41350051
Language engineering for the Semantic Web: a digital library for endangered languages
6586245
Reconstructing XML Subtrees from Relational Storage of XML documents
Numerous researchers have proposed to use relational databases to store and query XML documents. One important component of such systems is the XML subtree reconstruction, which reconstructs the subtrees rooted at the matching nodes of an XML query and returns them to the user as the query result. Existing reconstruction algorithms either do not support recursive XML view schema, or require expensive nested queries or joins of multiple relations. In this paper, we propose an efficient XML subtree reconstruction algorithm, Reconstruct, which overcomes these limitations and uses an efficient stack-based structural join algorithm to recover all the parent-child relationships between elements. One salient advantage of this algorithm is that it employs the inlining feature of the inlining-based storage of XML documents, which is known as one of the best relational XML storage schemes. Both our algorithmic analysis and experimental study show that Reconstruct is efficient and scalable.
15066544
Bidirectional interpretation of XQuery
XQuery is a powerful functional language to query XML data. This paper gives a bidirectional interpretation of XQuery to address the problem of updating XML data through materialized XQuery views. We first design an expressive bidirectional transformation language, and then translate XQuery expressions into the code of this language. As a result, an XQuery expression can execute in two directions: in the forward direction, it generates a materialized view from the source XML data; while in the backward direction, it updates the source data by putting back the updates on the view. we have implemented our approach and applied it to some XQuery use cases from a W3C draft, which confirms the practicability of this approach.
2385789
Efficient self-healing group key distribution with revocation capability
This paper presents group key distribution techniques for large and dynamic groups over unreliable channels. The techniques proposed here are based on the self-healing key distribution methods (with revocation capability) recently developed by Staddon et al. [27]. By introducing a novel personal key distribution technique, this paper reduces (1) the communication overhead of personal key share distribution from O(t2log q) to O(tlogq), (2) the communication overhead of self-healing key distribution with t-revocation capability from O((mt2+tm)log q) to O(mtlog q), and (3) the storage overhead of the self-healing key distribution with $t$-revocation capability at each group member from O(m2log q) to O(mlogq), where $t$ is the maximum number of colluding group members, $m$ is the number of sessions, and $q$ is a prime number that is large enough to accommodate a cryptographic key. All these results are achieved without sacrificing the unconditional security of key distribution. In addition, this paper presents two techniques that allow trade-off between the broadcast size and the recoverability of lost session keys. These two methods further reduce the broadcast message size in situations where there are frequent but short-term disruptions of communication and where there are long-term but infrequent disruptions of communication, respectively.
27206002
Exponential Stability Analysis of Neural Networks with Multiple Time Delays
Without assuming the boundedness, strict monotonicity and differentiability of the activation function, a result is established for the global exponential stability of a class of neural networks with multiple time delays. A new sufficient condition guaranteeing the uniqueness and global exponential stability of the equilibrium point is established. The new stability criterion imposes constraints, expressed by a linear matrix inequality, on the self-feedback connection matrix and interconnection matrices independent of the time delays. The stability criterion is compared with some existing results, and it is found to be less conservative than existing ones.
84347
Latent Layout Analysis for Discovering Objects in Images
Latent layout analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative model that associates latent aspects to local appearances. The dependency between aspects and position is captured by a spatial sensitive aspect model. This dependency distinguishes LLA from probabilistic latent semantic analysis (PLSA). The latent aspects together with the latent layout constitute a compact scene representation. We demonstrate that the proposed LLA significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization
8564766
Location-based pairwise key establishments for static sensor networks
Sensor networks are ideal candidates for applications such as target tracking and environment monitoring. Security in sensor networks is critical when there are potential adversaries. Establishment of pairwise keys is a fundamental security service, which forms the basis of other security services such as authentication and encryption. However, establishing pairwise keys in sensor networks is not a trivial task, particularly due to the resource constraints on sensors. This paper presents several techniques for establishing pairwise keys in static sensor networks. These techniques take advantage of the observation that in static sensor networks, although it is difficult to precisely pinpoint sensors' positions, it is often possible to approximately determine their locations. This paper presents a simple location-aware deployment model, and develops two pairwise key predistribution schemes, a closest pairwise keys predistribution scheme and a location-based pairwise keys scheme using bivariate polynomials, by taking advantage of sensors' expected locations. The analysis in this paper indicates that these schemes can achieve better performance if such location information is available and that the smaller the deployment error (i.e., the difference between a sensor's actual location and its expected location) is, the better performance they can achieve.