Content-based image retrieval: reading one's mind and helping people share.

Sia Ka Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-91).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.1Chapter 1.2 --- Contributions --- p.3Chapter 1.3 --- Thesis Organization --- p.4Chapter 2 --- Background --- p.5Chapter 2.1 --- Content-Based Image Retrieval --- p.5Chapter 2.1.1 --- Feature Extraction --- p.6Chapter 2.1.2 --- Indexing and Retrieval --- p.7Chapter 2.2 --- Relevance Feedback --- p.7Chapter 2.2.1 --- Weight Updating --- p.9Chapter 2.2.2 --- Bayesian Formulation --- p.11Chapter 2.2.3 --- Statistical Approaches --- p.12Chapter 2.2.4 --- Inter-query Feedback --- p.12Chapter 2.3 --- Peer-to-Peer Information Retrieval --- p.14Chapter 2.3.1 --- Distributed Hash Table Techniques --- p.16Chapter 2.3.2 --- Routing Indices and Shortcuts --- p.17Chapter 2.3.3 --- Content-Based Retrieval in P2P Systems --- p.18Chapter 3 --- Parameter Estimation-Based Relevance Feedback --- p.21Chapter 3.1 --- Parameter Estimation of Target Distribution --- p.21Chapter 3.1.1 --- Motivation --- p.21Chapter 3.1.2 --- Model --- p.23Chapter 3.1.3 --- Relevance Feedback --- p.24Chapter 3.1.4 --- Maximum Entropy Display --- p.26Chapter 3.2 --- Self-Organizing Map Based Inter-Query Feedback --- p.27Chapter 3.2.1 --- Motivation --- p.27Chapter 3.2.2 --- Initialization and Replication of SOM --- p.29Chapter 3.2.3 --- SOM Training for Inter-query Feedback --- p.31Chapter 3.2.4 --- Target Estimation and Display Set Selection for Intra- query Feedback --- p.33Chapter 3.3 --- Experiment --- p.35Chapter 3.3.1 --- Study of Parameter Estimation Method Using Synthetic Data --- p.35Chapter 3.3.2 --- Performance Study in Intra- and Inter- Query Feedback . --- p.40Chapter 3.4 --- Conclusion --- p.42Chapter 4 --- Distributed COntent-based Visual Information Retrieval --- p.44Chapter 4.1 --- Introduction --- p.44Chapter 4.2 --- Peer Clustering --- p.45Chapter 4.2.1 --- Basic Version --- p.45Chapter 4.2.2 --- Single Cluster Version --- p.47Chapter 4.2.3 --- Multiple Clusters Version --- p.51Chapter 4.3 --- Firework Query Model --- p.53Chapter 4.4 --- Implementation and System Architecture --- p.57Chapter 4.4.1 --- Gnutella Message Modification --- p.57Chapter 4.4.2 --- Architecture of DISCOVIR --- p.59Chapter 4.4.3 --- Flow of Operations --- p.60Chapter 4.5 --- Experiments --- p.62Chapter 4.5.1 --- Simulation Model of the Peer-to-Peer Network --- p.62Chapter 4.5.2 --- Number of Peers --- p.66Chapter 4.5.3 --- TTL of Query Message --- p.70Chapter 4.5.4 --- Effects of Data Resolution on Query Efficiency --- p.73Chapter 4.5.5 --- Discussion --- p.74Chapter 4.6 --- Conclusion --- p.77Chapter 5 --- Future Works and Conclusion --- p.79Chapter A --- Derivation of Update Equation --- p.81Chapter B --- An Efficient Discovery of Signatures --- p.82Bibliography --- p.8

The trust management framework for peer-to-peer networks

Popularity of peer-to-peer (P2P) networks exposed a number of security vulnerabilities. Among those is a problem of finding reliable communication partners. In this thesis, we present an integrated trust framework for peer-to-peer networks that quantifies the trustworthiness of a peer via reputation-based trust mechanism and anomaly detection techniques. As opposed to other known techniques in P2P networks, our trust management schema is fully decentralized and does not rely on the co-operation of peers. Furthermore, the reputation computation is based on traffic coming from other peers. We also describe an anomaly detection procedure that analyses peer activity on the network and flags potentially malicious behavior by detecting deviation from peer profile. We present integration of our anomaly detection to trust management scheme and study the performance of reputation-based approach using implementation and performance of trust framework through simulation

