The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy

Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.

Ordinal Pattern Dependence in the Context of Long-Range Dependence

Ordinal pattern dependence is a multivariate dependence measure based on the co-movement of two time series. In strong connection to ordinal time series analysis, the ordinal information is taken into account to derive robust results on the dependence between the two processes. This article deals with ordinal pattern dependence for a long-range dependent time series including mixed cases of short- and long-range dependence. We investigate the limit distributions for estimators of ordinal pattern dependence. In doing so, we point out the differences that arise for the underlying time series having different dependence structures. Depending on these assumptions, central and non-central limit theorems are proven. The limit distributions for the latter ones can be included in the class of multivariate Rosenblatt processes. Finally, a simulation study is provided to illustrate our theoretical findings.

Processing of Ordinal Information in Math-Anxious Individuals

This study aimed to investigate whether the ordinal judgments of high math-anxious (HMA) and low math-anxious (LMA) individuals differ. Two groups of 20 participants with extreme scores on the Shortened Mathematics Anxiety Rating Scale (sMARS) had to decide whether a triplet of numbers was presented in ascending order. Triplets could contain one-digit or two-digit numbers and be formed by consecutive numbers (counting condition), numbers with a constant distance of two or three (balanced) or numbers with variable distances between them (neutral). All these triplets were also presented unordered: sequence order in these trials could be broken at the second (D2) or third (D3) number. A reverse distance effect (worse performance for ordered balanced than for counting trials) of equal size was found in both anxiety groups. However, HMA participants made more judgment errors than their LMA peers when they judged one-digit counting ordered triplets. This effect was related to worse performance of HMA individuals on a symmetry span test and might be related to group differences on working memory. Importantly, HMAs were less accurate than LMA participants at rejecting unordered D2 sequences. This result is interpreted in terms of worse cognitive flexibility in HMA individuals.

Comparison-based centrality measures

Recently, learning only from ordinal information of the type “item x is closer to item y than to item z” has received increasing attention in the machine learning community. Such triplet comparisons are particularly well suited for learning from crowdsourced human intelligence tasks, in which workers make statements about the relative distances in a triplet of items. In this paper, we systematically investigate comparison-based centrality measures on triplets and theoretically analyze their underlying Euclidean notion of centrality. Two such measures already appear in the literature under opposing approaches, and we propose a third measure, which is a natural compromise between these two. We further discuss their relation to statistical depth functions, which comprise desirable properties for centrality measures, and conclude with experiments on real and synthetic datasets for medoid estimation and outlier detection.

Assessment of the Anemic Conditions during Pregnancy: A Study of Sukkur City, Sindh, Pakistan

Background: Anemia is pathological disorder caused by mal nutrition and it is very common among feminine gender during gestational period. Objective: To investigate the prevalence of anemia among pregnant women, and identify the risk factors and symptoms of anemia in pregnancy. Methodology: Descriptive cross-sectional study was carried out for 12 months from June 2018 to June 2019 at Sukkur Blood and Drugs donating Society Hospital in Sukkur Sindh. A total of 300 pregnant women with anemic condition were selected by purposive sampling method. Structured questionnaire was designed in order to collect nominal and ordinal information after getting consent from included patients. The collected information was interpreted by using statistical software SPSS version 24.00 Results: The result revealed that 82.3% of the women were diagnosed as anemic, categorized as mild, moderate and severe. Anemic condition was common among pregnant women with ages 26-35 years, 63.9%. Women with primary or secondary education were more prone to anemia. The pregnant women belongs to rural areas were more forwarded to anemia, 86.6%. The numbers of patients were seen more in second and thirds trimester of gestation while rate of anemia in primary gravida was 75.0% that increased to 81.8% in multigravida, and further increased to 91.5% in grand multiparity. Conclusion: The prevalence of anemia was high in rural area of Sindh. Haemoglobin concentration was very much low in most of the pregnant female. The major cause of anemia in pregnant women was mal nutrition.

Decision-Making Approach of Two-Sided Matching of Supply and Demand of Logistics Service Based on the Uncertain Preference Ordinal

Aiming at the problem of matching logistics service supply and demand, this paper proposes a two-sided matching decision model of logistics service supply and demand based on the uncertain preference ordinal. In this model, the uncertain preference ordinal information is first expressed by the interval numbers of the logistics service supply and demand, and it is converted into the satisfaction degree of supply and demand matching uncertainty expressed by the interval number. Then, a multiobjective optimal matching model is constructed based on the largest overall satisfaction of the logistics service supply and demand side and the smallest satisfaction variance of the supply- and demand-side individual, and the multiobjective solution algorithm is designed based on nondominated sorting genetic algorithm-III (NSGA-III). Interval numbers are used to sort the matching results to obtain the approximately optimal two-sided matching scheme. Finally, this paper verifies the correctness of the model and validity of the algorithm.

Objective evaluation of fabric smoothness appearance with an ordinal classification framework based on label noise estimation

Objective fabric smoothness appearance evaluation plays an important role in the textile and apparel industry. In most previous studies, objective fabric smoothness appearance evaluation is defined as a general pattern classification problem. However, the labels in this problem exhibit a natural ordering. Nominal classification ignores the ordinal information, which may cause overfitting in model training. In addition, for the existence of subjective errors, measurement errors, manual errors, etc., the labels in the data might be noisy, which has been rarely discussed previously. This paper proposes an ordinal classification framework based on label noise estimation (OCF-LNE) to objectively evaluate the fabric smoothness appearance degree, which takes the ordinal information and noise of the label in the training data into consideration. The OCF-LNE uses the basic classifier in pre-training as a label noise estimator, and uses the estimated label noise to adjust the labels in further training. The adjusted labels can introduce the ordinal constrain implicitly and reduce the negative impact of label noise in model training. Within a 10 × 10 nested cross-validation, the proposed OCF-LNE achieves 82.86%, 94.29%, and 100% average accuracies under errors of 0, 0.5, and 1 degree, respectively. Experiments on different fabric image features and basic classification models verify the effectiveness of the OCF-LNE. In addition, the proposed method outperforms the state-of-the-art methods for fabric smoothness evaluation and ordinal classification. Promisingly, the OCF-LNE can provide novel ideas for image-based fabric smoothness evaluation.

A kernel machine for hidden object-ranking problems (HORPs)

Hidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clustered. We propose a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information. We show how this kernel machine can be implemented with standard optimization libraries provided slight modifications on the original kernel. The proposed approach is tested on Tahitian pearls quality assessment and compared with four other classical methods. It yields better results (93.6% ± 3.9% of correct predictions without feature selection, 94.3% ± 3.4% with feature selection) than the best of the other tested methods (91.3% ± 3.4% and 92.6% ± 4.3% without and with feature selection for the instance-ranking approach), this improvement being significant (p-value < 0.05). Moreover, this method exhibits no significant difference in the results with and without feature selection (p-value = 0.33), which may be a hint that its learning bias fits the problem well and can thus alleviate the data preprocessing workload.