Ranking Information Research Articles

Multi-View Spectral Clustering via Integrating Global and Local Graphs

Robust multi-view spectral clustering (RMSC) minimizes the rank of probability matrix to recover a common transition probability matrix from the matrices calculated by each single view and achieves promising performance. However, for the clustering task, the underlying structure of the low-rank probability matrix is readily accessible. Yet, RMSC ignores a priori target rank information, and it does not efficiently depict the complementary information between different views. To address these problems, we propose a novel multi-view Markov chain spectral clustering method with a priori rank information. To be specific, we encourage the target rank constraint by minimizing the partial sum of singular values instead of the nuclear norm and construct a global graph from the concatenated features to exploit the complementary information embedded in different views. The objective function can be optimized efficiently by using the augmented Lagrangian multiplier algorithm. Extensive experimental results on one synthetic and eight benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches.

Mediated Trust – A Theoretical Framework to Address the Trustworthiness of Technological Trust Mediators

This paper introduces the new theoretical concept of technology mediated trust, or in short, mediated trust to capture and analyze the role of complex techno-social assemblages in trust production and distrust management. Societies are increasingly reliant on digital technologies to collect data; sort, rank and filter users and information in order to ease communication, commerce, or collaboration. This paper considers the impact of these digital practices on the interpersonal and institutional logics of trust production. The first part of the paper argues that globalization and digitalization unleashed a trust crisis, as the traditional institutional and interpersonal logics are not attuned to produce trust and manage distrust at the scale and speed of global networks of communication, media, finance, etc. In the second part, the paper describes how digital intermediation changes the traditional logics of interpersonal trust formation, and creates new trust mediating services. It also considers how old institutional forms of trust production are affected by the fact that these institutions employ digital technologies in their processes, and describes new, technological forms of institutional trust production. Finally, the paper asks the question: why should we trust these technological trust mediators? The analysis concludes at best, it is impossible to establish the trustworthiness trust mediators, at worst, we have no reason to trust them.

Robust Systems of Cooperation in the Presence of Rankings: How Displaying Prosocial Contributions Can Offset the Disruptive Effects of Performance Rankings

Sustaining systems of cooperation in the face of strong self-interest is a subject of longstanding inquiry in the social sciences. Much of this work has focused on understanding the antecedents and outcomes associated with cooperation, noting that the inertial properties of a system should sustain cooperation over time. This paper shifts the focus toward examining how cooperation is maintained in the face of potentially disruptive forces. To advance theory, research, and practice on how to maintain cooperation over time, we examine how systems of cooperation interact with, withstand, or succumb to a potentially disruptive force that is commonplace in organizational contexts: rankings. Using a longitudinal, nodeception, between-groups experimental design, we assess how systems of cooperation respond to the introduction of performance rankings. Examining data from more than 11,000 rounds of decision-making from 592 participants clustered in 74 teams, we find that cooperation plummets when performance rank information is introduced. However, the addition of reputation information — individuals’ histories of prosocial contributions — enables a system of cooperation to withstand the disruptive effects of performance rankings. Actors use reputation information to make decisions that reduce perceived inequity. Our study contributes to theories of cooperation, performance feedback, macro-level prosocial behavior, and management practice.

Virtual Reality. Now.

Abstract The purpose of this article is to introduce and rank information related to virtual reality as a new media phenomenon. In principle, in the Polish nomenclature, the term is so new that it is often confused, incomprehensible. This, in turn, translates into misunderstanding and the lack of the use of this communication channel. The article is a review of literature. In the first part concepts such as augmented reality, augmented virtuality, mixed reality, virtual reality, and immersion will be explained. A short historical outline of the virtual reality will also be shown. Then - in the next part of the article - the author compares this communication channel with well-known, such as the Internet, television, radio. Next the author will determine what are the fields of application of this communication channel and its condition.

Simultaneous Registration of Image Sequences – a novel singular value based images similarity measure

AbstractThe comparison of images is an important task in image processing. For a comparison of two images, a variety of measures has been suggested. However, applications such as dynamic imaging or serial sectioning provide a series of many images to be compared. When these images are to be registered, the standard approach is to sequentially align the j‐th image with respect to its neighbours and sweep with respect to j. One of the disadvantages is that information is distributed only locally. We introduce an alternative so‐called SqN approach. SqN is based on the Schatten‐q‐norm of the image sequence gradients, i.e. rank information of image gradients of the whole image sequence. With this approach, information is transported globally. Our experiments show that SqN gives at least comparable registration results to standard distance measures but its computation is about six times faster.

