Distance Metrics Research Articles

Recognition algorithm for laboratory protective equipment based on improved YOLOv7

In the university laboratory environment, it is not uncommon for individual laboratory personnel to be inadequately aware of laboratory safety standards and to fail to wear protective equipment (helmets, goggles, masks) in accordance with the prescribed norms. Manual inspection is costly and prone to leakage, and there is an urgent need to develop an efficient and intelligent detection technology. Video surveillance of laboratory protective equipment reveals that these items possess the characteristics of small targets. In light of this, a laboratory protective equipment recognition method based on the improved YOLOv7 algorithm is proposed. The Global Attention Mechanism (GAM) is introduced into the Efficient Layer Aggregation Network (ELAN) structure to construct an ELAN-G module that takes both global and local features into account. The Normalized Gaussian Wasserstein Distance (NWD) metric is introduced to replace the Complete Intersection over Union (CIoU), which improves the network's ability to detect small targets of protective equipment under experimental complex scenarios. In order to evaluate the robustness of the studied algorithm and to address the current lack of personal protective Equipment (PPE) datasets, a laboratory protective equipment dataset was constructed based on multidimensionality for the detection experiments of the algorithm. The experimental results demonstrated that the improved model achieved a mAP value of 84.2 %, representing a 2.3 % improvement compared to the original model, a 5 % improvement in the detection rate, and a 2 % improvement in the Micro-F1 score. In comparison to the prevailing algorithms, the accuracy of the studied algorithm has been markedly enhanced. The approach addresses the challenge of the challenging detection of small targets of protective equipment in complex scenarios in laboratories, and plays a pivotal role in perfecting laboratory safety management system.

Population diversity control based differential evolution algorithm using fuzzy system for noisy multi-objective optimization problems

The objective measurements of the real-world optimization problems are mostly subject to noise which occurs due to several reasons like human measurement or environmental factors. The performance of the optimization algorithm gets affected if the effect of noise is higher than the negligible limit. The previous noise handling optimization algorithms use a large population size or multiple sampling at same region which increases the total count of function evaluations, and few methods work for a particular problem type. To address the above challenges, a Differential Evolution based Noise handling Optimization algorithm (NDE) to solve and optimize noisy bi-objective optimization problems is proposed. NDE is a Differential Evolution (DE) based optimization algorithm where the strategies for trial vector generation and the control parameters of DE algorithm are self-adapted using fuzzy inference system to improve the population diversity along the evolution process. In NDE, explicit averaging based method for denoising is used when the noise level is higher than negligible limit. Extending noise handling method enhances the performance of the optimization algorithm in solving real world optimization problems. To improve the convergence characteristics of the proposed algorithm, a restricted local search procedure is proposed. The performance of NDE algorithm is experimented using DTLZ and WFG problems, which are benchmark bi-objective optimization problems. The obtained results are compared with other SOTA algorithm using modified Inverted Generational Distance and Hypervolume performance metrics, from which it is confirmed that the proposed NDE algorithm is better in solving noisy bi-objective problems when compared to the other methods. To further strengthen the claim, statistical tests are conducted using the Wilcoxon and Friedman rank tests, and the proposed NDE algorithm shows significance over the other algorithms rejecting the null hypothesis.

Quantification of First Metatarsal Joint Surface Interactions in Hallux Rigidus Using Distance and Coverage Mapping: A Case-Control Study.

