3,741 research outputs found

    Using Structure Indices for Efficient Approximation of Network Properties

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    Statistics on networks have become vital to the study of relational data drawn from areas including bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the most important measures—such as betweenness centrality, closeness centrality, and graph diameter—requires identifying short paths in these networks. However, finding these short paths can be intractable for even moderate-size networks. We introduce the concept of a network structure index (NSI), a composition of (1) a set of annotations on every node in the network and (2) a function that uses the annotations to estimate graph distance between pairs of nodes. We present several varieties of NSIs, examine their time and space complexity, and analyze their performance on synthetic and real data sets. We show that creating an NSI for a given network enables extremely efficient and accurate estimation of a wide variety of network statistics on that network

    Study and simulation of low rate video coding schemes

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    The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design

    Model-Based Environmental Visual Perception for Humanoid Robots

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    The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

    Modeling Human Group Behavior In Virtual Worlds

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    Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics

    Efficient service discovery in wide area networks

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    Living in an increasingly networked world, with an abundant number of services available to consumers, the consumer electronics market is enjoying a boom. The average consumer in the developed world may own several networked devices such as games consoles, mobile phones, PDAs, laptops and desktops, wireless picture frames and printers to name but a few. With this growing number of networked devices comes a growing demand for services, defined here as functions requested by a client and provided by a networked node. For example, a client may wish to download and share music or pictures, find and use printer services, or lookup information (e.g. train times, cinema bookings). It is notable that a significant proportion of networked devices are now mobile. Mobile devices introduce a new dynamic to the service discovery problem, such as lower battery and processing power and more expensive bandwidth. Device owners expect to access services not only in their immediate proximity, but further afield (e.g. in their homes and offices). Solving these problems is the focus of this research. This Thesis offers two alternative approaches to service discovery in Wide Area Networks (WANs). Firstly, a unique combination of the Session Initiation Protocol (SIP) and the OSGi middleware technology is presented to provide both mobility and service discovery capability in WANs. Through experimentation, this technique is shown to be successful where the number of operating domains is small, but it does not scale well. To address the issue of scalability, this Thesis proposes the use of Peer-to-Peer (P2P) service overlays as a medium for service discovery in WANs. To confirm that P2P overlays can in fact support service discovery, a technique to utilise the Distributed Hash Table (DHT) functionality of distributed systems is used to store and retrieve service advertisements. Through simulation, this is shown to be both a scalable and a flexible service discovery technique. However, the problems associated with P2P networks with respect to efficiency are well documented. In a novel approach to reduce messaging costs in P2P networks, multi-destination multicast is used. Two well known P2P overlays are extended using the Explicit Multi-Unicast (XCAST) protocol. The resulting analysis of this extension provides a strong argument for multiple P2P maintenance algorithms co-existing in a single P2P overlay to provide adaptable performance. A novel multi-tier P2P overlay system is presented, which is tailored for service rich mobile devices and which provides an efficient platform for service discovery
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