The current Internet did not define any inherent management constructs and mechanisms;such concepts were added after networking standards and architectures were constructed. This also influenced network management for...
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ISBN:
(纸本)9781424443376
The current Internet did not define any inherent management constructs and mechanisms;such concepts were added after networking standards and architectures were constructed. This also influenced network management for other types of networks. This paper is the first in a series that explores concepts for a new autonomic approach to network management that can be used for current and next generation networks as well as for the Future Internet.
To support more effective searches in large-scale weakly-tagged image collections, we have developed a novel algorithm to integrate both the visual similarity contexts between the images and the semantic similarity co...
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ISBN:
(纸本)9781605584805
To support more effective searches in large-scale weakly-tagged image collections, we have developed a novel algorithm to integrate both the visual similarity contexts between the images and the semantic similarity contexts between their tags for topic network generation and word sense disambiguation. First, a topic network is generated to characterize both the semantic similarity contexts and the visual similarity contexts between the image topics more sufficiently. By organizing large numbers of image topics according to their cross-modal inter-topic similarity contexts, our topic network can make the semantics behind the tag space more explicit, so that users can gain deep insights rapidly and formulate their queries more precisely. Second, our word sense disambiguation algorithm can integrate the topic network to exploit both the visual similarity contexts between the images and the semantic similarity contexts between their tags for addressing the issues of polysemes and synonyms more effectively, thus it can significantly improve the precision and recall rates for image retrieval. Our experiments on large-scale Flickr and LabelMe image collections have provided very positive results. Copyright 2009 ACM.
In this paper we propose a novel approach for generating expressive caricatures from an input image. The novelty of this work comes from combining an Active Appearance Model facial feature extraction system with a qua...
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Semi-Supervised Support Vector Machines(S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (super...
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ISBN:
(纸本)9781605585161
Semi-Supervised Support Vector Machines(S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supervised) SVM. In this paper, we show that S3VMs, with knowledge of the means of the class labels of the unlabeled data, is closely related to the supervised SVM with known labels on all he unlabeled data. This motivates us to first estimate the label means of the unlabeled data. Two versions of the meanS3VM, which work by maximizing the margin between the label means, are proposed. The first one is based on multiple kernel learning, while the second one is based on alternating optimization. Experiments show that both of the proposed algorithms achieve highly competitive and sometimes even the best performance as compared to the state-of-the-art semi-supervised learners. Moreover, they are more efficient than existing *** 2009.
Semi-Supervised Support Vector Machines (S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supe...
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ISBN:
(纸本)9781605585161
Semi-Supervised Support Vector Machines (S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supervised) SVM. In this paper, we show that S3VMs, with knowledge of the means of the class labels of the unlabeled data, is closely related to the supervised SVM with known labels on all the unlabeled data. This motivates us to first estimate the label means of the unlabeled data. Two versions of the mean S3VM, which work by maximizing the margin between the label means, are proposed. The first one is based on multiple kernel learning, while the second one is based on alternating optimization. Experiments show that both of the proposed algorithms achieve highly competitive and sometimes even the best performance as compared to the state-of-the-art semi-supervised learners. Moreover, they are more efficient than existing S3VMs.
The main difficulty in face image modeling is to decompose those semantic factors contributing to the formation of the face images, such as identity, illumination and pose. One promising way is to organize the face im...
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ISBN:
(纸本)9781605586083
The main difficulty in face image modeling is to decompose those semantic factors contributing to the formation of the face images, such as identity, illumination and pose. One promising way is to organize the face images in a higher-order tensor with each mode corresponding to one contributory factor. Then, a technique called Multilinear Subspace Analysis (MSA) is applied to decompose the tensor into the mode-n product of several mode matrices, each of which represents one semantic factor. In practice, however, it is usually difficult to obtain such a complete training tensor since it requires a large amount of face images with all possible combinations of the states of the contributory factors. To solve the problem, this paper proposes a method named M2SA, which can work on the training tensor with massive missing values. Thus M2SA can be used to model face images even when there are only a small number of face images with limited variations which will cause missing values in the training tensor). Experiments on face recognition show that M2SA can work reasonably well with up to 70% missing values in the training tensor. Copyright 2009 ACM.
Biomedical literature is an important source of information in any researcher's investigation of genes, risk factors, diseases and drugs. Often the information searched by public health researchers is distributed ...
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ISBN:
(纸本)9781605588032
Biomedical literature is an important source of information in any researcher's investigation of genes, risk factors, diseases and drugs. Often the information searched by public health researchers is distributed across multiple disparate sources that may include publications from PubMed, genomic, proteomic and pathway databases, gene expression and clinical resources and biomedical ontologies. The unstructured nature of this information makes it difficult to find relevant parts from it manually and comprehensive knowledge is further difficult to synthesize automatically. In this paper we report on LITSEEK (LITerature Search by metadata Enhancement with External Knowledgebases), a system we have developed for the benefit of researchers at the Centers for Disease Control (CDC) to enable them to search the HuGE (Human Genome for Epidemiology) database of PubMed articles, from a pharmacogenomic perspective. Besides analyzing text using TFIDF ranking and indexing of the important terms, the proposed system incorporates an automatic consultation with PharmGKB - a human-curated knowledge base about drugs, related diseases and genes, as well as with the Gene Ontology, a human-curated, well accepted ontology. We highlight the main components of our approach and illustrate how the search is enhanced by incorporating additional concepts in terms of genes/drugs/diseases (called metadata for ease of reference) from PharmGKB. Various measurements are reported with respect to the addition of these metadata terms. Preliminary results in terms of precision based on expert user feedback from CDC are encouraging. Further evaluation of the search procedure by actual researchers is under way. Copyright 2009 ACM.
A group key agreement (GKA) protocol allows a set of users to establish a common secret via open networks. Observing that a major goal of GKAs for most applications is to establish a confiDential channel among group m...
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Commitment-modeled protocols enable flexible and robust interactions among agents. However, existing work has focused on features and capabilities of protocols without considering the active role of agents in them. Th...
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ISBN:
(纸本)9781615673346
Commitment-modeled protocols enable flexible and robust interactions among agents. However, existing work has focused on features and capabilities of protocols without considering the active role of agents in them. Therefore, in this paper we propose to augment agents with the ability of reasoning about and manipulating their commitments to maximize the system utility. We adopt a bottom-up approach by first investigating the intra-dependency between each commitment's preconditions and result which leads to a novel classification of commitments as well as a formalism to express various types of complex commitment. Within this framework, we provide a set of inference rules to benefit an agent by means of commitment refactoring which enables composition and/or decomposition of its commitments to optimize runtime performance. We also discuss the pros and cons of an agent scheduling and executing its commitments in parallel. We propose a reasoning strategy and an algorithm to minimize possible loss when the commitment is broken and maximize the overall system robustness and performance. Experiments show that concurrent schedules based on the features of commitments can boost the system performance significantly.
To address the scalability problem in attack graphs generation, we propose a novel method to generate attack graphs automatically. Our approach constructs a two-tier attack graph framework, which includes a host acces...
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ISBN:
(纸本)9780769537580
To address the scalability problem in attack graphs generation, we propose a novel method to generate attack graphs automatically. Our approach constructs a two-tier attack graph framework, which includes a host access graph and some sub-attack graphs. A subattack graph describes concrete attack scenarios from one source host to one target host, while the host access graph describes the attacker's privilege transition among hosts. Our sub-attack graphs and host access graph have remarkable smaller scales and can help network administrators to find the key hosts in attack sequences. Analysis shows that the upper bound computational cost of our model is O(N3), which could also be competed in real time. The following experiment validates our approach.
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