Weightbearing cone-beam computed tomography (WBCT) has proven useful for analysis of structural changes of the foot and ankle when compared to conventional radiographs. WBCT allows for extraction of distance and coverage mapping metrics, which may provide novel insight into hallux rigidus (HR). This study retrospectively assessed HR joint space using distance and coverage mapping in a case-control study. WBCT images of the foot and ankle for 20 symptomatic HR and 20 control patients were obtained. Three-dimensional models were created and analyzed using a custom semiautomatic measurement algorithm. Distance and coverage mapping metrics for the first metatarsophalangeal and metatarsosesamoid joints were extracted from the models and compared between cohorts. Relationships between these metrics and visual analog scale (VAS) scores, a patient-reported outcome of pain, were assessed in HR patients. Overall first metatarsophalangeal joint space narrowing was noted in HR patients when compared to controls by an average of 11.8% (P = .02). However, no significant changes in the overall coverage of the joint were noted. Decreased joint space width and increased surface-to-surface coverage were only and particularly observed at the plantar medial quadrant of the first metatarsal head in HR patients relative to controls. VAS score was significantly but weakly correlated with dorsolateral quadrant coverage (R2 = 0.26, P = .03). Distance and coverage mapping serve as a complementary option to current techniques of quantifying HR changes. These metrics can expand the scope of future work investigating joint articulation changes in HR.

The Distance Between: An Algorithmic Approach to Comparing Stochastic Models to Time-Series Data

While mean-field models of cellular operations have identified dominant processes at the macroscopic scale, stochastic models may provide further insight into mechanisms at the molecular scale. In order to identify plausible stochastic models, quantitative comparisons between the models and the experimental data are required. The data for these systems have small sample sizes and time-evolving distributions. The aim of this study is to identify appropriate distance metrics for the quantitative comparison of stochastic model outputs and time-evolving stochastic measurements of a system. We identify distance metrics with features suitable for driving parameter inference, model comparison, and model validation, constrained by data from multiple experimental protocols. In this study, stochastic model outputs are compared to synthetic data across three scales: that of the data at the points the system is sampled during the time course of each type of experiment; a combined distance across the time course of each experiment; and a combined distance across all the experiments. Two broad categories of comparators at each point were considered, based on the empirical cumulative distribution function (ECDF) of the data and of the model outputs: discrete based measures such as the Kolmogorov–Smirnov distance, and integrated measures such as the Wasserstein-1 distance between the ECDFs. It was found that the discrete based measures were highly sensitive to parameter changes near the synthetic data parameters, but were largely insensitive otherwise, whereas the integrated distances had smoother transitions as the parameters approached the true values. The integrated measures were also found to be robust to noise added to the synthetic data, replicating experimental error. The characteristics of the identified distances provides the basis for the design of an algorithm suitable for fitting stochastic models to real world stochastic data.

Novel similarity measures under complex pythagorean fuzzy soft matrices and their application in decision making problems

Complex fuzzy soft matrices play a crucial role in various applications, including decision-making, pattern recognition, signals processing, and image processing. The main objective of this study is to introduce the unique notions of complex Pythagorean fuzzy soft matrices (CPFSMs), which provide more flexibility and accuracy in modelling uncertainty. CPFSMs incorporate Pythagorean fuzzy soft matrices, allowing for more sophisticated uncertainty modeling. The key findings of CPFSMs, specific instances, and certain fundamental set-theoretic operations and principles were covered. A set of new distance metrics between two CPFSMs has been defined. In the context of complex Pythagorean fuzzy soft sets and complex Pythagorean fuzzy soft matrices, we created a CPFS decision-making technique. Moreover, the application’s numerical example and comparison analysis have been effectively demonstrated. Thus, by integrating the concepts of Pythagorean fuzzy sets, soft matrices, and complex numbers, CPFSMs provide a robust framework with membership and non-membership degrees for complex decision-making modeling and analyzing uncertain data.

Areal and phylogenetic dimensions of word order variation in Indo-European languages

Abstract Both areal and phylogenetic affiliation have been discussed as driving factors of the distribution of word order in the languages of the world. However, disentangling the interaction of these two factors is challenging. Here we take Indo-European as a test case. Word order in this family is largely homogeneous both within areas and within branches, which makes it difficult to assess which factor was more important in shaping the present-day distribution. To break out of this impasse we turn to corpus data and explicit statistical modeling. Building on a parallel corpus of movie subtitles, we investigate word order on the sentence level under stable pragmatic conditions. We measure the similarity of word order variation between pairs of languages with an information-theoretic distance metric. Using cluster analysis and variation partitioning methods these distance metrics show that phylogenetic distance predicts more variation than geographical distance, but the most important predictor is the shared fraction where phylogeny and area overlap. We conclude that word order has evolved along both dimensions and cannot be reduced to a single one.

