986 resultados para information metrics


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In this paper metrics for assessing the performance of directional modulation (DM) physical-layer secure wireless systems are discussed. In the paper DM systems are shown to be categorized as static or dynamic. The behavior of each type of system is discussed for QPSK modulation. Besides EVM-like and BER metrics, secrecy rate as used in information theory community is also derived for the purpose of this QPSK DM system evaluation.

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With the advent of digital era web applications have become inevitable part of our lives. We are using the web to manage even the financially or ethically sensitive issues. For this reason exploration of information seeking behavior is an exciting area of research. Current study provides insight on information seeking behavior using a classic ‘Find the Difference’ game. 50 university students between the age of 19 and 26 participated in the study. Eye movement data were recorded with a Tobii T120 device. Participants carried out 4 continuous tasks. Each task included two pictures side by side with 7 hidden differences. After finishing the tasks, participants were asked to repeat the game with the same picture set. This data collection methodology allows the evaluation of learning curves. Additionally, participants were asked about their hand preference. For the purpose of analysis the following metrics were applied: task times (including saccades), fixation count and fixation duration (without saccades). The right- and left-hand side on each picture was selected as AOI (Area of Interest) to detect side preference in connection with hand preference. Results suggest a significant difference between male and female participants regarding aggregated task times (male 58.37s respectively female 68.37s), deviation in the number of fixations and fixation duration (apparently female have less but longer fixations) and also in the distribution of fixations between AOIs. Using eyetracking data current paper highlights the similarities and differences in information acquisition strategies respectively reveals gender and education (Arts vs. Sciences) dependent characteristics of interaction.

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The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

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A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.

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Annual company reports rarely distinguish between domestic and export market performance and even more rarely provide information about annual indicators of a specific export venture's performance. In this study, the authors develop and test a new measure for assessing the annual performance of an export venture (the APEV scale). The new measure comprises five dimensions: (1) annual export venture financial performance, (2) annual export venture strategic performance, (3) annual export venture achievement, (4) contribution of the export venture to annual exporting operations, and (5) satisfaction with annual export venture overall performance. The authors use the APEV scale to generate a scorecard of performance in exporting (the PERFEX scorecard) to assess export performance at the corporate level while comparatively evaluating all export ventures of the firm. Both the scale and the scorecard could help disclose export venture performance and could be useful instruments for annual planning, management, monitoring, and improvement of exporting programs.

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This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.

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Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

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Discriminating DDoS flooding attacks from flash crowds poses a tough challenge for the network security community. Because of the vulnerability of the original design of the Internet, attackers can easily mimic the patterns of legitimate network traffic to fly under the radar. The existing fingerprint or feature based algorithms are incapable to detect new attack strategies. In this paper, we aim to differentiate DDoS attack flows from flash crowds. We are motivated by the following fact: the attack flows are generated by the same prebuilt program (attack tools), however, flash crowds come from randomly distributed users all over the Internet. Therefore, the flow similarity among DDoS attack flows is much stronger than that among flash crowds. We employ abstract distance metrics, the Jeffrey distance, the Sibson distance, and the Hellinger distance to measure the similarity among flows to achieve our goal. We compared the three metrics and found that the Sibson distance is the most suitable one for our purpose. We apply our algorithm to the real datasets and the results indicate that the proposed algorithm can differentiate them with an accuracy around 65%.

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Realizing value from IT investments continues to be a challenge for most healthcare organizations. IT governance (ITG) is envisaged to solve many of these challenges. ITG is the practice that establishes accountability framework for IT investments by allocating decision rights among major participants involved in IT decision processes. As ITG is relatively new in healthcare industry, it is expected that knowledge about how healthcare organizations govern their IT decisions is limited. This research aims to extend this knowledge and to assist both researchers and professionals by providing insights on how IT decisions are made and governed in healthcare organizations (HOs). This research adopts case-study methodology to investigate IT governance in two distinctly different HOs. The research findings indicate that HOs implement ITG to achieve alignment between business objectives and IT. Both HOs set up a five-stage IT decision process to identify, evaluate and prioritize IT investment ideas. They also established generic committee-structures that clearly defined roles and decision authorities to govern such process. It is suggested here that ITG in HOs is heavily influenced by strategic priorities, organizational structure, governance experience and governmental initiatives. Effective ITG in HOs is challenged by IT alignment, policy government, involvement of healthcare executives, and lack of business metrics to justify and evaluate decisions. The research proposes recommendations to address these challenges.

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Image fusion process merges two images into a single more informative image. Objective image fusion per- formance metrics rely primarily on measuring the amount of information transferred from each source image into the fused image. Objective image fusion metrics have evolved from image processing dissimilarity metrics. Additionally, researchers have developed many additions to image dissimilarity metrics in order to better value the local fusion worthy features in source images. This paper studies the evolution of objective image fusion performance metrics and their subjective and objective validation. It describes how a fusion performance metric evolves starting with image dissimilarity metrics, its realization into image fusion contexts, its localized weighting factors and the validation process.

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We introduce a dataset of biological, ecological, conservation and legal information for every species and subspecies of Australian bird, 2056 taxa or populations in total. Version 1 contains 230 fields grouped under the following headings: Taxonomy & nomenclature, Phylogeny, Australian population status, Conservation status, Legal status, Distribution, Morphology, Habitat, Food, Behaviour, Breeding, Mobility and Climate metrics. It is envisaged that the dataset will be updated periodically with new data for existing fields and the addition of new fields. The dataset has already had, and will continue to have applications in Australian and international ornithology, especially those that require standard information for a large number of taxa.

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Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.

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The quality and the power of human activities affect the external environment in different ways that can be measured and evaluated by means of several approaches and indicators. While the scientific community has been publishing several proposals for sustainable development indicators, there is still no consensus regarding the best approach to the use of these indicators and their reliability to measure sustainability. It is important, therefore, to question the effectiveness of sustainable development indicators in an effort to continue in the search for sustainability. This paper compares the results obtained with emergy accounting with five global Sustainability Metrics (SMs) proposed in the literature to verify if metrics are communicating coherent and similar information to guide decision makers towards sustainable development. Results obtained using emergy indices are discussed with the aid of emergy ternary diagrams. Metrics are confronted with emergy results, and the degree of variability among them is analyzed using a correlation matrix created for the Mercosur nations. The contrast of results clearly shows that metrics arrive at different interpretations about the sustainability of the nations studied, but also that some metrics may be grouped and used more prudently. Mercosur is presented as a case study to highlight and explain the discrepancies and similarities among Sustainability Metrics, and to expose the extent of emergy accounting. (C) 2010 Elsevier Ltd. All rights reserved.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)