Ambiguous Data Research Articles

A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction

Grading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints’ symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In our method, two cascaded small convolution neural networks are designed to locate more accurate knee joints. Detected knee joints are further cropped and split into left and right patches via their symmetry, which are fed into SE-ResNext50-32x4d-based Siamese network with shared weights, extracting more detailed knee features. The adaptive gated feature fusion method is used to capture richer semantic information for better feature representation here. Meanwhile, knee OA/non-knee OA classification task is added, helping extract richer features. We specially introduce a new evaluation metric (top±1 accuracy) aiming to measure model performance with ambiguous data labels. Our model is evaluated on two public datasets: OAI and MOST datasets, achieving the state-of-the-art results comparing to competing approaches. It has the potential to be a tool to assist clinical decision making.

Neutrosophic ratio-type estimators for estimating the population mean

All researches, under classical statistics, are based on determinate, crisp data to estimate the mean of the population when auxiliary information is available. Such estimates often are biased. The goal is to find the best estimates for the unknown value of the population mean with minimum mean square error (MSE). The neutrosophic statistics, generalization of classical statistics tackles vague, indeterminate, uncertain information. Thus, for the first time under neutrosophic statistics, to overcome the issues of estimation of the population mean of neutrosophic data, we have developed the neutrosophic ratio-type estimators for estimating the mean of the finite population utilizing auxiliary information. The neutrosophic observation is of the form {Z}_{N}={Z}_{L}+{Z}_{U}{I}_{N}, {rm where}, {I}_{N}in left[{I}_{L}, {I}_{U}right], {Z}_{N}in [{Z}_{l}, {Z}_{u}]. The proposed estimators are very helpful to compute results when dealing with ambiguous, vague, and neutrosophic-type data. The results of these estimators are not single-valued but provide an interval form in which our population parameter may have more chance to lie. It increases the efficiency of the estimators, since we have an estimated interval that contains the unknown value of the population mean provided a minimum MSE. The efficiency of the proposed neutrosophic ratio-type estimators is also discussed using neutrosophic data of temperature and also by using simulation. A comparison is also conducted to illustrate the usefulness of Neutrosophic Ratio-type estimators over the classical estimators.

Agrometeorological and Morpho-Physiological Studies of the Response of Red Currant to Abiotic Stresses

The aim of this work was to study the mechanism of climatic adaptation of red currant genotypes (Ribes rubrum L.) on the basis of physiological, biochemical and agrometeorological measurements and to determine the different phenophases of plant development identify adaptive genotypes for introduction. The studies were carried out in 2014–2017. The indicators of the water status of annual shoots (water content, water retention capacity), the biochemical composition of berries (vitamin C) and phenological observations were evaluated, taking into account meteorological data. The genotypes of R. petraeum Wulf. and R. multiflorum Kit. had the longest production period. Ambiguous data on the influence of temperature on the content of ascorbic acid in berries were revealed. High temperatures (>+26 °C) contributed to a greater accumulation of ascorbic acid in the cultivars of R. vulgare Lam. High accumulations of vitamin C in the range of +25–27 °C were found in R. petraeum Wulf. and R. multiflorum Kit.. High water content and water loss contributed to early recovery from the dormant state and reduced resistance to spring temperature changes in R. vulgare Lam. Genotypes of R. vulgare Lam., and R. multiflorum Kit. are promising for growing in a zone with a temperate continental climate. The genotypes of the species R. petraeum Wulf are suitable for introduction to the areas with a continental climate. The obtained results are important for adaptive gardening.

Emerging fungal infections: focus on Saksenaea Erythrospora.

SummaryIntroductionIn recent times, improved diagnostic techniques have revealed an alarming number of cases of mucormycosis in immunocompetent individuals. The Saksenaea species, is a rare cause of mucormycosis, and is often associated with skin and subcutaneous infection due to trauma in both immunocompromised and immunocompetent subjects. The purpose of this study was therefore, through a review of the literature, to investigate the problem of infections caused by Saksenaea Erythrospora, evaluating the clinical manifestations of the infection, the triggering factors, the therapies and patients’ outcomes, implementing and updating what already reported in literature.MethodsA research of peer-reviewed literature in the electronic databases MEDLINE (PubMed) and Scopus was conducted in the period June 2020-January 2021 using the key word “Saksenaea erythrospora”. Studies in Italian, English, French, Spanish focused on cases of Saksenaea erythrospora were included, without time restrictions. Studies that provided ambiguous or insufficient data were excluded.ResultsBibliographic research yielded 23 publications; 7 were included in the review. The studies were published between 2011 and 2015 and involved a total of 11 patients of average age 37.9 years (SD 17.23) hospitalized in several hospitals in: USA, India, Argentina, Colombia, Thailand. 6 patients were women, 5 men. All patients had an almost normal immune status. The causes of the infection were: injections, traumas, surgery. Two patients, despite surgical and medical therapy, died.ConclusionsOur review partially updated what already published, because only one new study was found. Serious necrotizing infections from Saksenaea erythrospora have been observed in recent years and a early identification and timely management are essential to reduce morbidity and mortality. A greater awareness and education about the risks deriving from carrying out surgical procedures abroad, especially in precarious hygiene situations, could be additional effective weapons to reduce the incidence of these infections.

