Overexposure-aware influence maximization

Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product, after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, LAICO (Latency Aware Independent Cascade Model with Overexposure), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm which exploits monotone submodular upper and lower bound functions of spread, and a heuristic which aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms

Assessing the drivers of online impulse buying

Given the rapidly growing popularity of online impulse buying using digital and social media platforms, it has raised important interests about the antecedents of such consumer behaviour. Data analysis was conducted using confirmatory factor analysis and structural equation modelling. The results from a survey of 310 online buyers suggest that trust holds the strongest correlation with the experiential value. This study provides new insights for marketing literature and online retailers

Examining the influences of service quality and corporate image on students' loyalty of boarding schools in the UK: A study of Chinese students

The purpose of this study is to explore the impact of service quality and corporate image on students’ loyalty of boarding schools in the UK. Valid responses obtained from Chinese students in British boarding schools (n =300) were analysed using structural equation modelling. The results of the empirical research on Chinese students reveal how students’ loyalty relies on the level of service quality and corporate image. Additionally, corporate image mediates the relationship between service quality and students’ loyalty. Different from previous studies that investigated service quality in the higher education sector, this study extends the extant literature by examining the importance of corporate image on boarding schools. The results also corroborate the applicability of the five dimensions of SERVQUAL model to the boarding school sector

Text Indexing for Long Patterns: Anchors are All you Need

PVLDB Artifact Availability: The source code, data, and/or other artifacts have been made available at https://github.com/lorrainea/BDA- index.Copyright © 2023 the owner/author(s). In many real-world database systems, a large fraction of the data is represented by strings: sequences of letters over some alphabet. This is because strings can easily encode data arising from different sources. It is often crucial to represent such string datasets in a compact form but also to simultaneously enable fast pattern matching queries. This is the classic text indexing problem. The four absolute measures anyone should pay attention to when designing or implementing a text index are: (i) index space; (ii) query time; (iii) construction space; and (iv) construction time. Unfortunately, however, most (if not all) widely-used indexes (e.g., suffix tree, suffix array, or their compressed counterparts) are not optimized for all four measures simultaneously, as it is difficult to have the best of all four worlds. Here, we take an important step in this direction by showing that text indexing with locally consistent anchors (lc-anchors) offers remarkably good performance in all four measures, when we have at hand a lower bound l on the length of the queried patterns --- which is arguably a quite reasonable assumption in practical applications. Specifically, we improve on the construction of the index proposed by Loukides and Pissis, which is based on bidirectional string anchors (bd-anchors), a new type of lc-anchors, by: (i) designing an average-case linear-time algorithm to compute bd-anchors; and (ii) developing a semi-external-memory implementation to construct the index in small space using near-optimal work. We then present an extensive experimental evaluation, based on the four measures, using real benchmark datasets. The results show that, for long patterns, the index constructed using our improved algorithms compares favorably to all classic indexes: (compressed) suffix tree; (compressed) suffix array; and the FM-index.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No 872539 and 956229, respectively; and by UKRI through REPHRAIN (EP/V011189/1)

Bidirectional string anchors: A new string sampling mechanism

The minimizers sampling mechanism is a popular mechanism for string sampling introduced independently by Schleimer et al. [SIGMOD 2003] and by Roberts et al. [Bioinf. 2004]. Given two positive integers w and k, it selects the lexicographically smallest length-k substring in every fragment of w consecutive length-k substrings (in every sliding window of length w+k-1). Minimizers samples are approximately uniform, locally consistent, and computable in linear time. Although they do not have good worst-case guarantees on their size, they are often small in practice. They thus have been successfully employed in several string processing applications. Two main disadvantages of minimizers sampling mechanisms are: first, they also do not have good guarantees on the expected size of their samples for every combination of w and k; and, second, indexes that are constructed over their samples do not have good worst-case guarantees for on-line pattern searches. To alleviate these disadvantages, we introduce bidirectional string anchors (bd-anchors), a new string sampling mechanism. Given a positive integer , our mechanism selects the lexicographically smallest rotation in every length- fragment (in every sliding window of length ). We show that bd-anchors samples are also approximately uniform, locally consistent, and computable in linear time. In addition, our experimen

All-pairs suffix/prefix in optimal time using Aho-Corasick space

The all-pairs suffix/prefix (APSP) problem is a classic problem in computer science with many applications in bioinformatics. Given a set {S1,…,Sk} of k strings of total length n, we are asked to find, for each string Si, i∈[1,k], its longest suffix that is a prefix of string Sj, for all j≠i, j∈[1,k]. Several algorithms running in the optimal O(n+k2) time for solving APSP are known. All of these algorithms are based on suffix sorting and thus require space Ω(n) in any case. We consider the parameterized version of the APSP problem, denoted by ℓ-APSP, in which we are asked to output only the pairs whose suffix/prefix overlap is of length at least ℓ. We give an algorithm for solving ℓ-APSP that runs in the optimal O(n+|OUTPUTℓ|) time using O(n) space, where OUTPUTℓ is the set of output pairs. Our algorithm is thus optimal for the APSP problem as well by setting ℓ=0. Notably, our algorithm is fundamentally different from all optimal algorithms solving the APSP problem: it does not rely on sorting the suffixes of all input strings but on a novel traversal of the Aho-Corasick machine, and it thus requires space linear in the size of the machine