Lookup Protocols and Techniques for Anonymity

This dissertation covers two topics of interest for network applications: lookup protocols, a basic building block for distributed systems, and ring signatures, a powerful primitive for anonymous communication. In the first part of this work, we review lookup protocols, distributed algorithms that allow users to publish a document as well as to look up a published document that matches a given name. Our first major contribution is to design Local Minima Search (LMS), a new efficient lookup protocol for a model in which a node is physically connected to a few other nodes and may only communicate directly with them. Our second major contribution is the formulation of a new model in which we allow an arbitrary number of misbehaving nodes, but we assume a restriction on their network addresses. We then design a new lookup protocol for this setting. In the second part of this dissertation, we present our work on ring signatures, a variant of digital signatures, which enables a user to sign a message so that a set of possible signers is identified, without revealing which member of that set actually generated the signature. Our first contribution on this topic is new definitions of security which address attacks not taken into account by previous work. As our second contribution, we design the first provably secure ring signature schemes in the standard model

Design and Evaluation of Distributed Algorithms for Placement of Network Services

Network services play an important role in the Internet today. They serve as data caches for websites, servers for multiplayer games and relay nodes for Voice over IP: VoIP) conversations. While much research has focused on the design of such services, little attention has been focused on their actual placement. This placement can impact the quality of the service, especially if low latency is a requirement. These services can be located on nodes in the network itself, making these nodes supernodes. Typically supernodes are selected in either a proprietary or ad hoc fashion, where a study of this placement is either unavailable or unnecessary. Previous research dealt with the only pieces of the problem, such as finding the location of caches for a static topology, or selecting better routes for relays in VoIP. However, a comprehensive solution is needed for dynamic applications such as multiplayer games or P2P VoIP services. These applications adapt quickly and need solutions based on the immediate demands of the network. In this thesis we develop distributed algorithms to assign nodes the role of a supernode. This research first builds off of prior work by modifying an existing assignment algorithm and implementing it in a distributed system called Supernode Placement in Overlay Topologies: SPOT). New algorithms are developed to assign nodes the supernode role. These algorithms are then evaluated in SPOT to demonstrate improved SN assignment and scalability. Through a series of simulation, emulation, and experimentation insight is gained into the critical issues associated with allocating resources to perform the role of supernodes. Our contributions include distributed algorithms to assign nodes as supernodes, an open source fully functional distributed supernode allocation system, an evaluation of the system in diverse networking environments, and a simulator called SPOTsim which demonstrates the scalability of the system to thousands of nodes. An example of an application deploying such a system is also presented along with the empirical results

Supporting Complex Queries in P2P Networks

Ph.DDOCTOR OF PHILOSOPH

Efficient and scalable triangle centrality algorithms in the arkouda framework

Graph data structures provide a unique challenge for both analysis and algorithm development. These data structures are irregular in that memory accesses are not known a priori and accesses to these structures tend to lack locality. Despite these challenges, graph data structures are a natural way to represent relationships between entities and to exhibit unique features about these relationships. The network created from these relationships can create unique local structures that can describe the behavior between members of these structures. Graphs can be analyzed in a number of different ways including at a high level in community detection and at the node level in centrality. Both of these are difficult to quantitatively define because a “correct” answer is not readily apparent. The centrality of a node can be subjective; what does it mean central in an amorphous data structure? Further, even when centrality or community detection can be defined, there are typically trade offs in detection and analysis. A fine grained method may yield a more precise method but the run time may scale exponentially or even beyond. For small datasets this may not be a concern but for graph datasets this can make analysis prohibitive considering a social media networks where there are millions of people with millions of connections. Based on these two criteria, we implement several versions of a recently designed centrality measure called Triangle Centrality which is a centrality metric that considers both connectivity of a node with other nodes and the connectivity of a node’s neighbors. The connectivity is aptly measured through the triangles formed by nodes. There are two ways to implement triangle centrality; a graph based approach and an approach that utilizes linear algebra and matrix operations. This implementation is done with graph based data structures and to optimize this, we implement several versions of triangle counting based on prior research into the high performance computing framework, Arkouda. We implement an edge list intersection, a minimized search kernel method, a path merge method, and a small set intersection method. To compare these methods, we include a naive method and a comparison to a linear algebra implementation that uses the SuiteSparse GraphBLAS library. Our implementation utilizes an open-source framework called Arkouda which is a distributed platform for data scientists and developers. It simplifies complex parallel algorithms and the storage of datasets onto a back end Chapel server and allows users to access these from an intuitive pythonic interface. Our results demonstrate the scalability of the platform and are analyzed against different graph properties to see how these affect the implementation