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

Depression, anxiety and stress among healthcare workers in the context of the COVID-19 pandemic: a cross-sectional study in a tertiary hospital in Northern Vietnam

IntroductionThe outbreak of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) had significant effects on the mental well-being in general, particularly for healthcare professionals. This study examined the prevalence of depression, anxiety, and stress, and identified the associated risk factors amongst healthcare workers during the COVID-19 outbreak in a tertiary hospital located in Vietnam.MethodsWe conducted a cross-sectional study at a tertiary-level hospital, where the Depression Anxiety and Stress Scale 21 (DASS-21) web-based questionnaire was employed. We analyzed the determinant factors by employing multivariate logistic models.ResultsThe prevalence of depression, anxiety, and stress symptoms were 19.2%, 24.7%, and 13.9%, respectively. Factors such as engaging in shift work during the pandemic, taking care of patients with COVID-19, and staff’s health status were associated with mental health issues among health professionals. In addition, having alternate rest periods was likely to reduce the risk of stress.ConclusionThe prevalence of mental health problems in healthcare workers during the COVID-19 pandemic was relatively high. Having resting periods could potentially mitigate the development of stress among health professionals. Our findings could be taken into account for improving mental health of the health professional population

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

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

Drug-Resistant Aspergillus flavus Is Highly Prevalent in the Environment of Vietnam: A New Challenge for the Management of Aspergillosis?

The burden of aspergillosis, especially Chronic Pulmonary Aspergillosis, is increasingly recognized, and the increasing presence of azole-resistant environmental Aspergillus fumigatus has been highlighted as a health risk. However, a sizable minority of aspergillosis is caused by Aspergillus flavus, which is assumed to be sensitive to azoles but is infrequently included in surveillance. We conducted environmental sampling at 150 locations in a rural province of southern Vietnam. A. flavus isolates were identified morphologically, their identity was confirmed by sequencing of the beta-tubulin gene, and then they were tested for susceptibility to azoles and amphotericin B according to EUCAST methodologies. We found that over 85% of A. flavus isolates were resistant to at least one azole, and half of them were resistant to itraconazole. This unexpectedly high prevalence of resistance demands further investigation to determine whether it is linked to agricultural azole use, as has been described for A. fumigatus. Clinical correlation is required, so that guidelines can be adjusted to take this information into account

Ethical principles for artificial intelligence in education

Abstract The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students’ learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite the recent announcement of guidelines for ethical and trustworthy AIED, the debate revolves around the key principles underpinning ethical AIED. This paper aims to explore whether there is a global consensus on ethical AIED by mapping and analyzing international organizations’ current policies and guidelines. In this paper, we first introduce the opportunities offered by AI in education and potential ethical issues. Then, thematic analysis was conducted to conceptualize and establish a set of ethical principles by examining and synthesizing relevant ethical policies and guidelines for AIED. We discuss each principle and associated implications for relevant educational stakeholders, including students, teachers, technology developers, policymakers, and institutional decision-makers. The proposed set of ethical principles is expected to serve as a framework to inform and guide educational stakeholders in the development and deployment of ethical and trustworthy AIED as well as catalyze future development of related impact studies in the field

Azole-resistant Aspergillus fumigatus is highly prevalent in the environment of Vietnam, with marked variability by land use type

Azole-resistant environmental Aspergillus fumigatus presents a threat to public health but the extent of this threat in Southeast Asia is poorly described. We conducted environmental surveillance in the Mekong Delta region of Vietnam, collecting air and ground samples across key land-use types, and determined antifungal susceptibilities of Aspergillus section Fumigati (ASF) isolates and azole concentrations in soils. Of 119 ASF isolates, 55% were resistant (or non-wild type) to itraconazole, 65% to posaconazole and 50% to voriconazole. Azole resistance was more frequent in A. fumigatus sensu stricto isolates (95%) than other ASF species (32%). Resistant isolates and agricultural azole residues were overrepresented in samples from cultivated land. cyp51A gene sequence analysis showed 38/56 resistant A. fumigatus sensu stricto isolates carried known resistance mutations, with TR34/L98H most frequent (34/38)

Projected Evolution of Drought Characteristics in Vietnam based on CORDEX-SEA Downscaled CMIP5 Data

In this study, the projected drought characteristics over Vietnam for the future periods of the middle (2046–2065) and end of the 21st century (2080–2099) were investigated under the Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. The drought characteristics (duration, severity, intensity, inter-arrival time, and geographic extent) were estimated based on the Palmer Drought Severity Index (PDSI). The PDSI was calculated using temperature and precipitation data from six regional climate downscaling experiments and their ensemble conducted by the Coordinated Regional Climate Downscaling Experiment-Southeast Asia (CORDEX-SEA) project. Projected changes of drought characteristics in the future periods were determined with respect to those in the baseline period 1986–2005. Results show biases in the regional climate model (RCM) outputs, namely an underestimation of temperature and an overestimation of precipitation, which also affect the representation of drought characteristics by overestimating the PDSI. In terms of projections, substantial increases of drought duration, severity and intensity, and decreases in the inter-arrival time are found over the Red River Delta, northern parts of the North Central sub-region, parts of the Central Highlands and over southern Vietnam. The droughts are projected to be more widespread under scenario RCP8.5 than RCP4.5, especially in southern Vietnam. With the increasing likelihood of droughts in Vietnam as a result of climate change, sustainable water resources management should be taken into account for agriculture, natural ecosystems and social development