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1.
Various techniques have been developed for different query types in content-based image retrieval systems such as sampling
queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate
efficient processing techniques for this new framework. We exploit sequential access and data sharing by developing new storage
and query processing techniques to leverage inter-query concurrency. Our experimental results, based on the Corel dataset,
indicate that the proposed optimization can significantly reduce average response time in a multiuser environment, and achieve
better retrieval precision and recall compared to two recent techniques.
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2.
Text search engines are inadequate for indexing and searching XML documents because they ignore metadata and aggregation structure
implicit in the XML documents. On the other hand, the query languages supported by specialized XML search engines are very
complex. In this paper, we present a simple yet flexible query language, and develop its semantics to enable intuitively appealing
extraction of relevant fragments of information while simultaneously falling back on retrieval through plain text search if necessary. Our approach combines and generalizes several available techniques to obtain precise
and coherent results.
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3.
Existing estimation approaches for spatial databases often rely on the assumption that data distribution in a small region
is uniform, which seldom holds in practice. Moreover, their applicability is limited to specific estimation tasks under certain
distance metric. This paper develops the Power-method, a comprehensive technique applicable to a wide range of query optimization problems under both L∞ and L2 metrics. The Power-method eliminates the local uniformity assumption and is, therefore, accurate even for datasets where
existing approaches fail. Furthermore, it performs estimation by evaluating only one simple formula with minimal computational
overhead. Extensive experiments confirm that the Power-method outperforms previous techniques in terms of accuracy and applicability
to various optimization scenarios.
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4.
We have proposed the EXTENT system for automated photograph annotation using image content and context analysis. A key component
of EXTENT is a Landmark recognition system called LandMarker. In this paper, we present the architecture of LandMarker. The content of a query photograph is analyzed and compared against a database of sample landmark images, to recognize any
landmarks it contains. An algorithm is presented for comparing a query image with a sample image. Context information may
be used to assist landmark recognition. Also, we show how LandMarker deals with scalability to allow recognition of a large number of landmarks. We have implemented a prototype of the system,
and present empirical results on a large dataset.
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5.
We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed
about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked
on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive,
although they had several important reservations about camera-phone technology more generally. Data from our pointing-device
task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device
trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings,
design lessons for camera-phone and visual-tag applications are presented.
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6.
Modular redundancy and temporal redundancy are traditional techniques to increase system reliability. In addition to being used as temporal redundancy, with
technology advancements, slack time in a system can also be used by energy management schemes to save energy. In this paper,
we consider the combination of modular and temporal redundancy to achieve energy efficient reliable real-time service provided
by multiple servers. We first propose an efficient adaptive parallel recovery scheme that appropriately processes service requests in parallel to increase the number of faults that can be tolerated and
thus system reliability. Then we explore schemes to determine the optimal redundant configurations of the parallel servers to minimize system energy consumption for a given reliability goal or to maximize system reliability
for a given energy budget. Our analysis results show that small requests, optimistic approaches, and parallel recovery favor
lower levels of modular redundancy, while large requests, pessimistic approaches and restricted serial recovery favor higher
levels of modular redundancy.
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7.
We introduce the concept of an elastic block cipher which refers to stretching the supported block size of a block cipher to any length up to twice the original block size while
incurring a computational workload that is proportional to the block size. Our method uses the round function of an existing
block cipher as a black box and inserts it into a substitution- permutation network. Our method is designed to enable us to
form a reduction between the elastic and the original versions of the cipher. Using this reduction, we prove that the elastic
version of a cipher is secure against key-recovery attacks if the original cipher is secure against such attacks. We note
that while reduction-based proofs of security are a cornerstone of cryptographic analysis, they are typical when complete
components are used as sub-components in a larger design. We are not aware of the use of such techniques in the case of concrete
block cipher designs. We demonstrate the general applicability of the elastic block cipher method by constructing examples
from existing block ciphers: AES, Camellia, MISTY1, and RC6. We compare the performance of the elastic versions to that of
the original versions and evaluate the elastic versions using statistical tests measuring the randomness of the ciphertext.
We also use our examples to demonstrate the concept of a generic key schedule for block ciphers.
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8.
We compare inexact Newton and block coordinate descent optimization methods for improving the quality of a mesh by repositioning the vertices, where the overall quality is measured by the harmonic mean of the mean-ratio metric. The effects of problem size, element size heterogeneity, and various vertex displacement schemes on the performance of these algorithms are assessed for a series of tetrahedral meshes. 相似文献
9.
This paper focuses on the issue of OWL-DL ontology queries implemented in SPARQL. Currently, ontology repositories construct
inference ontology models, and match SPARQL queries to the models, to derive inference results. Because an inference model
uses much more storage space than the original model, and cannot be reused as inference requirements vary, this method is
not suitable for large-scale deployment. To solve this problem, this paper proposes a novel method that passes rewritten SPARQL
queries to the original ontology model, to retrieve inference results. We define OWL-DL inference rules and apply them to
rewriting Graph Patterns in queries. The paper classifies the inference rules and discusses how these rules affect query rewriting.
