17312614
Secrecy of Signals by Typing in Signal Transduction
We discuss secrecy of signals in signal transduction. As we have developed a basic concurrent language with interferencial coefficients, Iπ-calculus, to describe aberrance in biological models, a typing system for Iπ-calculus is proposed for achieving secrecy of signals in signal transduction. We show that this typing system guarantees that, if signal transduction typechecks, then it does not leak aberrance of signals.
31540088
Analyzing iKP Security in Applied Pi Calculus
The security for electronic payments is very important in electronic commerce. Many electronic payment protocols have been proposed recently to meet the security requirements. Since the design of a protocol is a difficult and error-prone task, the use of formal methods that allow for the verification of such protocols has received increasing attention. In this paper we take a look at the iKP from the point of view of the applied pi calculus. The iKP is described in the calculus and some security properties, mainly the authentication and anonymity properties, are verified.
3070522
Representation of the Signal Transduction with Aberrance Using Ipi Calculus
The pi calculus has been applied to modelling biochemical networks. In these applications, the modelling is done without considerations to exceptions. The Ipi calculus, the Interference pi calculus, is introduced to describe the signal transduction with aberrance. The calculus is obtained by adding two aberrant actions into the pi calculus and a tag system to check existing aberrance. A model of the signal transduction, RTK-MAPK, with the aberrant Ras is highlighted to illustrate the expressive power of the Ipi calculus.
1640564
Schema Mapping in P2P Networks Based on Classification and Probing
In this paper, we address the problems of adaptive schema mappings between different peers in peer-to-peer networks and searching for interesting data residing at different peers based on such mappings. We begin by classifying the shared schema of each peer into a taxonomy of relation categories and attribute categories. We then propose our adaptive schema mapping by selectively probing the shared schema with query probes, which are generated by the classification rules. To improve the accuracy of schema mapping, we introduce the notion of confusion matrix and prior-knowledge. Finally, we present the query reformulation strategy for retrieving and integrating data from all relevant peers. We have implemented our proposed schema mapping and query processing methods in real settings with real datasets. The experimental results show that our method can be adopted effectively in practice.
8467998
Truncation for Low-Complexity MIMO Signal Detection
Joint maximum-likelihood (JML) detector may be used in memoryless multiple-input multiple-output (MIMO) systems to obtain optimal detection performance. However, JML detector performs an exhaustive search and has prohibitively large decoding complexity. To reduce the complexity of MIMO signal detection, minimum mean-square-error (mmse) linear detector (LD), decision-feedback detector (DFD), group detector, and sphere detector (SD) may be used. In this correspondence, we propose a truncation based detector for low-complexity MIMO signal detection, and give theoretical insight into the design and performance of such a detector. We study bitruncation in detail and present two bitruncation approaches. These approaches have low-complexity, and computer simulation results show that they outperform mmse-LD and mmse-DFD
2367308
ClaiMaker: Weaving a Semantic Web of Research Papers
The usability of research papers on the Web would be enhanced by a system that explicitly modelled the rhetorical relations between claims in related papers. We describe ClaiMaker, a system for modelling readers' interpretations of the core content of papers. ClaiMaker provides tools to build a Semantic Web representation of the claims in research papers using an ontology of relations. We demonstrate how the system can be used to make inter-document queries.
1032115
Incremental Mining of Frequent Query Patterns from XML Queries for Caching
Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.
44653881
DMT: A Flexible and Versatile Selectivity Estimation Approach for Graph Query
Efficient and accurate selectivity estimation in graph-structured data, specifically for complex branching path query, is becoming a challenging and all-important problem for query performance optimization. Precise and flexible statistics summarization about graph-structured data plays a crucial role for graph query selectivity estimation. We propose DMT, Dynamic Markov Table, which is a dynamic graph summarization based on Markov Table by applying flexible combination of 4 Optimized Rules which investigate local forward and backward inclusions. The efficient DMT construction algorithm DMTBuilder and DMT-based statistical methods are introduced for selectivity estimations of various graph queries. Our extensive experiments demonstrate the advantages in accuracy and scalability of DMT by comparing with previously known alternative, as well as the preferred Optimized Rules that would favor different situations.