The degree of uncertainty associated with the desired output from the team’s endeavours has a major impact on the management of the project. This is different to the issues around bleeding edge projects discussed above. The less certain the client is of its requirements, the greater the uncertainty associated with delivering a successful project and the greater the effort required from the project team to work with the client to evolve a clear understanding of what’s required for success. This is not an issue as long as all of the project stakeholders appreciate they are on a journey to initially determine what success looks like, and then deliver the required outputs. Budgets and timeframes are expected to change to achieve the optimum benefits for the client; and the project is set up with an appropriately high level of contingencies to deal with the uncertainty. Problems occur if the expectations around the project are couched in terms of achieving an ‘on time, on budget’ delivery when the output is not defined and the expected benefits are unclear 4 . Managing uncertainty is closely associated with and influences the **complexity** of the relationships discussed below.

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The key underpinnings of the PMBOK and general project management theory derive from the principles of ‘scientific management’ 3 . These principles are very effective in optimising and controlling simple manual tasks such as loading iron into rail cars and laying bricks. Managers can see and measure the work, quality is an ‘obvious’ factor and production rates can be established. Similarly, **scheduling** and cost estimating are relatively straightforward; you cannot build a brick wall until after the foundations are laid and all of the cost elements are measurable.

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[15]. Mei-RongXu, Bao Shi and Xiao-Yun Zeng, Asymptotic behavior for non-oscillatory solutions of difference equations with several delays in the neutral term, Journal of Applied Mathematics and Computing, Vol. 27, No. 1– 2, 2008, **pp**. 33–45.

The participants made their **complexity** judgments on a rating scale that had as many increments as there where rhythms to compare in a set. The sets as well as the rhythms within each set were shown on the screen in random order. At the end of the test we asked for information about musical experience and **age**. We left some space for comments and feedback. The whole task typically took about 10 minutes to complete. We recorded the total time from the moment the subject started the experiment until the response form was sent, to ensure that the subject listened to all stimuli.

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Within **complexity** theory, the Complex Responsive Processes of Relating (CRPR) sees the delivery of the project being crafted by thousands of individual decisions and actions taken by people who are ‘actors’ within the social network of the project team and its immediate surrounds. The role of ‘project management’ is to motivate, coordinate and lead the team towards the common objective of a successful project outcome. In this environment the project schedule has two key roles to play, firstly as a tool to develop a common understanding of the optimum approach for achieving the project objectives and then as a flexible tool to measure the inevitable deviations from the plan and re-assess the best way forward (Weaver, 2009) 4 . When designed properly, the schedule provides the framework to support effective decision making by all members of the project team.

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Among the four algorithms for PICOD(1) presented in the chapter, GRCOV performs the best. For the random graphs on which the simulations were run, arguments similar to the ones used in Section 1.4 can be used to show that it produces an encoding with the same asymptotic performance as RANDCOV, but the practical performance is much better. In fact, the maximum number of coded bits required by GRCOV (this is among the random instances in the simulation, not globally), which is also plotted in the figure, is not substantially different from the average number. The performance of RANDCOV-**PP** is substantially better than RANDCOV, especially when the side information sets are denser and hence the G is sparser. Also, the performance of RANDCOV takes a hit in this regime. Both of these are due to the fact that the number of partitions in the client vertices increases, although most of them are “disjoint” which allows RANDCOV-**PP** to improve the performance significantly. Finally, ICOD-SETCOV performs as good as GRCOV when p msg ≤ 0.5 but becomes worse as G becomes sparser. This can be partly explained

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The only tool for effective day-to-day coordination of a project’s work and the key underpinning of the ‘time phased budget’ needed for valid EV and ES calculations is an effective, useful and relevant schedule. Unfortunately, the current state of **scheduling** is not good, and **scheduling** is consistently failing to deliver the expected outcomes on large complicated projects in both the Defence arena and elsewhere.

We then define the parameterized **complexity** class BH (level) to be the class of all pa- rameterized problems that can be fpt-reduced to the problem BH (level)- Sat . In other words, the class BH (level) consists of all parameterized problems P for which there exists an fpt-reduction that reduces each instance (x, k) of P to an instance of some problem in the f (k)-th level of the Boolean Hierarchy, for some computable function f . As we will see below, the classes FPT NP[f(k)] and BH (level) coincide. Moreover, we will show that Agenda - Safety maj (agenda size) is complete for this class. We begin with showing the upper bound on the number of SAT calls needed to solve Agenda - Safety maj (agenda size). Proposition 9. Agenda - Safety maj (agenda size) is in co- BH (level).

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The study of the K reducibility is part of a larger study of the so-called ‘weak reducibilities’. These are preorders that measure various notions related to random- ness (of sets), as opposed to computational **complexity**. Such reducibilities, like K, do not have an underlying map i.e. an algorithm mapping (reducing) the second set to the first one. The existence of such maps is a vital feature in the Turing or stronger reducibilities.