To illustrate the advantages of our proposal, we present a prototype system based on Jena, and address query optimization,
to eliminate the disadvantages of augmented query sentences. We perform a set of query tests and compare the results with
related works. The results show that the proposed method results in significantly improved query efficiency, without compromising
completeness or soundness.
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10.
We propose a SAO index to approximately answer arbitrary linear optimization queries in a sliding window of a data stream.
It uses limited memory to maintain the most “important” tuples. At any time, for any linear optimization query, we can retrieve
the approximate top- K tuples in the sliding window almost instantly. The larger the amount of available memory, the better the quality of the answers
is. More importantly, for a given amount of memory, the quality of the answers can be further improved by dynamically allocating
a larger portion of the memory to the outer layers of the SAO index.
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11.
In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the advantages and disadvantages of these techniques in time-series forecasting using Spanish bank and company stocks. Then, we describe a new hybrid model for time-series forecasting which combines ANNs with genetic algorithms (GAs). We also describe the possibilities when implementing the model on parallel processing systems. 相似文献
12.
Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed. 相似文献
13.
Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature.
Association rule mining is a computationally and I/ O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders.
We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering
techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach,
extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering
the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested
using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly
based intrusions. Experiments show that the proposed method is efficient in many scenarios.
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14.
This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary
Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition
and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational
intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process,
we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.
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15.
In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence
of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in
families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology
we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls
to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls
in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities
show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness
performances efficiently.
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16.
Tracking location is challenging due to the numerous constraints of practical systems including, but not limited to global
cost, device volume and weight, scalability and accuracy; these constraints are typically more severe for systems that should
be wearable and used indoors. We investigate the use of wearable solar cells to track changing light conditions (a concept
that we named LuxTrace) as a source of user displacement and activity data. We evaluate constraints of this approach and present
results from an experimental validation of displacement and activity estimation. The results indicate that a distance estimation
accuracy of 21 cm (80% quantile) can be achieved. A simple method to combine LuxTrace with complementary absolute location
estimation methods is also presented. We apply carpet-like distributed RFID tags to demonstrate online learning of new lighting
environments.
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17.
Query refinement and feature re-weighting are the two core techniques underlying the relevance feedback of content-based image
retrieval. Most existing relevance feedback mechanisms generally model the user’s query target with a single query point and
weight each extracted feature with a single importance factor. A designed estimation procedure then estimates the best query
point and all importance factors by optimizing a formulated criterion which measures the goodness of the estimation. This
formulated criterion simultaneously encapsulates all positive and negative examples supplied from the user’s feedback. Under
such formulation, the positive and negative examples may contribute contradictorily to the criterion and sometimes may introduce
higher difficulty in attaining a good estimation. In this paper, we propose a different statistical formulation to estimate
independently two pairs of query points and feature weights from the positive examples and negative examples, respectively.
These two pairs then define the likelihood ratio, a criterion term used to rank the relevance of all database images. This
approach simplifies the criterion formulation and also avoids the mutual impeditive influence between positive examples and
negative examples. The experimental results demonstrate that the proposed approach outperforms some other related approaches.
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18.
There are only a few ethical regulations that deal explicitly with robots, in contrast to a vast number of regulations, which
may be applied. We will focus on ethical issues with regard to “responsibility and autonomous robots”, “machines as a replacement
for humans”, and “tele-presence”. Furthermore we will examine examples from special fields of application (medicine and healthcare,
armed forces, and entertainment). We do not claim to present a complete list of ethical issue nor of regulations in the field
of robotics, but we will demonstrate that there are legal challenges with regard to these issues.
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19.
A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns
about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the
extent that these concerns are adequately addressed. In this article, we report on our work on P eopleF inder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends,
family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards
privacy as they interact with such an application, and to explore technologies that empower users to more effectively and
efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules
and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present
evaluations of these technologies in the context of one laboratory study and three field studies.
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20.
We present a framework that supports the formal verification of early requirements specifications. The framework is based on Formal Tropos, a specification language that adopts primitive concepts for modeling early requirements (such as actor, goal, and strategic dependency), along with a rich temporal specification language. We show how existing formal analysis techniques, and in particular model checking, can be adapted for the automatic verification of Formal Tropos specifications. These techniques have been implemented in a tool, called the T-Tool, that maps Formal Tropos specifications into a language that can be handled by the NuSMV model checker. Finally, we evaluate our methodology on a course-exam management case study. Our experiments show that formal analysis reveals gaps and inconsistencies in early requirements specifications that are by no means trivial to discover without the help of formal analysis tools. 相似文献
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