Purpose The purpose of this paper is to examine the relationship between urbanization and economic growth in ASEAN countries for the period 1993-2014. Design/methodology/approach The Granger causality test and the regression estimation method with static and dynamic panel data (FE, RE, Driscoll and Kraay, D-GMM and PMG) were used. The sample includes seven ASEAN countries: Brunei, Cambodia, Indonesia, Malaysia, Philippines, Thailand and Vietnam. Findings The results show that at least a causal relationship exists between urbanization and economic growth and urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model. Research limitations/implications The evidence from this study suggests that there is a non-linear relationship between urbanization and the economic growth. Urbanization has the potential to accelerate the economic growth, and this potential will depend on the establishment of favorable institutions and investments in appropriate public infrastructure. Practical implications The decision on the model of urbanization needs to be based on social and environmental considerations as well as market-based economic efficiency. The quality of urbanization manifests in the way that people and businesses perceive when they come to cities and their position in the labor market, urban housing, niche commodity markets, supply chain, collaborative network and physical space for the operation of the business. Most ASEAN countries have not yet reached a high level of urbanization, despite having a number of policies for promoting urbanization to contribute to the economic growth. However, policymakers should find ways to facilitate the development of urbanization that contributes to economic growth, employment growth, environmental sustainability, rather than the pursuit of speeding up the process of urbanization. Originality/value Between urbanization and economic growth at least a causal relationship exists. Urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

Most modern day phishing attacks occur by luring users into visiting a malicious web page that looks and behaves like the original. Phishing is a web-based attack which end users are lured to visit fraudulent websites and give away personal information unconsciously. The key problem for checking phishing pages is timely and efficiently calculation. There is still remaining a large space for phishing detection methods. In this paper, we present an approach which calculates similarity of two web pages based on genetic algorithm and applied it to detecting phishing web pages using DOM-Tree structure. Our experimental evaluation demonstrates that our solution is feasible in practice.

This paper is concerned with two salient allocationproblems in fair division of indivisible goods, aiming atmaximizing egalitarian and Nash product social welfare.These problems are computationally NP-hard, meaning thatachieving polynomial time algorithms is impossible, unlessP = NP. Approximation algorithms, which return near-optimalsolution with a theoretical guarantee, have been widely usedfor tackling the problems. However, most of them are often ofhigh computational complexity or not easy to implement. It istherefore of great interest to explore fast greedy methods thatcan quickly produce a good solution. This paper presents anempirical study of the performance of several such methods.Interestingly, the obtained results show that fair allocationproblems can be practically approximated by greedy algorithms.Keywords: Fair allocation, exact algorithm, greedy algorithm,mixed-integer linear program, NP-hard.I. INTRODUCTIONIn this paper, we study the fair allocation problem, whichhas shown its growing interest during last decades, with awide range of real-world applications [1]. In short, this is acombinatorial optimization problem which asks to allocate???? discrete items amongst a set of ???? agents (or players)so as to meet a certain notion of fairness. It is assumedthat every item is “indivisible” and “non-sharable”, thatis, i) it cannot be broken in pieces before allocating toagents, and ii) it cannot be shared by two or more agents.For example, laptops and cell-phones are indivisible itemswhich agents might not want to share with others. Anallocation of items to agents is simply a partition of thewhole set of items into ???? disjoint subsets. There are up to???????? such partitions, making the solution space large enoughso that an exhaustive search for an optimal solution isimpossible.It now remains to define what a fair allocation is, aconcept that is of independent interest in the field ofEconomic and Social Choice Theory [2, 3]. In general, thereare many different ways of defining fairness, depending onparticular applications. The most common way is to eitheruse a so-called Collective Utility Function (CUF), which isa function for aggregating individual agents’ utilities in afair manner, or to follow an orthogonal method relying ondetermining the fair share of agents. Since we are focusingon the first method in this paper, we refer the reader tothe paper [4] and the references therein for more details ofthe second method. Suppose that every agent evaluates thevalue of items through a utility function, which maps eachsubset of items to a numerical value representing the utilityof the agent for the subset. Then, one can define a maxmin fair allocation to be the one that maximizes the

Allocating indivisible items between agents in a fair manner is a fundamental problem that has attracted a lot of interests in the last decades due to the wide range of its applications. There are several common criteria for defining what a fair allocation is, including: max-min share, proportional share, min-max share, envy-freeness, CEEI. In this paper, we introduce a new notion of fairness, called Nash-product share, which can be determined through computing an allocation of maximum Nash-product welfare, assuming that all agents have the same additive utilities. An allocation satisfies this fairness criterion is called a Nashproduct fair allocation. We first show that computing the Nash-product share of every agent is NP-hard, even with two agents only. In addition, we prove that the problem of testing the existence of a Nash-product fair allocation is an NP-hard problem when the number of agents is part of the input. Finally, since a Nash-product fair allocation does not always exist, we investigate the problem that, given a problem instance, asks for the largest value c for which there is an allocation such that every agent receives a subset of items of values of at least c times her Nash-product share. For the case where the number of agents is constant, we present a polynomial-time approximation scheme (PTAS). I. GIỚI THIỆUFair allocation is a fundamental problem which is of interest in both computer science and economics due to the wide range of its applications (see a book chapter by Bouveret [8] and Lang and Rothe [15]). This problem concerns with allocating a finite set of goods (that may also be called items or objects) among a group of agents having different preferences over the subsets of goods. We will assume that the preferences of agents are presented by additive (or linear) utility functions. Our goal is to find an allocation that satisfies a certain notion of fairness. Among others, maxmin share, proportional share, and envy-freeness are the three notions that have been studied intensively in the literature. The max-min share of an agent is defined as the bundle that the agent can guarantee for herself when partitioning the items into bundles but choosing last. In a proportionally fair allocation between n agents, each agent receives a bundle of value at least 1/ n of the whole. An allocation is said to be envy-free if no agent wants to exchange her bundle of goods with that of any other agent. These three fairness criteria have attractive theoretical and practical properties (see the references cited above and the references therein). In [7], it was shown that envy-freeness is the strongest among the above fairness notions and implies proportional fairness, which implies max-min fairnessthe weakest one. On the other hand, as argued in the literature (see, for example, [14,16,17]), an allocation satisfying any of the these fairness notions is not guaranteed to exist in general. Furthermore, checking the existence of an envyfree (proportional fair) allocation is NP-complete (see [7]) even in th...