983 resultados para Multiple datasets
Resumo:
A multi-secret sharing scheme allows several secrets to be shared amongst a group of participants. In 2005, Shao and Cao developed a verifiable multi-secret sharing scheme where each participant’s share can be used several times which reduces the number of interactions between the dealer and the group members. In addition some secrets may require a higher security level than others involving the need for different threshold values. Recently Chan and Chang designed such a scheme but their construction only allows a single secret to be shared per threshold value. In this article we combine the previous two approaches to design a multiple time verifiable multi-secret sharing scheme where several secrets can be shared for each threshold value. Since the running time is an important factor for practical applications, we will provide a complexity comparison of our combined approach with respect to the previous schemes.
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The interest in utilising multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in close proximity is growing rapidly. As such, many challenges are presented in the effective coordination and management of these UAVs; converting the current n-to-1 paradigm (n operators operating a single UAV) to the 1-to-n paradigm (one operator managing n UAVs). This paper introduces an Information Abstraction methodology used to produce the functional capability framework initially proposed by Chen et al. and its Level Of Detail (LOD) indexing scale. This framework was validated through comparing the operator workload and Situation Awareness (SA) of three experiment scenarios involving multiple autonomously heterogeneous UAVs. The first scenario was set in a high LOD configuration with highly abstracted UAV functional information; the second scenario was set in a mixed LOD configuration; and the final scenario was set in a low LOD configuration with maximal UAV functional information. Results show that there is a significant statistical decrease in operator workload when a UAV’s functional information is displayed at its physical form (low LOD - maximal information) when comparing to the mixed LOD configuration.
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Mutations in the genes encoding for either the biosynthetic or transcriptional regulation of the anthocyanin pathway have been linked to color phenotypes. Generally, this is a loss of function resulting in a reduction or a change in the distribution of anthocyanin. Here, we describe a rearrangement in the upstream regulatory region of the gene encoding an apple (Malus x domestica) anthocyanin-regulating transcription factor, MYB10. We show that this modification is responsible for increasing the level of anthocyanin throughout the plant to produce a striking phenotype that includes red foliage and red fruit flesh. This rearrangement is a series of multiple repeats, forming a minisatellite-like structure that comprises five direct tandem repeats of a 23-bp sequence. This MYB10 rearrangement is present in all the red foliage apple varieties and species tested but in none of the white fleshed varieties. Transient assays demonstrated that the 23-bp sequence motif is a target of the MYB10 protein itself, and the number of repeat units correlates with an increase in transactivation by MYB10 protein. We show that the repeat motif is capable of binding MYB10 protein in electrophoretic mobility shift assays. Taken together, these results indicate that an allelic rearrangement in the promoter of MYB10 has generated an autoregulatory locus, and this autoregulation is sufficient to account for the increase in MYB10 transcript levels and subsequent ectopic accumulation of anthocyanins throughout the plant.
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This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.
Resumo:
Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.
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This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.
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It has been 21 years since the decision in Rogers v Whitaker and the legal principles concerning informed consent and liability for negligence are still strongly grounded in this landmark High Court decision. This paper considers more recent developments in the law concerning the failure to disclose inherent risks in medical procedures, focusing on the decision in Wallace v Kam [2013] HCA 19. In this case, the appellant underwent a surgical procedure that carried a number of risks. The surgery itself was not performed in a sub-standard way, but the surgeon failed to disclose two risks to the patient, a failure that constituted a breach of the surgeon’s duty of care in negligence. One of the undisclosed risks was considered to be less serious than the other, and this lesser risk eventuated causing injury to the appellant. The more serious risk did not eventuate, but the appellant argued that if the more serious risk had been disclosed, he would have avoided his injuries completely because he would have refused to undergo the procedure. Liability was disputed by the surgeon, with particular reference to causation principles. The High Court of Australia held that the appellant should not be compensated for harm that resulted from a risk he would have been willing to run. We examine the policy reasons underpinning the law of negligence in this specific context and consider some of the issues raised by this unusual case. We question whether some of the judicial reasoning adopted in this case, represents a significant shift in traditional causation principles.
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Voltage rise is the main issue which limits the capacity of Low Voltage (LV) network to accommodate more Renewable Energy (RE) sources. In addition, voltage drop at peak load period is a significant power quality concern. This paper proposes a new robust voltage support strategy based on distributed coordination of multiple distribution static synchronous compensators (DSTATCOMs). The study focuses on LV networks with PV as the RE source for customers. The proposed approach applied to a typical LV network and its advantages are shown comparing with other voltage control strategies.
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This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.
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This paper presents a method to enable a mobile robot working in non-stationary environments to plan its path and localize within multiple map hypotheses simultaneously. The maps are generated using a long-term and short-term memory mechanism that ensures only persistent configurations in the environment are selected to create the maps. In order to evaluate the proposed method, experimentation is conducted in an office environment. Compared to navigation systems that use only one map, our system produces superior path planning and navigation in a non-stationary environment where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners.
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This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.
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This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.
Resumo:
NLS is a stream cipher which was submitted to the eSTREAM project. A linear distinguishing attack against NLS was presented by Cho and Pieprzyk, which was called Crossword Puzzle (CP) attack. NLSv2 is a tweak version of NLS which aims mainly at avoiding the CP attack. In this paper, a new distinguishing attack against NLSv2 is presented. The attack exploits high correlation amongst neighboring bits of the cipher. The paper first shows that the modular addition preserves pairwise correlations as demonstrated by existence of linear approximations with large biases. Next, it shows how to combine these results with the existence of high correlation between bits 29 and 30 of the S-box to obtain a distinguisher whose bias is around 2^−37. Consequently, we claim that NLSv2 is distinguishable from a random cipher after observing around 2^74 keystream words.
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Marine sediments around volcanic islands contain an archive of volcaniclastic deposits, which can be used to reconstruct the volcanic history of an area. Such records hold many advantages over often incomplete terrestrial datasets. This includes the potential for precise and continuous dating of intervening sediment packages, which allow a correlatable and temporally-constrained stratigraphic framework to be constructed across multiple marine sediment cores. Here, we discuss a marine record of eruptive and mass-wasting events spanning ~250 ka offshore of Montserrat, using new data from IODP Expedition 340, as well as previously collected cores. By using a combination of high-resolution oxygen isotope stratigraphy, AMS radiocarbon dating, biostratigraphy of foraminifera and calcareous nannofossils and clast componentry, we identify five major events at Soufriere Hills volcano since 250 ka. Lateral correlation of these events across sediment cores collected offshore of the south and south west of Montserrat, have improved our understanding of the timing, extent and associations between events in this area. Correlations reveal that powerful and potentially erosive density-currents travelled at least 33 km offshore, and demonstrate that marine deposits, produced by eruption-fed and mass-wasting events on volcanic islands, are heterogeneous in their spatial distribution. Thus, multiple drilling/coring sites are needed to reconstruct the full chronostratigraphy of volcanic islands. This multidisciplinary study will be vital to interpreting the chaotic records of submarine landslides at other sites drilled during Expedition 340 and provides a framework that can be applied to the stratigraphic analysis of sediments surrounding other volcanic islands.
Resumo:
The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.