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• Only two U.S. national surveys, PATH and NSDUH, measure dual blunt/cigar use. • Findings validate ordinal measure of blunt/cigar use in PATH survey. • Ordinal measure should be separately assessed for cigarillo and any cigar use. • Ordinal measure should be implemented in other national surveys (e.g., NSDUH). Researchers have drawn attention to the need for modifying survey questions on cigars for distinguishing use intended for tobacco versus cannabis (i.e. blunt) consumption. Yet, most surveys do not distinguish persons who only smoke blunts (POSB) from persons who smoke blunts and unmodified cigars/cigarillos (PSBC). This study was intended to evaluate existing measures in U.S. national surveys for establishing a standard for the field. Two of six leading U.S national surveys, the NSDUH and PATH, measured dual use of blunts and cigars. The analytical sample of this study included adult participants of the 2017 NSDUH (n = 2493) and Wave 4 PATH (n = 3175) who smoked a cigar or blunt in the past month and reported cigar brand usually or last smoked; the latter was used as a validation measure. Odds of using Swisher Sweets and other brands (vs. Black & Mild) increased with more frequent blunt use relative to persons who only smoked unmodified cigars/cigarillos (POSC). Further, regression coefficients for the three subgroups of PSBC differed significantly, highlighting the utility of an ordinal versus aggregated measure. Estimates of the former were diminished in magnitude upon expanding the sample to persons who smoked any cigar product. Validation of the ordinal measure of blunt-cigarillo use in PATH supports the measure's implementation as a standard for U.S. national surveys. Implementation of the measure in other surveys (e.g., NSDUH) would provide a more consistent and accurate assessment of blunt and cigar use for monitoring health risks. [ABSTRACT FROM AUTHOR]

Nicholas Georgescu-Roegen (1971) educated economists on the notion of entropy laws in economics and ecological process. An earlier paper by Kenneth E. Boulding (1962) asked what we might do with a measure of organizational entropy, were one ever devised. The aim of this paper is to propose the notion of planning horizons as a candidate for this role. First, the concept of organizational entropy is discussed and defined within the interdependent domain of ecological economics. Next, the character and contributions of an entropic measure of organization are reviewed, as described in Boulding’s work. Third, the concept of planning horizons – and their relation to economic cohesion, efficiency and well-being – is introduced to show how ‘horizon effects’ (shifts in planning horizons) serve as an ordinal entropic measure of organization in dynamic complex settings of interdependent effects. Last, the promise of planning horizons as a new social research program in ecological economics shall be discussed.

Background and aims The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores. Methods We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations. Results We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions. Conclusions and Implications We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann–Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods ( t -test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods. [ABSTRACT FROM AUTHOR]

Nicholas Georgescu-Roegen (1971) educated economists on the notion of entropy laws in economics and ecological process. An earlier paper by Kenneth E. Boulding (1962) asked what we might do with a measure of organizational entropy, were one ever devised. The aim of this paper is to propose the notion of planning horizons as a candidate for this role. First, the concept of organizational entropy is discussed and defined within the interdependent domain of ecological economics. Next, the character and contributions of an entropic measure of organization are reviewed, as described in Boulding's work. Third, the concept of planning horizons - and their relation to economic cohesion, efficiency and well-being - is introduced to show how 'horizon effects' (shifts in planning horizons) serve as an ordinal entropic measure of organization in dynamic complex settings of interdependent effects. Last, the promise of planning horizons as a new social research program in ecological economics shall be discussed. [ABSTRACT FROM AUTHOR]

Computer vision researchers have developed various learning methods based on the bag of words model for image related tasks, including image categorization and image retrieval. In this model, images are represented as histograms of visual words from a vocabulary that is obtained by clustering local image descriptors. Next, a classifier is trained on the data. Most often, the learning method is a kernel-based one. Various kernels, such as the linear kernel, the intersection kernel, the χ 2 kernel or the Jensen-Shannon kernel, can be plugged into the kernel method. Recent results indicate that the novel PQ kernel of Ionescu and Popescu [8] seems to improve the accuracy over most of the state of the art kernels. The PQ kernel is inspired from a set of rank correlation statistics specific for ordinal data, that are based on counting concordant and discordant pairs among two variables. This paper describes an algorithm to compute the PQ kernel in O ( n APTARANORMAL log n ) time, based on merge sort. Matlab and C/C++ implementations are provided for future development and use at http://pq-kernel.herokuapp.com . Extensive object recognition experiments are conducted to compare the PQ kernel with other state of the art kernels on two benchmark data sets. The PQ kernel has the best results on both data sets, even when a spatial pyramid representation is used. In conclusion, the PQ kernel can be used to obtain a better pairwise similarity between visual word histograms, which, in turn, improves the object recognition accuracy of the bag of visual words system. [ABSTRACT FROM AUTHOR]

To address digital video copyright protection issue, this paper proposed a method which was based on ORB local feature that was a binary feature descriptor and ordinal measure global feature for video copy detection. The method detected shot from video through comparing Bhattacharyya distance of the adjacent frames' gray histogram and selected the first frame in shot as a key-frame. Then it extracted the ordinal measure feature and ORB feature from key-frames respectively. When query video came, it used ordinal measure feature to get the first result which could remove some irrelevant videos and then obtained the video copy result through ORB feature. Experimental results show the effectiveness and usefulness of the proposed approach for video copy detection, both the accuracy and recall are above 80%. [ABSTRACT FROM AUTHOR]