LDTR: Transformer-based lane detection with anchor-chain representation

Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving. Moreover, current lane representations require complex post-processing and struggle with specific instances. Inspired by the DETR architecture, we propose LDTR, a transformer-based model to address these issues. Lanes are modeled with a novel anchor-chain, regarding a lane as a whole from the beginning, which enables LDTR to handle special lanes inherently. To enhance lane instance perception, LDTR incorporates a novel multi-referenced deformable attention module to distribute attention around the object. Additionally, LDTR incorporates two line IoU algorithms to improve convergence efficiency and employs a Gaussian heatmap auxiliary branch to enhance model representation capability during training. To evaluate lane detection models, we rely on Fréchet distance, parameterized Fl-score, and additional synthetic metrics. Experimental results demonstrate that LDTR achieves state-of-the-art performance on well-known datasets.

Nonparametric statistical analysis of system resilience migration and application for electric distribution structures

This paper proposes a set of nonparametric statistical tools for analyzing the system resilience of civil structures and infrastructure and its migration upon changes in critical system parameters. The work is founded on the classic theoretic framework that system resilience is defined in multiple dimensions for a constructed system. Consequentially, system resilience can lose its parametric form as a random variable, falling into the realm of nonparametric statistics. With this nonparametric shift, traditional distribution-based statistics are ineffective in characterizing the migration of system resilience due to the variation of system parameters. Three statistical tools are proposed under the nonparametric statistical resilience analysis (npSRA) framework, including nonparametric copula-based sensitivity analysis, two-sample resilience test analysis, and a novel tool for resilience attenuation analysis. To demonstrate the use of this framework, we focus on electric distribution systems, commonly found in many urban, suburban, and rural areas and vulnerable to tropical storms. A novel procedure for considering resourcefulness parameters in the socioeconomic space is proposed. Numerical results reveal the complex statistical relations between the distributions of system resilience, physical aging, and socioeconomic parameters for the power distribution system. The proposed resilience distance computing and resilience attenuation analysis further suggests two proper nonparametric distance metrics, the Earth Moving Distance (EMD) metric and the Cramévon Mises (CVM) metric, for characterizing the migration of system resilience for electric distribution systems.

SFIDMT-ART: A metamorphic group generation method based on Adaptive Random Testing applied to source and follow-up input domains

Context:The performance of metamorphic testing relates strongly to the quality of test cases. However, most related research has only focused on source test cases, ignoring follow-up test cases to some extent. In this paper, we identify a potential problem that may be encountered with existing metamorphic group generation algorithms. We then propose a possible solution to address this problem. Based on this solution, we design a new algorithm for generating effective source and follow-up test cases. Objective:To improve the performance (test effectiveness and efficiency) of metamorphic testing. Methods:We introduce the concept of the input-domain difference problem, which is likely to affect the performance of metamorphic group generation algorithms. We propose a new test-case distribution criterion for metamorphic testing to address this problem. Based on our proposed criterion, we further present a new metamorphic group generation algorithm, from a black-box perspective, with new distance metrics to facilitate this algorithm. Results:Our algorithm performs significantly better than existing algorithms, in terms of test effectiveness, efficiency and test-case diversity. Conclusions:Through experiments, we find that the input-domain difference problem is likely to affect the performance of metamorphic group generation algorithms. The experimental results demonstrate that our algorithm can achieve good test efficiency, effectiveness, and test-case diversity.