The impact of information on taste ranking and cultivation method on rice types that protect endangered birds in Japan: Non-hypothetical choice experiment with tasting

We investigate consumers’ reactions to information on rice types produced using a cultivation method that protects the crested ibis (Nipponia nippon), a symbol of the endangered birds of Japan. We employ a non-hypothetical choice experiment with real monetary incentives, in which participants taste three types of rice—Niigata rice, Sado rice, and Sado-Ibis certified rice (Ibis rice)—and choose one to take home. The participants make decisions twice in each choice set, once before and once after tasting. Three information treatments are used: information about taste ranking from chefs and consumers, cultivation method, and no-information. Comparing the expected and actual willingness to pay (WTP) for Ibis rice, only the cultivation method information increases the WTP, which triples. The WTP in the taste ranking information treatment becomes lower among the participants who refer to chefs, but there is no significant difference in preferences between the expected and actual stages among all participants. For Sado rice, the WTP increases when we provide no-information or information on the cultivation method. In both cases, the WTP changes from negative to positive relative to Niigata rice; however, this WTP is less than that for Ibis rice. These results imply that consumers are more sensitive to information of process and effort for a cultivation method that protects endangered species than to the information about taste ranking.

Optimality Conditions for Problems over Symmetric Cones and a Simple Augmented Lagrangian Method

In this work, we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second-order cone programming, and the classical nonlinear programming problems. We explore the possibility of reformulating NSCPs as common nonlinear programs (NLPs), with the aid of squared slack variables. Through this connection, we show how to obtain second-order optimality conditions for NSCPs in an easy manner, thus bypassing a number of difficulties associated to the usual variational analytical approach. We then discuss several aspects of this connection. In particular, we show a “sharp” criterion for membership in a symmetric cone that also encodes rank information. Also, we discuss the possibility of importing convergence results from nonlinear programming to NSCPs, which we illustrate by discussing a simple augmented Lagrangian method for nonlinear symmetric cones. We show that, employing the slack variable approach, we can use the results for NLPs to prove convergence results, thus extending a special case (i.e., the case with strict complementarity) of an earlier result by Sun et al. [Sun D, Sun J, Zhang L (2008) The rate of convergence of the augmented Lagrangian method for nonlinear semidefinite programming. Math. Programming 114(2):349–391] for nonlinear semidefinite programs.

Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS

The AHP–GTMA (analytic hierarchy process and graph theory and matrix approach) has been applied to select the best paper shredder before a company was making a bulk purchase order. However, there is a question as to whether one such relatively recent approach is effective to aid the selection decision problems in industrial/commercial practice. In this paper, a novel multi-measure, rank-based comparative research flow is proposed. The real decision problem case mentioned above is solved using the AHP–GTMA and the AHP–TOPSIS methods, respectively, with relevant datasets sourced. Several measures in the proposed flow, i.e., the arithmetical, geometrical, or even statistical ones, are multiplexed and used to validate the similarity between the rank order vectors (ROVs) (and thus between the final preferential orders determined over the alternatives) that are obtained using these two different methods. While AHP–TOPSIS is a confident multi-attribute decision-making (MADM) approach which has been successfully applied to many other fields, the similarity validated between these individual results using the proposed method is used to confirm the efficacy of the AHP–GTMA approach and to determine its applicability in practice. In addition, along with this study, some contributable points are also rendered for implementing the decision models, e.g., the optimized recursive implementation in R to compute the permanent value of a square ASAM (alternative selection attribute matrix, which is the computational basis required by AHP–GTMA) of any dimension. The proposed methodological flow to confirm the similarity based on the ordinal rank information is not only convenient in operational practice with ubiquitous tool supports (e.g., the vector-based R statistical platform), but also generalizable (to verify between another pair of results obtained using any other MADM methods). This gives options for future research.

Judgment post-stratification in finite mixture modeling: An example in estimating the prevalence of osteoporosis.

Judgment post-stratification is used to supplement observations taken from finite mixture models with additional easy to obtain rank information and incorporate it in the estimation of model parameters. To do this, sampled units are post-stratified on ranks by randomly selecting comparison sets for each unit from the underlying population and assigning ranks to them using available auxiliary information or judgment ranking. This results in a set of independent order statistics from the underlying model, where the number of units in each rank class is random. We consider cases where one or more rankers with different ranking abilities are used to provide judgment ranks. The judgment ranks are then combined to produce a strength of agreement measure for each observation. This strength measure is implemented in the maximum likelihood estimation of model parameters via a suitable expectation maximization algorithm. Simulation studies are conducted to evaluate the performance of the estimators with or without the extra rank information. Results are applied to bone mineral density data from the third National Health and Nutrition Examination Survey to estimate the prevalence of osteoporosis in adult women aged 50 and over.

