Aproximační algoritmy pro submodulární optimalizaci a aplikace

This study proposes approximation algorithms by using several strategies such as streaming, improved-greedy, stop-and-stare, and reverse influence sampling ( \RIS ) to solve three variants of the submodular optimization problem, and perform experiments of these algorithms on the well-known application problems of submodular optimization such as Influence Threshold ( \IT ) and Influence Maximization ( \IM) . Specifically, in the first problem, we propose the two single-pass streaming algorithms ( \StrA and \StrM ) for minimizing the cost of the submodular cover problem under the multiplicative and additive noise models. \StrA and \StrM provide bicriteria approximation solutions. These algorithms effectively increase performance computing the objective function, reduce complexity, and apply to big data. For the second problem, we focus on maximizing a submodular function on fairness constraints. This problem is also known as the problem of fairness budget distribution for influence maximization. We design three algorithms ( \FBIM1 , \FBIM2 , and \FBIM3 ) by combining several strategies such as the threshold greedy algorithm, dynamic stop-and-stare technique, generating samplings by reverse influence sampling framework, and seeds selection to ensure max coverage. \FBIM1 , \FBIM2 , and \FBIM3 perform effectively on big data, provide $(1/2-\epsilon)$-approximation to the optimum solutions, and require complexities of the comparison algorithms. Finally, we devise two effective streaming algorithm ( \StrI and \StrII ) to maximize the Diminishing Returns submodular (DR-submodular) function with a cardinality constraint on the integer lattice for the third problem. \StrI and \StrII provide $(1/2-\epsilon)$-approximation ratio and $(1-1/e-\epsilon)$-approximation ratio, respectively. Simultaneously, compared with the state-of-the-art, these two algorithms have reduced complexity, superior runtime performance, and negligible difference in objective function values. In each problem, we further investigate the performance of our proposed algorithms by conducting many experiments. The experimental results have indicated that our approximation algorithms provide high-efficiency solutions, outperform the-state-of-art algorithms in complexity, runtime, and satisfy the specified constraints. Some of the results have been confirmed through five publications at the Scopus international conferences (RIVF 2021, ICABDE 2021) and the SCIE journals (Computer Standards $\&$ Interfaces (Elsevier) and Mathematics (MDPI)).Tato studie navrhuje aproximační algoritmy pomocí několika strategií, jako je streamování, vylepšená chamtivost, stop-and-stare a vzorkování zpětného vlivu ( \RIS ) k vyřešení tří variant submodulárního optimalizačního problému a provádění experimentů s těmito algoritmy na dobře známé aplikační problémy submodulární optimalizace, jako je práh vlivu ( \IT ) a maximalizace vlivu ( \IM) . Konkrétně v prvním problému navrhujeme dva jednoprůchodové streamovací algoritmy ( \StrA a \StrM ) pro minimalizaci nákladů na problém submodulárního pokrytí v rámci multiplikativních a aditivních šumových modelů. \StrA a \StrM poskytují řešení aproximace bikriterií. Tyto algoritmy efektivně zvyšují výkon při výpočtu cílové funkce, snižují složitost a aplikují se na velká data. U druhého problému se zaměřujeme na maximalizaci submodulární funkce na omezeních spravedlnosti. Tento problém je také známý jako problém spravedlivého rozdělení rozpočtu pro maximalizaci vlivu. Navrhujeme tři algoritmy ( \FBIM1 , \FBIM2 a \FBIM3 ) kombinací několika strategií, jako je prahový greedy algoritmus, dynamická technika stop-and-stare, generování vzorkování pomocí rámce vzorkování s obráceným vlivem a semena výběr pro zajištění maximálního pokrytí. \FBIM1 , \FBIM2 a \FBIM3 fungují efektivně na velkých datech, poskytují $(1/2-\epsilon)$-přiblížení optimálním řešením a vyžadují složitost srovnávacích algoritmů. Nakonec jsme navrhli dva efektivní streamovací algoritmy ( \StrI a \StrII ), abychom maximalizovali submodulární (DR-submodulární) funkci klesající návraty s omezením mohutnosti na celočíselné mřížce pro třetí problém. \StrI a \StrII poskytují poměr přiblížení $(1/2-\epsilon)$ a poměr přiblížení $(1-1/e-\epsilon)$. Současně mají tyto dva algoritmy ve srovnání s nejmodernějšími algoritmy sníženou složitost, vyšší výkon za běhu a zanedbatelný rozdíl v hodnotách objektivních funkcí. V každém problému dále zkoumáme výkon námi navrhovaných algoritmů prováděním mnoha experimentů. Experimentální výsledky ukázaly, že naše aproximační algoritmy poskytují vysoce účinná řešení, překonávají nejmodernější algoritmy ve složitosti, době běhu a splňují specifikovaná omezení. Některé z výsledků byly potvrzeny prostřednictvím pěti publikací na mezinárodních konferencích Scopus (RIVF 2021, ICABDE 2021) a v časopisech SCIE (Computer Standards $\&$ Interfaces (Elsevier) a Mathematics (MDPI)).460 - Katedra informatikyvyhově

Structures of 2,5-diaryl- and 2,3,5,6-tetra[3,2-b]thiophene synthesized by the palladium-catalyzed Suzuki-Miyaura cross-coupling reaction

The crystal structures of 2,5-di(ethoxyphenyl)-3,6-dibromothieno[3,2-b]thiophene (I) and 2,5-di(ethoxyphenyl)-3,6-diphenylthieno[3,2-b]thiophene (II) have been studied in order to evaluate the planarity of these molecules. The aromatic systems introduced to the thieno[3,2-b]thiophene core structure show a degree of rotation from 30.94° to 66.56°. The crystal packing of (I) are characterized by π×××π stacking, while in (II), C-H×××p and C-H×××O interactions are observed