HLA haplotype frequency estimation for heterogeneous populations using a graph-based imputation algorithm

HLA haplotype frequencies are estimated from ambiguous unphased HLA genotyping data using Expectation-Maximization (EM) algorithms. Current population genetics methods require independent EM frequency estimates for each population, and assume that each population is in Hardy-Weinberg Equilibrium (HWE). The HWE assumption of EM has thus far resulted in the exclusion of individuals from mixed or unknown ethnic backgrounds from reference datasets. Multi-region populations are currently poorly served by stem cell donor registry HLA imputation and matching implementations due to the inability of such algorithms to incorporate admixture into their population genetics models. To address this unmet need, we have expanded the imputation component of our GRaph IMputation and Matching (GRIMM) framework, where imputation becomes the expectation step in an iterative EM algorithm. Our novel multi-region EM implementation considers region as a Bayesian prior, enabling integration of HLA information from multiple single-region population groups, and for the first time including individuals with ambiguous or mixed ethnic backgrounds. We show that our multi-region EM produces much higher likelihood values and better haplotype recovery as measured by Kullback-Leibler divergence than all evaluated EM implementations when tested on real datasets of US donor registry HLA typings as well as simulated multi-region datasets of ambiguous HLA typings.

Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.

Domination in Join of Fuzzy Incidence Graphs Using Strong Pairs with Application in Trading System of Different Countries

Fuzzy graphs (FGs), broadly known as fuzzy incidence graphs (FIGs), are an applicable and well-organized tool to epitomize and resolve multiple real-world problems in which ambiguous data and information are essential. In this article, we extend the idea of domination of FGs to the FIG using strong pairs. An idea of strong pair dominating set and a strong pair domination number (SPDN) is explained with various examples. A theorem to compute SPDN for a complete fuzzy incidence graph (CFIG) is also provided. It is also proved that in any fuzzy incidence cycle (FIC) with l vertices the minimum number of elements in a strong pair dominating set are M[γs(Cl(σ,ϕ,η))]=⌈l3⌉. We define the joining of two FIGs and present a way to compute SPDN in the join of FIGs. A theorem to calculate SPDN in the joining of two strong fuzzy incidence graphs is also provided. An innovative idea of accurate domination of FIGs is also proposed. Some instrumental and useful results of accurate domination for FIC are also obtained. In the end, a real-life application of SPDN to find which country/countries has/have the best trade policies among different countries is examined. Our proposed method is symmetrical to the optimization.

Privacy preserving in blockchain-based government data sharing: A Service-On-Chain (SOC) approach

Sharing government data is of great significance to social development, but insecure and inappropriate sharing may lead to privacy breaches. Data sharing in consortium blockchain has provided a promising direction for efficient privacy preserving government data sharing. However, since government data is not owned by a single person or any government employers, it is hard to attribute participants’ responsibility for motivating data sharing behavior in blockchain systems. Furthermore, the scope and scale of government data to be shared among departments is unclear, as the purposes for retrieving shared data are dynamically changing. In order to solve these problems, we propose a Service-On-Chain (or simply, SOC) approach. The SOC approach can effectively identify different departments’ data retrieving requirements while efficiently sharing government data with trustworthiness in data content and controllability in data ownership. In particular, we utilize smart contracts to provide an onchain service to define data sharing agreements between government departments, which can identify ambiguous data retrieving requirements and formalize the process logic. We apply the SOC approach to a real world scenario and demonstrate that the SOC approach provides a feasible solution for secure and efficient sharing of data among government departments.

Sharia-compliant stock preference using similarity-based Fuzzy Grey Relational Analysis

The introduction of Sharia-compliant stocks at Bursa Malaysia can be considered as a type of socially responsible investing mechanism, especially for Muslim investors. An increase in the public interest in stock investment has led to the introduction of methods of categorizing and selecting preferred stocks effectively. Fuzzy decision-making methods have also become an important approach due to the nature of ambiguous and imprecise available data in the decision-making process. Nevertheless, some simplifications are inevitable to reduce the complexity of the problem, and consequently, loss of vital information may occur. In this paper, a similarity-based fuzzy grey relational analysis (FGRA) is proposed to rank selected Sharia-compliant stocks. An element of similarity measure is introduced in the original FGRA to reduce the loss of information in the computation when the distance between fuzzy numbers is utilized. The similarity measure can capture more information compared to distance measure since, in the computation, additional geometrical features of fuzzy numbers will be incorporated. An improvised procedure of FGRA is offered and implemented to rank selected Sharia-compliant stocks based on some fundamental analysis criteria. A comparison is then made on the preference obtained by the proposed procedure with the original FGRA.