Academic productivity in surgical oncology: Where is the bar set for those training the next generation?

Promotion and tenure are important milestones for academic surgical oncologists. The aim of this study was to quantify academic metrics associated with rank in surgical oncologists training the next generation. Faculty were identified from accredited surgical oncology fellowships in the United States. Scopus was used to obtain the number of publications/citations and h-index values. The National Institutes of Health (NIH) RePORT website was used to identify funding history. Of the 319 surgeons identified, complete rank information was obtained for 308. The majority of faculty were men (70%) and only 11% of full professors were women. The median h-index values were 7, 17, and 39 for assistant, associate, and full professors, respectively. While 50% of full professors had a history of NIH funding, only 26%had RO1s and 20% had current NIH funding. Using multivariate analysis, years in practice, h-index, and a history of NIH funding were associated with academic rank (P < .05). Objective benchmarks, such as the median h-index and NIH funding, provide additional insights for both junior faculty and leadership into the productivity needed to attain promotion to the next academic rank for surgical oncologists.

Saliency enabled compression in JPEG framework

Under low bit-rate requirements, JPEG baseline causes compression artifacts in the image. Through this paper, a novel region-of-interest (ROI) dependent quantization method in JPEG framework is proposed. The proposed method judiciously quantizes DCT coefficients belonging to salient and non-salient regions of the image. In this work, multiple ROIs are optimally identified and ranked by using variances. The number of classes is adaptively calculated using goodness-of-segmentation. After the number of classes and their ranks are obtained, the image is divided into blocks of size . These blocks may belong to more than one class and hence these are ranked based on their membership in various classes. 2D-DCT coefficients of each block are obtained and then quantized adaptively based on their ranks. Overhead for rank information of blocks is minimized by applying delta-encoding. Results are analyzed in terms of objective quality parameters and visual perception and found that the blocking artifacts in the proposed method are significantly lower than JPEG. The efficiency of the proposed method is demonstrated by results of recently published similar methods and the former is found superior in terms of quality of the reconstructed image.

Random forest for label ranking

Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.

Detection of rank attack in routing protocol for Low Power and Lossy Networks

Internet Engineering Task Force (IETF) has recommended the use of routing protocol for Low Power and Lossy Network (RPL) for Internet Protocol version 6 (IPv6) enabled Internet of Things. However, RPL is vulnerable to internal and external attacks in a network. A malicious node in a rank attack, which is consumed by its child nodes, advertises false rank information. This consequently causes the selection of a malicious node as preferred parent for routing information to the sink node. Given the widespread application of RPL protocol in smart homes, smart cities, and the smart world, it is imperative to address this problem. In this paper, a novel Sink-based intrusion detection system (SBIDS) for the detection of rank attack in RPL is presented. SBIDS has less computational overhead as all detection processes take place at the sink node, which saves network resources. Through a comprehensive simulation analysis, it is shown that the proposed SBIDS provides high detection rate.

Quantile regression with nominated samples: An application to a bone mineral density study.

This paper studies quantile regression analysis with maxima or minima nomination sampling designs. These designs are often used to obtain more representative samples from the tails of the underlying distribution using the easy to access rank information during the sampling process. We propose new loss functions to incorporate the rank information of nominated samples in the estimation process. Also, we provide an alternative approach that translates estimation problems with nominated samples to corresponding problems under simple random sampling (SRS). Strategies are given to choose proper nomination sampling designs for a given population quantile. Numerical studies show that quantile regression models with maxima (or minima) nominated samples have higher relative efficiencies compared with their counterparts under SRS for analyzing the upper (or lower) tail quantiles of the distribution of the response variable. Results are then implemented on a large cohort study in the Canadian province of Manitoba to analyze quantiles of bone mineral density using available covariates. We show that in some cases, methods based on nomination sampling designs require about one-tenth of the sample used in SRS to estimate the lower or upper tail conditional quantiles with comparable mean squared errors. This is a dramatic reduction in time and cost compared with the usual SRS approach.