RetrieveDroid: k-NN Performance Evaluation of Distance Measurement Schemes for Android Malware Detection Using Case-Based Retrieval

One of the big challenges in cybersecurity is the detection of Android attacks since Android is the most popular mobile operating system. Within this system, applications require certain permissions to access critical resources. Investigation of the use of permissions is a concern to check whether an application is not mislead to divulge sensitive information. This work aims to determine whose distance schemes offers the best malware detection based on permission similarities. Case based reasoning (CBR) is a concept which aims to find a solution based on historical experiences. CBR performance relies on finding similarities between actual cases and stored cases and then to deduce solutions. This paper proposes to transform app data as vector of appearance of dangerous permissions and to store such vectors based on CBR structure. Then we investigate k-NN classification performance related to five distance-based metrics such as Euclidean, Cosine, Manhattan, Minkowski, and Mahalanobis. Experiments were carried out with a set of 419 applications, including 203 malicious and 216 benign samples. The whole dataset has been split in training set of 291 samples with 162 benign and 129 malicious, and the testing set of 128 samples with 54 benign and 74 malicious samples. k-Nearest Neighbor (k-NN) are used as the similarity algorithm in which the distance model is varied in each of the five distance models. Results reveal that Minkowski and Manhattan models provide the best overall performance to detect Android malware based on permissions, in terms of accuracy (99.21%) and precision (97%). This work is a good start to recommend to researchers distance metrics exploitable when performing permission similarity-based detection.

Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology.

It is common for people working on linguistic geography, language contact and typology to make use of some type of distance metric between lects. However, most work so far has either used Euclidean distances, or geodesic distance, both of which do not represent the real separation between communities very accurately. This paper presents two datasets: one on walking distances and one on topographic distances between over 8700 lects across all macro-areas. We calculated walking distances using Open Street Maps data, and topographic distances using digital elevation data. We evaluate these distance metrics on three case studies and show that from the four distances, the topographic and geodesic distances showed the most consistent performance across datasets, and would be likely to be reasonable first choices. At the same time, in most cases, the Euclidean distances were not much worse than the other distances, and might be a good enough approximation in cases for which performance is critical, or the dataset cover very large areas, and the point-location information is not very precise.

Towards a better future for DNA barcoding: Evaluating monophyly- and distance-based species identification using COI gene fragments of Dacini fruit flies.

The utility of a universal DNA 'barcode' fragment (658 base pairs of the Cytochrome C Oxidase I [COI] gene) has been established as a useful tool for species identification, and widely criticized as one for understanding the evolutionary history of a group. Large amounts of COI sequence data have been produced that hold promise for rapid species identification, for example, for biosecurity. The fruit fly tribe Dacini holds about a thousand species, of which 80 are pests of economic concern. We generated a COI reference library for 265 species of Dacini containing 5601 sequences that span most of the COI gene using circular consensus sequencing. We compared distance metrics versus monophyly assessments for species identification and although we found a 'soft' barcode gap around 2% pairwise distance, the exceptions to this rule dictate that a monophyly assessment is the only reliable method for species identification. We found that all fragments regularly used for Dacini fruit fly identification >450 base pairs long provide similar resolution. 11.3% of the species in our dataset were non-monophyletic in a COI tree, which is mostly due to species complexes. We conclude with recommendations for the future generation and use of COI libraries. We revise the generic assignment of Dacus transversus stat. rev. Hardy 1982, and Dacus perpusillus stat. rev. Drew 1971 and we establish Dacus maculipterus White 1998 syn. nov. as a junior synonym of Dacus satanas Liang etal. 1993.

Enhanced decision technique for optimized crude oil pretreatment under disc spherical fuzzy Aczel Alsina aggregation information

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper’s primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment.

Measuring data similarity in population-based structural health monitoring using distance metrics

Population-based structural health monitoring (PBSHM) expands structural health monitoring (SHM) from a single structure to a group of structures, allowing inferences to be made within and between populations by transferring knowledge across them. Within the populations of interest, the similarity of structures, via their corresponding data, should be assessed to successfully implement PBSHM. This paper focusses on using distance metrics to assess similarity at the very start of the analysis chain, to discover information about a population for which there is little prior knowledge and before any analysis has taken place on individual structures. By doing so, it is possible to quickly and automatically identify abnormalities within the population, group similarly behaving structures together, and inform further decisions. The suitability of several candidate metrics that are not widely employed in SHM are tested using a number of commonly occurring feature behaviours, such as varying amplitudes and temporary mean shifts. The effect of data normalisation/standardisation on the metrics is also explored to identify interesting behaviours within the data. A case study is then presented where distance metrics are used to discover similarities and dissimilarities within temperature data from turbines in an offshore wind farm.