Geometrical properties of the interaction between oblique incoming coronal waves and coronal holes

Observations of coronal waves (CWs) interacting with coronal holes (CHs) show the formation of typical wave-like features, such as reflected, refracted and transmitted waves (collectively, secondary waves). In accordance with these observations, numerical evidence for the wave characteristics of CWs is given by simulations, which demonstrate effects of deflection and reflection when a CW interacts with regions exhibiting a sudden density drop, such as CHs. However, secondary waves are usually weak in their signal and simulations are limited in the way the according idealisations have to be chosen. Hence, several properties of the secondary waves during a CW–CH interaction are unclear or ambiguous and might lead to misinterpretations. In this study we follow a theoretical approach and focus in particular on the geometrical properties of secondary waves caused by the interaction between oblique incoming CWs and CHs. Based on a linear theory, we derive analytical expressions for reflection and transmission coefficients, which tell us how strongly the amplitudes of the secondary waves increase and decrease with respect to the incoming wave, respectively. Additionally, we provide analytical terms for crucial incidence angles that are capable of giving information about the energy flux, the phase and the reflection properties of the secondary waves. These novel expressions provide a supplementary tool for estimating CW properties in a fast and straightforward way, and therefore might have relevant consequences for a possible new interpretation of previously studied CW–CH interaction events and may help in the clarification of ambiguous observational data.

Multiobjective semisupervised learning with a right-censored endpoint adapted to the multiple imputation framework.

Semisupervised learning aims to use additional knowledge in the search for data structure. In clinical applications, including predictive information in the construction of a data-driven classification is of major importance. This work was motivated by a study that aimed to identify different patterns of immune parameters that would be associated with relapse-free survival in a cohort of breast cancer patients. Supervised and unsupervised objectives can be concomitantly optimized using multiobjective optimization. We propose such a procedure that addresses two challenges in the semisupervised approach, that is, missing data and additional knowledge based on survival time. The former was handled by using multiple imputation and consensus clustering. Survival information was incorporated in the supervised objective through the estimation of a cross-validation error of a Cox regression. A simulation study was performed to assess the performance of the proposed procedure. On complete datasets, the performances were compared to those of an existing modified multiobjective semisupervised learning method. The added value of including the survival data in the learning process was assessed by comparing the procedure to unsupervised learning. The proposed procedure showed better performance than the existing method, notably in the selection of the number of clusters. On incomplete datasets, the procedure showed little sensitivity to most of its parameters, even though a high number of imputations and partition initialization seeds improved the performance. The performance was degraded with a high proportion of missing data (40%) and with more ambiguous data structures. Simulation results and application on real data support the conclusion that our procedure enables the construction of a classification associated with a right-censored endpoint on a possibly incompletedataset.

Estimating pose from pressure data for smart beds with deep image-based pose estimators

In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy.

25-LB: Are You a Good Steward of Data?

The Problem: There is an urgent need for innovative solutions for new and improved treatments and therapies. Often clinical research data is “silo-ed” within an organization, preventing the broader research community from leveraging aggregated information from individuals and studies globally. This isolation of data limits the discoveries possible within a disease preventing the discovery of new scientific links between disease areas. Inconsistent and ambiguous data representation leads to a lack of transparency, traceability and reproducibility, in turn fostering inefficiencies and inaccuracies in research that loses time and increases costs. Simply put Are we being good stewards of our study data? Data Standards and Data Sharing as part of the solution: Describing data consistently using data standards can bring clarity to data that amplifies its value. The T1D Data Standards Project builds on existing CDISC diabetes standards by describing concepts relevant to T1D in the areas of: • Pediatrics and Devices (Released Sep 2020) • Exercise and Nutrition (Due Q2-2021) • Screening, Staging and Monitoring for Pre-Clinical Type 1 Diabetes (Due Q3-2021). The team of CDISC standards experts, T1D experts from CDISC member organizations and T1D subject matter experts from academia and industry followed a consensus-driven process to ensure the final standards are fit for use within the research community, reinforced by the open community review. How data standards help overcome some of the challenges: Publicizing the availability and promoting adoption of consistent standards brings clarity to data, reduces data collection, organization and analysis time, and facilitates interoperability that promotes data re-use and operational efficiencies. When researchers speak the same harmonized language, talking and understanding each other is much easier leading to the sharing of common, understandable data within the research community, thereby promoting scientific and therapeutic advances general confidence in the data. Disclosure R. Baker: None. J. M. Owen: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (G-2018PG-T1D069)

