13876948
A framework for incremental deployment strategies for router-assisted services
Incremental deployment of a new network service or protocol is typically a hard problem, especially when it has to be deployed in the routers. First, an incrementally deployable protocol is needed. Second, a study of the performance impact of incremental deployment should be carried out to evaluate deployment strategies. Choosing the wrong strategy can be disastrous, as it may inhibit reaping the benefits of an otherwise robust service, and prevent widespread adoption. Unfortunately, to date there has been no systematic evaluation of incremental deployment for such services. Our research work is focused on the second aspect, namely the performance impact of incremental deployment of router-assisted services. We take the first step to define a framework for evaluating incrementally deployable services, which consists of three parts: (a) selection and classification of deployment strategies; (b) definition of performance metrics; and (c) systematic evaluation of deployment strategies. As a case study for our framework, we evaluate the performance of router-assisted reliable multicast protocols. Although our framework is still evolving, our results clearly demonstrate that the choice of a strategy has a substantial impact on performance, and thus affirms the need for systematic evaluation of incremental deployment. Our case study includes two router-assisted reliable multicast protocols, namely PGM and LMS. We make several interesting observations: (a) the performance of different deployment strategies varies widely; for example, with some strategies, both PGM and LMS approach full deployment performance with as little as 5% of the routers deployed, but with other strategies up to 80% deployment may be needed to approach the same level; (b) our sensitivity analysis reveals relatively small variation in the results in mos
16128617
CyberSeer: 3D audio-visual immersion for network security and management
Large complex networks have become an inseparable part of modern society. However, very little has been done to develop tools to manage and ensure the security of such networks. Network operators continue to slave over endless daily logs and alerts in a struggle to keep networks operational. Perhaps the most formidable enemy of network operations today is the volume of management data that must be perused. Expensive commercial products attempt to visualize data but with limited utility, as witnessed by the prevailing use of command-line interfaces and homegrown scripts. In addition to data collection tools, operators need to immediately observe and debug the effects of their actions; yet that information is buried deep in the data that pours daily from monitoring equipment. Thus, they need better ways to abstract network events and better, more informative ways to render them.
 In this work we first propose a new approach for abstracting network information, namely spectral representation. Second, we introduce immersive spatial audio representations of network events. Third, we introduce 3D interactive auto-stereoscopic (AS) displays for visual representations. We integrate the three techniques as follows. We use spectral techniques to extract complex events buried inside voluminous network traces and logs. Then, we create a desktop interactive immersive auto-stereoscopic 3D environment that is seamlessly integrated with multi-channel spatially rendered audio to render such events in a far more human-friendly fashion.
2649054
Knowing a tree from the forest: art image retrieval using a society of profiles
This paper aims to address the problem of art image retrieval (AIR), which aims to help users find their favorite painting images. AIR is of great interests to us because of its application potentials and interesting research challenges---the retrieval is not only based on painting contents or styles, but also heavily based on user preference profiles. This paper describes the collaborative ensemble learning, a novel statistical learning approach to this task. It at first applies probabilistic support vector machines (SVMs) to model each individual user's profile based on given examples, i.e. liked or disliked paintings. Due to the high complexity of profile modelling, the SVMs can be rather weak in predicting preferences for new paintings. To overcome this problem, we combine a society of users' profiles, represented by their respective SVM models, to predict a given user's preferences for painting images. We demonstrate that the combination scheme is embedded in a Bayesian framework and retains intuitive interpretations---like-minded users are likely to share similar preferences. We report extensive empirical studies based on two experimental settings. The first one includes some controlled simulations performed on 4533 painting images. In the second setting, we report evaluations based on user preferences collected through an online web-based survey. Both experiments demonstrate that the proposed approach achieves excellent performance in terms of capturing a user's diverse preferences.
17445618
A Formal Definition of Hierarchical Predicate Transition Nets
Hierarchical predicate transition nets have recently been introduced as a visual formalism for specifying complex reactive systems. They extend predicate transition nets with hierarchical structures so that large systems can be specified and understood stepwisely, and thus are more suitable for real-world applications. In this paper, we provide a formal syntax and an algebraic semantics for hierarchical predicate transition nets, which establish the theory of hierarchical predicate transition nets for precise specification and formal reasoning.
9650912
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded in a high dimensional Euclidean space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors in Rn, the natural distance is often different from the distance induced by the ambient space Rn. The geodesic distances on manifold are used to measure the similarities between images. However, when the number of data points is small, it is hard to discover the intrinsic manifold structure. Based on user interactions in a relevance feedback driven query-by-example system, the intrinsic similarities between images can be accurately estimated. We then develop an algorithmic framework to approximate the optimal mapping function by a Radial Basis Function (RBF) neural network. The semantics of a new image can be inferred by the RBF neural network. Experimental results show that our approach is effective in improving the performance of content-based image retrieval systems.
14585493
Learning a locality preserving subspace for visual recognition
We have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. We propose a new approach to mapping face images into a subspace obtained by locality preserving projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
1437223
A Fast General Methodology for Information - Theoretically Optimal Encodings of Graphs
We propose a fast methodology for encoding graphs with informationtheoretically minimum numbers of bits. The methodology is applicable to general classes of graphs; this paper focuses on simple planar graphs. Specifically, a graph with property ? is called a ?-graph. If ? satisfies certain properties, then an n-node ?-graph G can be encoded by a binary string X such that (1) G and X can be obtained from each other in O(n log n) time, and (2) X has at most s(n)+o(s(n)) bits for any function s(n) = ?(n) so that there are at most 2s(n)+o(s(n)) distinct n-node ?-graphs. Examples of such ? include all conjunctions of the following sets of properties: (1) G is a planar graph or a plane graph; (2) G is directed or undirected; (3) G is triangulated, triconnected, biconnected, merely connected, or not required to be connected; and (4) G has at most l1 (respectively, l2) distinct node (respectively, edge) labels. These examples are novel applications of small cycle separators of planar graphs and settle several problems that have been open since Tutte's census series were published in 1960's.