Automated modal parameter tracking with neural network based normalization of environmental perturbations

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.

감염병 발생에 대한 연령별 무질서도 및 다차원척도 분석

In this study, we crawled statistical data on 63 infectious diseases from the Korea Disease Control and Prevention Agency (KCDA) and examined the age-specific disease occurrence associations for the top 15 diseases with relatively high frequency of occurrence. Cumulative patient counts for infectious diseases were aggregated from 2001 to 2021, revealing chickenpox had the highest number of cases over the past 21 years, totaling approximately 660,000 cases. The Shannon index, reflecting disorderliness in age-specific occurrence, was highest for chickenpox, indicating concentrated disease occurrence in specific age groups. In contrast, typhoid fever showed the lowest Shannon index value, suggesting a disease with a high potential for occurrence across all age groups. Additionally, various distance metrics such as Euclidean distance, Chi-squared distance, weighted Chi-squared distance in contingency tables, and Hellinger distance were utilized to calculate the distance matrices of infectious diseases and applied multidimensional scaling. In particular, multidimensional scaling based on Hellinger distance for categorical data provided meaningful results in interpretation. According to the findings, diseases such as chickenpox, measles, mumps, and epidemic parotitis demonstrated a high occurrence frequency with a tendency for occurrence in younger age groups, forming a cluster of diseases. On the other hand, diseases like carbapenem infection, pneumococcal infection, trachoma, and hepatitis C formed a group characterized by lower occurrence frequency but occurring predominantly in older age groups.

Study of prerequisites for the formation of systematically important banks in regional economic associations

Despite the fact that more than ten years have passed since the rules for domestic systemically important banks (D-SIBs) were developed, the issues of identification of systemically important banks (SIB) and their regulation remain relevant today. Of particular interest is the possibility of forming uniform criteria for identifying systemically important banks within the framework of regional economic associations. The study is aimed at studying the prerequisites and fundamental possibilities of creating common criteria for classifying banks of the participating countries (Kazakhstan, Belarus, Russia) as SIB at the level of the Eurasian Economic Union (EAEU) through a clusterization approach. Clustering methodologies using Euclidean distance metrics and the Hamming model allowed for an initial study on the potential of the participating countries to develop common criteria for the SIB of the EAEU Union. The results of the analysis showed that the participating countries have the potential and some prerequisites for the development of common criteria within the union. However, it was found that the systemically important banks of the participating countries often change the level, since the criteria are not stable. Therefore, when forming uniform criteria, this point should be taken into account and criteria should be developed that are not approximated with each other, and similar features are significantly distinctive. The study consists of two parts, this work is the first part.

Median and small parsimony problems on RNA trees.

Noncoding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis. We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labeled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility. https://github.com/bmarchand/rna\_small\_parsimony.

Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis

The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover, the analysis of customer transitions between different groups has largely been overlooked. This study addresses these gaps by developing an efficient framework that enables businesses to forecast customer behavior, assess the impact of various strategies on each customer separately, and analyze customer transition between segments. This can facilitate providing personalized marketing strategies, fostering a gradual transition toward a desired customer status, and enhancing the overall marketing precision. In this study, we employ time series feature vectors encompassing recency, frequency, monetary value, and lifespan, applying the K-means algorithm with a range of distance metrics for customer segmentation along with classification algorithms to predict customer behavior. Leveraging counterfactual analysis, we establish a solution for analyzing customer transitions between groups and evaluating personalized marketing strategies. Our findings underscore the superior performance of the Euclidean distance metric, closely followed by the Manhattan distance, in distinguishing the patterns in time series customer behavior, with logistic regression excelling in predicting customer status. This study enables decision-makers to forecast the impact of diverse marketing strategies on customer behavior which facilitates customer retention and engagement through well-informed decisions.

"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.