Local Adjustment Method Research Articles

A Local Adjustment Method to Improve Multiplicative Consistency of Fuzzy Reciprocal Preference Relations

Preferences that verify the transitivity property are usually referred to as rational or consistent preferences. Existent methods to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) fail to retain the original preference values because they always derive a new FPR. This article presents a new inconsistency identification and modification (IIM) method to detect and rectify only the most inconsistent elements of an inconsistent FPR. As such, the proposed IIM can be considered a local adjustment method to improve multiplicative consistency (MC) of FPRs. The case of inconsistent FPRs with missing values, i.e., incomplete FPRs, is addressed with the estimation of the missing preferences with a constrained nonlinear optimization model by the application of the IIM method. The implementation process of the proposed algorithms is illustrated with numerical examples. Simulation experiments and comparisons with existent methods are also included to show that the new method requires fewer iterations than existent methods to improve the MC of FPRs and achieves better MC level, while preserving the original preference information as much as possible than the existent methods. Thus, the results presented in this article demonstrate the correctness, effectiveness, and robustness of the proposed method.

Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

Locations are usually necessary for task allocation in spatial crowdsourcing, which may put individual privacy in jeopardy without proper protection. Although existing studies have well explored the problem of location privacy protection in task allocation under geo-indistinguishability, they potentially assume the workers could perform any tasks, which might not be practical in reality. Moreover, they usually adopt planar laplacian mechanism to achieve geo-indistinguishability, which will introduce excessive noise due to its randomness and boundlessness. To this end, we propose a task allo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CA</b> tio <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> approach via gr <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> up-based nois <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> addition under Geo-I, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Its main idea is that each worker uploads the noisy distances between his true location and the obfuscated locations of his preferred tasks instead of uploading his obfuscated location. In particular, to alleviate the total noise when conducting grouping, we put forward an optimized global grouping with adaptive local adjustment method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OGAL</i> with convergence guarantee. To collect the noisy distances which are required for subsequent task allocation, we develop a utility-aware obfuscated distance collection method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UODC</i> with solid privacy and utility guarantees. We further theoretically analyze the privacy, utility and complexity guarantees of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Extensive analyses and experiments over two real-world datasets confirm the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> .

Robust plant cell tracking using local spatio-temporal context

Automated segmentation and tracking of cells in actively developing plant tissues can provide high-throughput and quantitative spatio-temporal measurements of a range of cell behaviors. In this paper, we propose an automated segmentation and tracking method for the shoot apical meristem cells based on the cells׳ spatio-temporal contextual information. The cells are properly segmented and then tracked by using the proposed Triangle Neighborhood Structure matching method, which exploits the cells’ spatial context and turns out to be more robust than the other local graph matching methods. The tracking output acts as an indicator of the quality of segmentation and, in turn, the over-segmentation and under-segmentation errors are corrected by a local adjustment method, which is proved to be much more accurate and efficient than the global correction method. Furthermore, the cells׳ lineage tracklets are associated by using the cells׳ spatio–temporal contextual information to obtain long-term lineages. Our results on two datasets validate the effectiveness of the proposed method and we are able to track 99% of the plant cells across a long-term time period.

Local adjustment method for field experiments. 1. The method and its examination by computer simulation

The modification of ‘the nearest neighbour’ method for using various size plots in field trials has been developed and the results of testing its validity by computer simulation are presented. It was found that the Field-S variant of this method using a square pilot area is the best for fields with a low and medium spatial heterogeneity. The Field-L variant using the least quantity of neighbours, offers advantages for fields with a medium and high spatial effect. The optimal values of the controlling coefficients of these variants were computed to allow for the calculation of the environmental indices over a wide interval of spatial heterogeneity with sufficient accuracy. For this reason, this local adjustment method is recommended as an obligatory statistical procedure for a prior data analysis of any field experiments that have fulfilled the necessary requirements. The original Field and CRBSD programs using the local adjustment method developed have been created for IBM-compatible personal computers.

Local adjustment method for field experiments. 2. Application in experiments with and without replications

Presented are the results of an application of the local adjustment Field-method in the analysis of the real field experiments without replications and in experiments with pseudo- and random replications. It was found that the local adjustment allowed a more precise estimation of the real values of genotypes in all kinds of experiments over a wide interval of spatial heterogeneity. The results obtained indicate that by simply using up to 5 random replications the decrease of the spatial effects is inadequate for fields with medium and high soil heterogeneity or with unsuccessful randomization. The Field-method which has sufficient precision in evaluating spatial heterogeneity, considerably increases the reliability of estimates in experiments even with a small number of replications or with unsuccessful randomization. Therefore, for all field experiments that have fulfilled the necessary requirements the Field-method is recommended as an obligatory statistical procedure for a prior data analysis as it allows an accurate estimation of the real character expression of genotypes, as well as the random variance in experiments without replication and the parameters of ANOVA in ones with pseudo- and random replications.