However, it required a 2-fold higher concentration of melanin derivatives than erythromycin to inhibit 90% of the bacterial population and eradicate 90% of their biofilm. Regarding anti-ultraviolet activity, blending melanins or melanin derivatives with a moisturizer/sunscreen enhanced their UV light absorption and the sun protection factor (SPF) values. In addition, melanins showed better effects than their derivatives, and those of D. tricolor were better than F. fomentarius. Remarkably, adding D. tricolor melanin (10%) to a Nivea pure cream could turn this cream into a broad-spectrum sunscreen, with its SPF value and critical wavelength increasing from 7.74 and 338.67 to 14.02 and 377.0, respectively. In addition, adding melanin or a melanin derivative of D. tricolor to an Olay sunscreen enhanced the SPF and the critical wavelength of the sunscreen from 17.25 and 371.67 to 23.82 and 374 and 23.38 and 372, respectively. Notably, melanins and melanin derivatives showed no toxicity in human fibroblasts. The obtained data suggest that arginine modification significantly enhanced the antibacterial and biofilm eradication activities of melanins from D. tricolor and F. fomentarius. However, this is not the case when it comes to their anti-ultraviolet activities. In addition, melanin and melanin derivatives from D. tricolor are potential candidates for anti-acne sunscreen products and are worth further investigation.</p