Text indexing for long patterns: Anchors are all you need

In many real-world database systems, a large fraction of the data is represented by strings: Sequences of letters over some alphabet. This is because strings can easily encode data arising from different sources. It is often crucial to represent such string datasets in a compact form but also to simultaneously enable fast pattern matching queries. This is the classic text indexing problem. The four absolute measures anyone should pay attention to when designing or implementing a text index are: (ⅰ) index space; (ⅱ) query time;(ⅲ) construction space; and (iv) construction time. Unfortunately, however, most (if not all) widely-used indexes (e.g., suffix tree, suffix array, or their compressed counterparts) are not optimized for all four measures simultaneously, as it is difficult to have the best of all four worlds. Here, we take an important step in this direction by showing that text indexing with locally consistent anchors (lc-anchors) offers remarkably good performance in all four measures, when we have at hand a lower bound ℓ on the length of the queried patterns — which is arguably a quite reasonable assumption in practical applications. Specifically, we improve on the construction of the index proposed by Loukides and Pissis, which is based on bidirectional string anchors (bd-anchors), a new type of lc-anchors,by: (i) designing an average-case linear-time algorithm to compute bd-anchors; and (ii) developing a semi-external-memory implementation to construct the index in small space using near-optimal work. We then present an extensive experimental evaluation, based on the four measures, using real benchmark datasets. The results show that, for long patterns, the index constructed using our improved algorithms compares favorably to all classic indexes: (compressed) suffix tree; (compressed) suffix array; and the FM-index

Vascular endothelial growth factor and cysteinyl leukotrienes in sputum supernatant of patients with asthma

SummaryBackgroundVascular endothelial growth factor (VEGF) is considered to be the most important angiogenic factor in asthma. Cysteinyl leukotrienes (Cyst-LTs) have been implicated in vascular permeability in asthma. Cyst-LTs receptor antagonists modulate vascular permeability by reducing VEGF expression.ObjectiveWe aimed to determine the levels of VEGF and Cyst-LTs in sputum supernatants of patients with asthma and to investigate possible associations within them and with airway vascular permeability (AVP) index. Possible confounding factors were also assessed.MethodsOne hundred twenty one patients with asthma (38 with severe refractory asthma, 41 smokers) and 30 healthy subjects (15 smokers) were studied. All subjects underwent lung function tests, and sputum induction for cell count identification and VEGF, Cyst-LTs, measurement in supernatants. AVP index was also assessed.ResultsBoth VEGF & Cyst-LTs (pg/ml) levels were significantly elevated in patients with asthma compared to normal subjects (median, interquartile ranges 845 [487–1034] vs. 432 (327–654) and 209 [171–296] vs. 92 [75–114] respectively, p < 0.001 for both). Multivariate regression analysis in the whole group showed a significant association of Cyst-LTs levels in sputum supernatants with VEGF levels in sputum supernatants and AVP index. A similar positive association was observed between VEGF levels in sputum supernatants and AVP index. The presence of Severe asthma was a significant covariate for both associations.ConclusionOur results indicate that Cyst-LTs may modulate vascular permeability by up-regulating VEGF expression. The above effect seems to be affected by asthma severity

Clustering sequence graphs

In application domains ranging from social networks to e-commerce, it is important to cluster users with respect to both their relationships (e.g., friendship or trust) and their actions (e.g., visited locations or rated products). Motivated by these applications, we introduce here the task of clustering the nodes of a sequence graph, i.e., a graph whose nodes are labeled with strings (e.g., sequences of users’ visited locations or rated products). Both string clustering algorithms and graph clustering algorithms are inappropriate to deal with this task, as they do not consider the structure of strings and graph simultaneously. Moreover, attributed graph clustering algorithms generally construct poor solutions because they need to represent a string as a vector of attributes, which inevitably loses information and may harm clustering quality. We thus introduce the problem of clustering a sequence graph. We first propose two pairwise distance measures for sequence graphs, one based on edit distance and shortest path distance and another one based on SimRank. We then formalize the problem under each measure, showing also that it is NP-hard. In addition, we design a polynomial-time 2-approximation algorithm, as well as a heuristic for the problem. Experiments using real datasets and a case study demonstrate the effectiveness and efficiency of our methods