Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page

Towards Profit Maximization for Online Social Network Providers

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page

Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection

Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature of cyber attacks, it has been a technical challenge for an intrusion detection system (IDS) to effectively recognize unknown attacks or known attacks with inadequate training data. Therefore in this dissertation work, an innovative two-stage classifier is developed for accurately and efficiently detecting both unknown attacks and known attacks with insufficient or inaccurate training information. The novel two-stage fuzzy classification scheme is based on advanced machine learning techniques specifically for handling the ambiguity of traffic connections and network data. In the first stage of the classification, a fuzzy C-means (FCM) algorithm is employed to softly compute and optimize clustering centers of the training datasets with some degree of fuzziness counting for feature inaccuracy and ambiguity in the training data. Subsequently, a distance-weighted k-NN (k-nearest neighbors) classifier, combined with the Dempster-Shafer Theory (DST), is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes to further address the data uncertainty issue in the cyberattack data. In the second stage of the proposed classification algorithm, a subsequent classification scheme is implemented based on the obtained pignistic probabilities and their entropy functions to determine if the input data are normal, one of the known attacks or an unknown attack. Secondly, to strengthen the robustness to attacks, we form the three-layer hierarchy ensemble classifier based on the FCM weighted k-NN DST classifier to have more precise inferences than those made by a single classifier. The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks. The experimental results show that the new two-stage fuzzy KNN-DST classifier outperforms other well-known classifiers in intrusion detection and is especially effective in detecting unknown attacks

Efficient Approximation Algorithms for Adaptive Seed Minimization

As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number $\eta$ of users in a given social network $G$. Existing algorithms for seed minimization mostly consider the non-adaptive setting, where all seed nodes are selected in one batch without observing how they may influence other users. In this paper, we study seed minimization in the adaptive setting, where the seed nodes are selected in several batches, such that the choice of a batch may exploit information about the actual influence of the previous batches. We propose a novel algorithm, ASTI, which addresses the adaptive seed minimization problem in $O\Big(\frac{\eta \cdot (m+n)}{\varepsilon^2}\ln n \Big)$ expected time and offers an approximation guarantee of $\frac{(\ln \eta+1)^2}{(1 - (1-1/b)^b) (1-1/e)(1-\varepsilon)}$ in expectation, where $\eta$ is the targeted number of influenced nodes, $b$ is size of each seed node batch, and $\varepsilon \in (0, 1)$ is a user-specified parameter. To the best of our knowledge, ASTI is the first algorithm that provides such an approximation guarantee without incurring prohibitive computation overhead. With extensive experiments on a variety of datasets, we demonstrate the effectiveness and efficiency of ASTI over competing methods.Comment: A short version of the paper appeared in 2019 International Conference on Management of Data (SIGMOD '19), June 30--July 5, 2019, Amsterdam, Netherlands. ACM, New York, NY, USA, 18 page

Quantifying oxygen diffusion in bitumen films using molecular dynamics simulations

Bitumen in asphalt pavements reacts slowly with atmospheric oxygen, resulting in oxidative ageing. This oxidative reaction is strongly dependent on the physical diffusion of the oxygen into the bitumen. This study aims to use molecular dynamics (MD) simulation to investigate the oxygen diffusion into the bitumen film and analyse the effects of anti-ageing compounds (AACs) on the oxygen diffusion. The MD diffusion simulations using a Polymer Consistent Force Field (PCFF) were conducted on a bitumen-air bi-layer model at different temperatures. Fick's second law was used to calculate the diffusion coefficient of the oxygen in the bitumen film. It is found that the oxygen diffusion coefficients ranged from 6.67 × 10−10 to 7.45 × 10−11 m2/s for the unmodified and AAC-modified bitumens at the simulating temperatures of 25, 50 and 100 °C. Irganox acid and DLTDP (Dilauryl thiodipropionate):furfural showed two different anti-aging mechanisms, i.e., reducing the oxygen physical diffusion and controlling the chemical oxidative reaction. Reducing the oxygen diffusivity by constructing a network in the bitumen to retard oxygen diffusion and increase the transport path is an efficient way to slow down the bitumen aging without the antioxidant consumption. This work proposed a MD-based computational approach, contributing to 1) determination of the oxygen diffusion coefficient of the existing bitumen that is extremely challenging for the experimental measurement and 2) instruction of developing new antioxidant

Is first pregnancy age associated with hypertension in the Chinese rural women population?

