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Information, Volume 4, Issue 3 (September 2013), Pages 262-350

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Research

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Open AccessArticle Lightweight Proofs of Retrievability for Electronic Evidence in Cloud
Information 2013, 4(3), 262-282; doi:10.3390/info4030262
Received: 27 March 2013 / Revised: 4 June 2013 / Accepted: 19 June 2013 / Published: 5 July 2013
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Abstract
Proofs of Retrievability (PoR) is one of the basic functions of electronic evidence preservation center in cloud. This paper proposes two PoR schemes to execute the workflow of evidence preservation center, which are named Finer Grained Proofs of Retrievability (FG-PoR) and More [...] Read more.
Proofs of Retrievability (PoR) is one of the basic functions of electronic evidence preservation center in cloud. This paper proposes two PoR schemes to execute the workflow of evidence preservation center, which are named Finer Grained Proofs of Retrievability (FG-PoR) and More Lightweight Proofs of Retrievability (ML-PoR). The two PoR schemes do not use multi-replication technology or erasure code technology, but employ the verification tags and signatures to implement provable data possession and data recovery dual functions. When some data blocks have been lost in Archive Storage Area (ASA), FG-PoR can recover each data block of evidence matrix, but ML-PoR can only recover a column of evidence matrix. The analysis results show our two PoR schemes do not only provide the integrity verification guarantee but also have robust recovery guarantee to electronic evidence in cloud. The two schemes can allow for lower computation and storage costs than other similar schemes; moreover, ML-PoR can provide lower costs than FG-PoR. Full article
Open AccessArticle Passivity-Based Nonlinear Excitation Control of Power Systems with Structure Matrix Reassignment
Information 2013, 4(3), 342-350; doi:10.3390/info4030342
Received: 10 April 2013 / Revised: 12 August 2013 / Accepted: 12 August 2013 / Published: 20 August 2013
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Abstract
Passivity-based control is widely used in electronic circuit systems because it can utilize their internal structures to facilitate the controller design. In this paper, we first propose a dissipative Hamiltonian realization of power systems and discuss the disadvantages of the traditional passivity-based [...] Read more.
Passivity-based control is widely used in electronic circuit systems because it can utilize their internal structures to facilitate the controller design. In this paper, we first propose a dissipative Hamiltonian realization of power systems and discuss the disadvantages of the traditional passivity-based excitation controller. Then, a novel excitation controller is put forward to reassign the interconnection and dissipative matrix, and the corresponding Hamiltonian function. Simulation results verify that the proposed controller can effectively improve the transient stability of the power system. Full article

Review

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Open AccessReview The SP Theory of Intelligence: An Overview
Information 2013, 4(3), 283-341; doi:10.3390/info4030283
Received: 5 June 2013 / Revised: 2 July 2013 / Accepted: 25 July 2013 / Published: 6 August 2013
Cited by 5 | PDF Full-text (683 KB) | HTML Full-text | XML Full-text
Abstract
This article is an overview of the SP theory of intelligence, which aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived of as a [...] Read more.
This article is an overview of the SP theory of intelligence, which aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived of as a brain-like system that receives "New" information and stores some or all of it in compressed form as "Old" information; and it is realised in the form of a computer model, a first version of the SP machine. The matching and unification of patterns and the concept of multiple alignment are central ideas. Using heuristic techniques, the system builds multiple alignments that are "good" in terms of information compression. For each multiple alignment, probabilities may be calculated for associated inferences. Unsupervised learning is done by deriving new structures from partial matches between patterns and via heuristic search for sets of structures that are "good" in terms of information compression. These are normally ones that people judge to be "natural", in accordance with the "DONSVIC" principle—the discovery of natural structures via information compression. The SP theory provides an interpretation for concepts and phenomena in several other areas, including "computing", aspects of mathematics and logic, the representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience and human perception and cognition. Examples include the parsing and production of language with discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks, including "explaining away", causal diagnosis, and the solving of a geometric analogy problem. Full article
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