Proportion estimation in ranked set sampling in the presence of tie information

Ranked set sampling (RSS) is a statistical technique that uses auxiliary ranking information of unmeasured sample units in an attempt to select a more representative sample that provides better estimation of population parameters than simple random sampling. However, the use of RSS can be hampered by the fact that a complete ranking of units in each set must be specified when implementing RSS. Recently, to allow ties declared as needed, Frey (Environ Ecol Stat 19(3):309–326, 2012) proposed a modification of RSS, which is to simply break ties at random so that a standard ranked set sample is obtained, and meanwhile record the tie structure for use in estimation. Under this RSS variation, several mean estimators were developed and their performance was compared via simulation, with focus on continuous outcome variables. We extend the work of Frey (2012) to binary outcomes and investigate three nonparametric and three likelihood-based proportion estimators (with/without utilizing tie information), among which four are directly extended from existing estimators and the other two are novel. Under different tie-generating mechanisms, we compare the performance of these estimators and draw conclusions based on both simulation and a data example about breast cancer prevalence. Suggestions are made about the choice of the proportion estimator in general.

Fusion-based methods for result diversification in web search

Search result diversification of text documents is especially necessary when a user issues a faceted or ambiguous query to the search engine. A variety of approaches have been proposed to deal with this issue in recent years. In this article, we propose a group of fusion-based result diversification methods with the aim to improve performance that considers both relevance and diversity. They are linear combinations of scores that are obtained from different component search systems. The weight of each search system is determined by considering three factors: performance, dissimilarity, and complementarity. There are two major contributions. Firstly, we find that all the three factors of performance and complementarity and dissimilarity are useful for effective weighting of linear combination. Secondly, we present the logarithmic function-based model for converting ranking information into scores. Experiments are carried out with four groups of results submitted to the TREC web diversity task. Experimental results show that some of the fusion methods that use the aforementioned techniques perform more effectively than the state-of-the-art fusion methods for result diversification.

Some perspectives on nonparametric statistical process control

ABSTRACTStatistical process control (SPC) charts play a central role in quality control and management. Many conventional SPC charts are designed under the assumption that the related process distribution is normal. In practice, the normality assumption is often invalid. In such cases, some articles show that certain conventional SPC charts are robust and can still be used as long as their parameters are properly chosen. Some other articles argue that results from such conventional SPC charts would not be reliable and that nonparametric SPC charts should be considered instead. In recent years, many nonparametric SPC charts have been proposed. Most of them are based on the ranking information in process observations collected at different time points. Some of them are based on data categorization and categorical data analysis. In this article, we give some perspectives on issues related to the robustness of conventional SPC charts and to the strengths and limitations of various nonparametric SPC charts.

Distribution function estimation using concomitant-based ranked set sampling

Ranked set sampling (RSS) is a data collection method designed to exploit auxiliary ranking information. In this paper, a new estimator of distribution function is proposed when RSS is done by using a con comitant variable. It is shown by simulation study that the alternative estimator can be considerably more efficient than the standard one, especially when the rankings are perfect.

UDLF

The Unsupervised Distance Learning Framework (UDLF) [1] is a software developed to facilitate the general use and evaluation of novel unsupervised learning methods. These methods aim at post-processing the ranking information for different tasks, being especially useful for multimedia retrieval. The major advantage of UDLF is that it provides a unified and extensive model for implementing different unsupervised methods. Any execution or experiment can be easily conducted by setting a configuration file, no recompilation is required. To evaluate the retrieval results, the framework computes different effectiveness and efficiency measures and it allows for exporting visual output results to external files. UDLF is written in C++, works out of the box, does not require external libraries, and is compatible with different operating systems including Linux, MacOS, and Windows. The source code is publicly available on GitHub under the terms of the GPLv2 license.

PENGUKURAN KINERJA PEMBANGUNAN MANUSIA DESA/KECAMATAN KABUPATEN SERAM BAGIAN BARAT DENGAN MENGGUNAKAN ANALISIS MULTIVARIAT

The first purpose of this paper is to develop or construct a new human development composite index and applied to measure the performance of human development in the village / district of West Seram Regency. The second, to develop the priority scale of human development planning. This paper applies quantitative analysis method that is principal component regression (PCR) and clustering analysis (CA). Data is sourced from the Regional Development Planning Agency (Bappeda) of West Seram Regency of year 2016. Application of principal component regression and clustering analysis method, aims to improve the aggregation method of human development composite index developed by UNDP. Composite index compositions resulting from principal component regression and IPM-UNDP largely result in different ranking information but in some villages have the same rank. Based on K-means clustering analysis, there were 3 main clusters, namely high, medium and low cluster. The number of villages in the high cluster is 6 villages, the medium cluster is 13 villages and low cluster is 14 villages.