Cognitive radio networks (CRNs) significantly improve spectrum utilization efficiency by allowing secondary users (SUs) to opportunistically share unused spectrum bands with primary users (PUs). In this paper, we pres...
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Cognitive radio networks (CRNs) significantly improve spectrum utilization efficiency by allowing secondary users (SUs) to opportunistically share unused spectrum bands with primary users (PUs). In this paper, we present a spectrum assignment model and propose a genetic algorithm (GA) and a heuristic algorithm to determine the proper spectrum assignment, which optimizes the SUs' reward and the network operator's revenue while satisfying capacity constraints, interference constraints and rate requirement constraints. We show that both algorithms greatly outperform the random assignment approach.
This paper is motivated by the lack of study on the diversity of user information needs in the scenario of graph search, which offers the prospect of significant improvements on search. We report our investigation on ...
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A considerable part of software design is dedicated for the composition of two or more modules. The implication is that changes made later in the implementation often require some reasoning about module composition pr...
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With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening th...
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ISBN:
(纸本)9781450319676
With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results. Copyright 2013 ACM.
There has been a proliferation in the amount of data being generated and collected in the past several years. One of the leading factors contributing to this increased data scale is cheaper commodity storage, making i...
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There has been a proliferation in the amount of data being generated and collected in the past several years. One of the leading factors contributing to this increased data scale is cheaper commodity storage, making it easier for organisations to house large data stores containing massive amounts of historical data. To effectively analyse these data sets, a preprocessing step is often required as most real data sets are inherently dirty and inconsistent. Existing data cleaning tools have focused on cleaning the errors at hand. In this paper, we take a more formal approach and propose the use of information algebra as a general theory to describe structured data sets and data cleaning. We formally define the notion of association rule, association function, and we present results relating these concepts. We also propose an algorithm for generating association rules from a given structured data set.
Smart scheduling can be used to reduce infrastructure energy costs in vehicular roadside networks [1]. In this paper we consider the scheduling problem when there are multiple roadside units (RSUs) in tandem. In this ...
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Smart scheduling can be used to reduce infrastructure energy costs in vehicular roadside networks [1]. In this paper we consider the scheduling problem when there are multiple roadside units (RSUs) in tandem. In this case it is often desirable to load balance the energy consumption across the roadside units so that energy provisioning costs can be reduced as much as possible. We first derive an integer linear programming bound on the min-max energy usage of the roadside units for a given input sample function. This bound is used for comparisons with two proposed on-line scheduling algorithms. The first is a low complexity First-Come-First-Assigned (FCFA) scheduler that makes greedy RSU selections followed by a minimum energy time slot assignment. The second algorithm, the Greedy Flow Graph Algorithm (GFGA), makes the same RSU selection but reassigns time slots whenever a new vehicle is assigned to the same RSU. This is done using a locally optimum integer linear program that can be efficiently solved using a minimum cost flow graph. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared to the energy lower bounds. Our results also show that near-optimal results are possible but come with increased computation times compared to our heuristic algorithms.
Smart downlink scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay tolerant roadside networks. In this paper we incorporate this type of scheduling into ON/OFF roadside unit sleep activity...
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Smart downlink scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay tolerant roadside networks. In this paper we incorporate this type of scheduling into ON/OFF roadside unit sleep activity, to further reduce infrastructure power consumption. To achieve significant power savings however, the OFF-to-ON sleep transitions may be very lengthy, and this overhead must be taken into account when performing the ON state scheduling. We first incorporate the OFF/ON sleep transitions into a lower bound on energy usage that can be computed for given input sample functions. An online scheduling algorithm referred to as the Flow Graph Sleep Scheduler (FGS) is then introduced, which makes locally optimum decisions about when to initiate new ON/OFF cycles. This is done by computing an estimate of the energy needed to fulfill known vehicle communication requirements with and without the OFF period. This calculation is efficiently done using a novel minimum flow graph formulation. Results from a variety of experiments show that the proposed scheduling algorithm performs well when compared to the energy lower bound. It is especially attractive in situations where vehicle demands and arrival rates are such that the energy costs permit frequent ON/OFF cycling.
Business managers are constantly under pressure to sustain their organizations' competitiveness and to make sound agile decisions in volatile business environments. Business managers make use of business intellige...
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ISBN:
(纸本)9781467359429
Business managers are constantly under pressure to sustain their organizations' competitiveness and to make sound agile decisions in volatile business environments. Business managers make use of business intelligence (BI) tools to assist them make intelligent decisions. software project managers also need what can be called `project intelligence' tools in order to deal with the continuously changing and complex software project environment which can be likened to a business environment. Based on this requirement, this research paper proposes that software projects be modeled as a business in order to apply a business intelligence model so as to develop `project intelligence' tools for software projects. That is, the aim of this research paper is to propose `project intelligence' tools which are modeled on business intelligence tools. The extension of BI tools to software projects follows from the premise that projects are business constructs through which a business is operated to create a sustainable stakeholder-value. The use of `project intelligence' tools will enable a software project manager to gain a clear knowledge about factors that may affect project progress and such knowledge will enable quick and informed quality decision making processes which will ensure improved sustainable project performance.
In this paper we propose a method for reverse engineering the features of Ajax-enabled web applications. The method first collects instances of the DOM trees underlying the application web pages, using a state-of-the-...
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ISBN:
(纸本)9781467358330
In this paper we propose a method for reverse engineering the features of Ajax-enabled web applications. The method first collects instances of the DOM trees underlying the application web pages, using a state-of-the-art crawling framework. Then, it clusters these instances into groups, corresponding to distinct features of the application. The contribution of this paper lies in the novel DOM-tree similarity metric of the clustering step, which makes a distinction between simple and composite structural changes. We have evaluated our method on three real web applications. In all three cases, the proposed distance metric leads to a number of clusters that is closer to the actual number of features and classifies web page instances into these feature-specific clusters more accurately than other traditional distance metrics. We therefore conclude that it is a reliable distance metric for reverse engineering the features of Ajax-enabled web applications.
Without requiring pre-fixed infrastructure, Vehicular Ad Hoc networks (VANETs) allow drivers to exchange information and access variant services in real-time manner. While achieving significant flexibility and conveni...
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Without requiring pre-fixed infrastructure, Vehicular Ad Hoc networks (VANETs) allow drivers to exchange information and access variant services in real-time manner. While achieving significant flexibility and convenience, dynamic network topology is also the source of challenges that invalidate most of well-designed protocols. To address this problem, in this paper, we propose a novel scheme called Location Aware Virtual Infrastructure (LAVI). The fundamental rationale of the LAVI scheme is based on an observation: although each individual vehicle will not stay at a location for an extended period of time, statistically there are some vehicles available as long as the density of vehicles is reasonably high. LAVI creates a virtually stable infrastructure layer on top of the dynamic physical topology. Through extensive experimental study, we have verified the effectiveness of our LAVI scheme.
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