SOCIAL-EMOTIONAL SKILLS IN STUDENTS’ LEARNING IN CAN THO UNIVERSITY, VIETNAM

Based on an overview of domestic and foreign research related to the research field of the topic; the research of the topic has determined the urgency of the topic, research objectives, research objects and scope, research hypotheses, research tasks, and methods. The research on the topic has systematized the theoretical basis for the concept and manifestation of social-emotional skills in student learning: generalizing the picture of the current situation of recognizing the importance of social-emotional skills in student learning, the level of assessment of the manifestation and educational measures of social-emotional skills in learning, and finding out the factors that affect social emotions in student learning such as from the external environment, at school, family and external social relationships. Factors from the internal environment are the self-awareness of each student. From the current situation of the problem, the study has proposed educational measures for 504 students from 8 schools and colleges in Can Tho University (CTU), Vietnam. Some measures were to educate social-emotional skills for students through soft skills topics, teach soft skills to students through extracurricular activities, and soft skills education for students through integrated teaching.  Article visualizations

Musculoskeletal Pain and Work-related Risk Factors among Waste Collectors in Hanoi, Vietnam: A Cross-sectional Study

BACKGROUND: Musculoskeletal disorders (MSDs) are prevalent among waste collectors (WCs) in developing countries. AIM: This study aimed to investigate the prevalence of MSDs and the factors associated with the risk of persistent musculoskeletal pain among WCs in Hanoi, Vietnam. METHODS: A cross-sectional survey was utilized to study 468 WCs in 2017. The Örebro Musculoskeletal Pain Questionnaire and a questionnaire on demographic and work conditions were used to collect data. Descriptive and multivariate logistics regression analyzes were applied at a significance level of 0.05 to examine the factors related to the risk of persistent pain. FINDINGS: About 74.4% of the participants of this study experienced MSDs in at least one body region and 9.4% reported MSDs in all 10 body sites. The lower back was reported to be the most affected followed by the neck and shoulders. The risk of persistent musculoskeletal pain was significantly associated with age (odds ratio (OR) = 2.31, confidence interval (CI) = 1.05–5.09), gender (OR = 3.29, CI = 1.28–8.44), work hours (OR = 2.35, CI = 1.12–4.92), work shift (OR = 0.48, CI = 0.26–0.92), duration of poor postures of the neck (OR = 0.31, CI = 0.13–0.76), bent back (OR = 0.4 CI = 0.18–0.92) and for medial rotation (OR = 3.01, CI = 1.42–6.36), carrying heavy objects (OR = 2.94, CI = 1.15–7.48), and experience of work dissatisfaction (OR = 3.31, CI = 1.46-7.52), stress (OR = 7.14, CI = 3.14–16.24), or anxiety (OR = 6.37, CI = 3.07–13.21). CONCLUSIONS: High prevalence of MSDs among WCs and its association with self-assessed unfavorable work postures and work-related stress implies the need of mechanical and social support at work for WC to prevent the development of MSDs and persistent pain

Structural basis for the influence of a single mutation K145N on the oligomerization and photoswitching rate of Dronpa

The crystal structure of the on-state of PDM1-4, a single-mutation variant of the photochromic fluorescent protein Dronpa, is reported at 1.95 angstrom resolution. PDM1-4 is a Dronpa variant that possesses a slower off-switching rate than Dronpa and thus can effectively increase the image resolution in subdiffraction optical microscopy, although the precise molecular basis for this change has not been elucidated. This work shows that the Lys145Asn mutation in PDM1-4 stabilizes the interface available for dimerization, facilitating oligomerization of the protein. No significant changes were observed in the chromophore environment of PDM1-4 compared with Dronpa, and the ensemble absorption and emission properties of PDM1-4 were highly similar to those of Dronpa. It is proposed that the slower off-switching rate in PDM1-4 is caused by a decrease in the potential flexibility of certain beta-strands caused by oligomerization along the AC interface

Network alignment across social networks using multiple embedding techniques

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10-15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.Web of Science1021art. no. 397

Synthesis of cuprous oxide nanocubes combined with chitosan nanoparticles and its application to p-nitrophenol degradation

For the first time, cuprous oxide nanocubes (Cu2O NCBs) were successfully combined with chitosan nanoparticles (CS NPs) to generate Cu2O NCBs/CS NPs composites material with highly optical property and photocatalytic activity using a simple and eco-friendly synthetic approach at room temperature for 30 min. The synthesized Cu2O NCBs NPs/CS NPs were determined characterizations by Ultraviolet-visible spectroscopy (UV-vis), Fourier transform infrared spectroscopy (FTIR), X – ray Diffraction (XRD),  Transmission Electron Microscope (TEM) and Energy-dispersive X-ray spectroscopy (EDX). Results show that the Cu2O NCBs/CS NPs composites have an average particle size of ~3-5 nm; in which, Cu2O has the form of nanocubes (Cu2O NCBs) with size ~3-4 nm and chitosan nanoparticles with spherical shape (CS NPs) with size ~4-5 nm. In addition, the percent (%) composition of elements present in Cu2O NCBs/CS NPs composites material have been obtained respective: Cu (23.99%), O (38.18%), and C (33.61%). Moreover, Cu2O NCBs/CS NPs composites material was also investigated for photocatalytic activity applied in p-nitrophenol degradation. The obtained results showed that the catalytic capability of Cu2O NCBs/CS NPs for p-nitrophenol reduction reached the highest efficiency >55% in the treatment time of 25 min, and this efficiency was higher than that result of using ZnO@chitosan nanoparticles (ZnO@CS NPs) catalyst under the same conditions for comparison

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