Nowadays, more and more multimedia websites appear in social network. It brings some security problems, such as privacy, piracy, disclosure of sensitive contents and so on. Aiming at copyright protection, the copy detection technology of multimedia contents becomes a hot topic. In our previous work, a new computer-based copyright control system used to detect the media has been proposed. Based on this system, this paper proposes an improved media feature matching measure and an entropy based copy detection method. The Levenshtein Distance was used to enhance the matching degree when using for feature matching measure in copy detection. For entropy based copy detection, we make a fusion of the two features of entropy matrix of the entropy feature we extracted. Firstly,we extract the entropy matrix of the image and normalize it. Then, we make a fusion of the eigenvalue feature and the transfer matrix feature of the entropy matrix. The fused features will be used for image copy detection. The experiments show that compared to use these two kinds of features for image detection singly, using feature fusion matching method is apparent robustness and effectiveness. The fused feature has a high detection for copy images which have been received some attacks such as noise, compression, zoom, rotation and so on. Comparing with referred methods, the method proposed is more intelligent and can be achieved good performance.

Searching for near-duplicate content has become an important task in many multimedia applications, for example, images, videos and music. The ability to detect duplicate videos plays an important role in several video applications, for example, effective video search, copyright infringement and the study on users’ behaviour on near-duplicate video production. Current web video search systems rely only on text keywords and, hence, fail to detect many duplicate videos. In this paper, we analyse the problem of near-duplicate detection and propose a practical solution for real-time large-scale video retrieval. Unlike many existing approaches which make use of video frames or key-frames, our solution is based on a more discriminative signature of video clips. The feature used in this paper is an extension of ordinal measures which have proven to be robust to change in brightness, compression formats and compression ratios. For efficient retrieval, we propose to use multi-probe locality sensitive hashing (MPLSH) to index the video clips for fast similarity search and high recall. MPLSH is able to filter out a large number of dissimilar clips from video database. To refine the search process, we apply a similarity voting based on video clip signatures. Experimental results on the dataset of 12,790 web videos show that the proposed approach improves average precision over the baseline colour histogram approach while satisfying real-time requirements. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel video sequence matching method based on temporal ordinal measurements. Each frame is divided into a grid and corresponding grids along a time series are sorted in an ordinal ranking sequence, which gives a global and local description of temporal variation. A video sequence matching means not only finding which video a query belongs to, but also a precise temporal localization. Robustness and discriminability are two important issues of video sequence matching. A quantitative method is also presented to measure the robustness and discriminability attributes of the matching methods. Experiments are conducted on a BBC open news archive with a comparison of several methods. [Copyright &y& Elsevier]

Conventional image copy detection research concentrates on finding features that are robust enough to resist various kinds of image attacks. However, finding a globally effective feature is difficult and, in many cases, domain dependent. Instead of simply extracting features from copyrighted images directly, we propose a new framework called the extended feature set for detecting copies of images. In our approach, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. The copy-detection problem can be solved by learning classifiers from the training data, thus, generated. Our approach can be integrated into existing copy detectors to further improve their performance. Experiment results demonstrate that the proposed approach can substantially enhance the accuracy of copy detection. [ABSTRACT FROM AUTHOR]

Questions: Are ordinal data appropriately treated by multivariate methods in numerical ecology? If not, what are the most common mistakes? Which dissimilarity coefficients, ordination and classification methods are best suited to ordinal data? Should we worry about such problems at all? Methods: A new classification model family, OrdClAn (Ordinal Cluster Analysis), is suggested for hierarchical and non- hierarchical classifications from ordinal ecological data, e.g. the abundance/dominance scores that are commonly recorded in relevés. During the clustering process, the objects are grouped so as to minimize a measure calculated from the ranks of within-cluster and between-cluster distances or dissimilarities. Results and Conclusions: Evaluation of the various steps of exploratory data analysis of ordinal ecological data shows that consistency of methodology throughout the study is of primary importance. In an optimal situation, each methodological step is order invariant. This property ensures that the results are independent of changes not affecting ordinal relationships, and guarantees that no illusory precision is introduced into the analysis. However, the multivariate procedures that are most commonly applied in numerical ecology do not satisfy these requirements and are therefore not recommended. For example, it is inappropriate to analyse Braun-Blanquet abudance/dominance data by methods assuming that Euclidean distance is meaningful. The solution of all problems is that the dissimilarity coefficient should be compatible with ordinal variables and the subsequent ordination or clustering method should consider only the rank order of dissimilarities. A range of artificial data sets exemplifying different subtypes of ordinal variables, e.g. indicator values or species scores from relevés, illustrate the advocated approach. Detailed analyses of an actual phytosociological data set demonstrate the classification by OrdClAn of relevés and species and the subsequent tabular rearrangement, in a numerical study remaining within the ordinal domain from the first step to the last. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to detect copied versions of digital images. The proposed copy detection scheme can be used as either an alternative approach or a complementary approach to watermarking. A test image is reduced to an 8×8 sub-image by intensity averaging, and the AC coefficients of its discrete cosine transform (DCT) are used to compute distance from those generated from the query image, of which a user wants to find copies. A challenge is that the replicated image may be processed to elude copy detection or enhance image quality. We show ordinal measure of DCT coefficients, which is based on relative ordering of AC magnitude values and using distance metrics between two rank permutations, is robust to various modifications of the original image. The optimal threshold selection scheme using the maximum a posteriori criterion is also described. The efficacy of the proposed method is extensively tested with both cluster-free and cluster-based detection scheme. [Copyright &y& Elsevier]