IntroductionThe purpose of this study was to investigate the relationship between first pregnancy age and hypertension later in the life of women from Chinese rural areas.MethodsIn total, 13,493 women were enrolled in the Henan Rural Cohort study. Logistic regression and linear regression were used to evaluate the association between first pregnancy age and hypertension and blood pressure indicators [including systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP)]. The restricted cubic spline was used to examine the dose–response relationship between the first pregnancy age and hypertension or blood pressure indicators.ResultsAfter adjusting for potential confounders, each 1-year increase in first pregnancy age was associated with a 0.221 mmHg increase in SBP values, a 0.153 mmHg increase in DBP values, and a 0.176 mmHg decrease in MAP values (all P < 0.05). The β of SBP, DBP, and MAP showed a trend of first increasing and then decreasing with increasing first pregnancy age and there was no statistical significance after first pregnancy age beyond 33 years on SBP, DBP, and MAP, respectively. A 1-year increment in first pregnancy age was associated with a 2.9% [OR (95% CI): 1.029 (1.010, 1.048)] higher odds of prevalent hypertension. The odds of hypertension increased sharply and then eventually leveled off with an increment of first pregnancy age after adjusting for potential confounders.ConclusionFirst pregnancy age might increase the risk of hypertension later in life and might be an independent risk factor for hypertension in women

Genetic diversity and structure of the 4th cycle breeding population of Chinese fir (Cunninghamia lanceolata (lamb.) hook)

Studying population genetic structure and diversity is crucial for the marker-assisted selection and breeding of coniferous tree species. In this study, using RAD-seq technology, we developed 343,644 high-quality single nucleotide polymorphism (SNP) markers to resolve the genetic diversity and population genetic structure of 233 Chinese fir selected individuals from the 4th cycle breeding program, representing different breeding generations and provenances. The genetic diversity of the 4th cycle breeding population was high with nucleotide diversity (Pi) of 0.003, and Ho and He of 0.215 and 0.233, respectively, indicating that the breeding population has a broad genetic base. The genetic differentiation level between the different breeding generations and different provenances was low (Fst < 0.05), with population structure analysis results dividing the 233 individuals into four subgroups. Each subgroup has a mixed branch with interpenetration and weak population structure, which might be related to breeding rather than provenance, with aggregation from the same source only being in the local branches. Our results provide a reference for further research on the marker-assisted selective breeding of Chinese fir and other coniferous trees

Enhanced Bio-Barcode Immunoassay Using Droplet Digital PCR for Multiplex Detection of Organophosphate Pesticides

A bio-barcode immunoassay based on droplet digital polymerase chain reaction (ddPCR) was developed to simultaneously quantify triazophos, parathion, and chlorpyrifos in apple, cucumber, cabbage, and pear. Three gold nanoparticle (AuNP) probes and magnetic nanoparticle (MNP) probes were prepared, binding through their antibodies with the three pesticides in the same tube. Three groups of primers, probes, templates, and three antibodies were designed to ensure the specificity of the method. Under the optimal conditions, the detection limits (expressed as IC10) of triazophos, parathion, and chlorpyrifos were 0.22, 0.45, and 4.49 ng mL–1, respectively. The linear ranges were 0.01–20, 0.1–100, and 0.1–500 ng mL–1, and the correlation coefficients (R2) were 0.9661, 0.9834, and 0.9612, respectively. The recoveries and relative standard deviations (RSDs) were in the ranges of 75.5–98.9 and 8.3–16.7%. This study provides the first insights into the ddPCR for the determination of organophosphate pesticides. It also laid the foundation for high-throughput detection of other small molecules.This study was financially supported by the National Key Research Program of China (No. 2019YFC1604503), the NIEHS Superfund Research Program (No. P42 ES04699), the Central Public-interest Scientific Institution Basal Research Fund for the Chinese Academy of Agricultural Sciences (No. Y2021PT05), and the Ningbo Innovation Project for Agro-Products Quality and Safety (No. 2019CXGC007).Peer reviewe