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Nies [Nie05] showed that the class of low for K sets coincides with K. However this coincidence is not effective, in the sense that there is no algorithm which outputs a level in the low for K hierarchy where a set lives, given a level of it in the K- triviality hierarchy. Hence determining the **complexity** of the functions giving the cardinality of the levels of the two hierarchies constitutes two separate problems. 1.1. Overview. We start by discussing the problem of finding the number of in- finite paths through a given tree. 1 An analysis of this general problem is given in

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The last of the significant changes in the industry started in latter part of the 1980s and has continued through to the present time. Despite the ever increasing number of people using PC based **scheduling** tools; the competition in the market has driven prices down and caused a major consolidation of the industry. For many years, Microsoft Project could be bought for less than $100 per set. This decimated the ‘low end’ market. Similarly the cost of developing GUI interfaces and staying competitive in the features arms race at the ‘high end’ caused most of the system developers to move to greener pastures or simply close up shop.

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The representation of preferences of agents is a central fea- ture in many AI systems. In particular when the number of alternatives to be considered may become large, the use of compact preference representation languages is crucial. The framework of weighted propositional formulas can be used to define several such languages. The central idea is to associate numerical weights with goals specified in terms of proposi- tional formulas, and to compute the utility value of an alter- native as the sum of the weights of the goals it satisfies. In this paper, we analyze several properties of languages defined by weighted goals: their expressivity, the relative succinctness of different sublanguages, and the computational **complexity** of finding the best alternative with respect to a given utility function expressed in terms of weighted goals.

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The ratio between the number of values required to rationalise a given profile of AF’s by an AVAF with a given fixed master attack-relation under the expansion semantics using a maximal [r]

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In the following an approach for reducing the computa- tional **complexity** of the **scheduling** process is presented. It relies on the fact that certain requirements have to be ful- filled for advantageous effects of suppressing interference at the PMS. In case these requirements are currently not fulfilled, selected transmission parameters can be excluded from the considerations in the **scheduling**. As these trans- mission parameters would show lower or equal perfor- mance compared to others, their exclusion can theoretically happen without affecting the performance. The definition of requirements is based on a detailed study of the per- formance gains of coordinated beamforming under differ- ent parameters that will be introduced in the following subsections. Section 6 then describes the conclusions and how they are applied in the proposed approach.

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In order to talk about questions like this, we need a relation “is harder than” or “is at least as hard as” and a corresponding **complexity** hierar- chy. In this paper, we shall restrict our attention to a special class of **complexity** hierarchies, viz. those induced by reduction functions. This choice is motivated by the fact that the hierarchies investigated in com- puter science are of this type, and some of the most famous hierarchies in mathematical logic (e.g., the Wadge hierarchy, one-reducibility and many-one-reducibility) are as well. We shall introduce a notion of com- plexity hierarchy in an abstract way in Section 2 and then specialize in the sections to follow.

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Nonetheless, its assumption in this context is natural. As pointed out by Welch in (Welch, 2001, Remark 4) and discussed further by L¨owe and Welch in (L¨owe & Welch, 2001, Section 6), the high descriptive **complexity** of revision-theoretic definitions yields certain dependencies between Revision Theory and aspects of the surrounding set theoretic universe. The answers to some questions about revision-theoretic ob- jects depend strongly on set theory, they have different answers, e.g., in the axiom systems ZFC + V=L and ZFC + Det(Σ 1 1 ). That means that in order to make definite claims about Revision Theory, we need to make a choice what sort of set theory we want to work in. Since we are interested in game-theoretic characterizations, choosing the game- theoretically smooth ZFC + Det(Σ 1

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In this section, we will look at two concrete polynomial- time data **complexity** results for the description logic ELI . Even though the data **complexity** view gives the same out- look on the **complexity** of these problems, we will use the viewpoint of parameterized **complexity** theory to argue that these two problems in fact have a different **complexity**. One of these problems is more efficiently solvable than the other. We chose the example of ELI to illustrate our point be- cause it is technically straightforward. More intricate fixed- parameter tractability results for conjunctive query answer- ing in description logics have been obtained in the literature (Bienvenu et al. 2017a; 2017b; Kikot, Kontchakov, and Za- kharyaschev 2011).

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Money laundering a suspicious fund transfer between accounts without names which affects and threatens the stability of countries economy. The growth of internet technology and loosely coupled nature of fund transfer gateways helps the malicious user’s to perform money laundering. There are many approaches has been discussed earlier for the detection of money laundering and most of them suffers with identifying the root of money laundering. We propose a time variant approach using behavioral patterns to identify money laundering. In this approach, the transaction logs are split into various time window and for each account specific to the fund transfer the time value is split into different time windows and we generate the behavioral pattern of the user. The behavioral patterns specifies the method of transfer between accounts and the range of amounts and the frequency of destination accounts and etc.. Based on generated behavioral pattern , the malicious transfers and accounts are identified to detect the malicious root account. The proposed approach helps to identify more suspicious accounts and their group accounts to perform money laundering identification. The proposed approach has produced efficient results with less time **complexity**. Keywords: Money Laundering, Data Mining, Behavior Patterns.

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The Network dataset vs Intrusion occurring rate the dataset pattern using time probability, the proposed system is handle pattern discover frequently that is occurred on the datasets thi[r]

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In large database, inclusion of metadata provides well separation of data objects from large dataset in order to facilitate thematic clustering of resources [13].. However, clustering al[r]