Resolving the ambiguous direction of arrival of weak meteor radar trail echoes

Abstract. Meteor phenomena cause ionized plasmas that can be roughly divided into two distinctly different regimes: a dense and transient plasma region co-moving with the ablating meteoroid and a trail of diffusing plasma left in the atmosphere and moving with the neutral wind. Interferometric radar systems are used to observe the meteor trails and determine their positions and drift velocities. Depending on the spatial configuration of the receiving antennas and their individual gain patterns, the voltage response can be the same for several different plane wave directions of arrival (DOAs), thereby making it impossible to determine the correct direction. A low signal-to-noise ratio (SNR) can create the same effect probabilistically even if the system contains no theoretical ambiguities. Such is the case for the standard meteor trail echo data products of the Sodankylä Geophysical Observatory SKiYMET all-sky interferometric meteor radar. Meteor trails drift slowly enough in the atmosphere and allow for temporal integration, while meteor head echo targets move too fast. Temporal integration is a common method to increase the SNR of radar signals. For meteor head echoes, we instead propose to use direct Monte Carlo (DMC) simulations to validate DOA measurements. We have implemented two separate temporal integration methods and applied them to 2222 events measured by the Sodankylä meteor radar to simultaneously test the usefulness of such DMC simulations on cases where temporal integration is possible, validate the temporal integration methods, and resolve the ambiguous SKiYMET data products. The two methods are the temporal integration of the signal spatial correlations and matched-filter integration of the individual radar channel signals. The results are compared to Bayesian inference using the DMC simulations and the standard SkiYMET data products. In the examined data set, ∼ 13 % of the events were indicated as ambiguous. Out of these, ∼ 13 % contained anomalous signals. In ∼ 95 % of all ambiguous cases with a nominal signal, the three methods found one and the same output DOA, which was also listed as one of the ambiguous possibilities in the SkiYMET analysis. In all unambiguous cases, the results from all methods concurred.

A systematic review of agouti (Dasyproctidae: Dasyprocta) records from the pre‐1492 Lesser Antilles: New perspectives on an introduced commensal

AbstractThe agouti (Dasyprocta sp.), a medium‐sized neotropical rodent, was introduced by indigenous peoples to the Caribbean islands over a millennium ago and is recorded at archeological sites throughout the Lesser Antilles and southern Caribbean. Like many commensal fauna, these mammals are valuable proxies for deciphering ancient human practices and mobility. However, unsystematic reporting and ambiguous data hinder determination of the agouti's timing of entry, paths of dissemination, distribution, ecological impact, and cultural significance. Here, we present the first systematic review of all published records of which we are aware for pre‐1492 agouti skeletal remains in the Caribbean, totaling 63 sites on 24 islands. On the basis of these data, we address the issue of archeological species attributions in relation to unresolved phylogenetic and taxonomic issues for Dasyprocta spp. In addition, we synthesize findings for agouti distribution, abundance, and chronology of occurrence. Data are consistent with an initial introduction to the northern Lesser Antilles by ca. AD 35 and perhaps as early as ca. 500 BC. While the midden contexts in which agoutis are almost always found affirm their use as food, ethnohistoric records point to the utilization of skeletal remains in ritual and artifact manufacture. This study provides the first region‐wide synthesis of archeological agoutis and highlights the important insights to be gained from such systematic reviews. It is particularly significant for informing the potential ecological impacts of indigenous faunal introductions prior to Columbus's arrival.

Mechanistic elucidation of the oral pungency of capsaicin-related dietary components: Spatial structural insights

The mechanistic insights into the oral pungency of capsaicin-related dietary components have been elucidated from the spatial structural perspectives by establishing statistically significant and highly predictive three-dimensional quantitative structure–property relationship models. Our results visualized the possible favorable and unfavorable steric and electrostatic interactions with the pungent receptors with the assistance of pharmacophore models, and revealed the suitable electronegative/positive or bulky substitutions in the vanillyl group, amide moiety, linear alkyl chain and their adjacent structural area of capsaicin required for the desired pungency, which was not only complementary to the viewpoints proposed in our previous structure-pungency correlations, but also was applied to clearly clarify the pungent differences in compounds, and well predict the pungency of 21 capsaicin analogs though with ambiguous experimental data on pungency. Hopefully, this work would benefit the overall understanding of the pungent mechanism and facile discovery/design of analogs with desired pungency to expand their applications in foods.

95347 Examining the Impact of the BPCA: Promoting Pediatric Inclusion in Clinical Trials and Pediatric-Specific Drug Information

ABSTRACT IMPACT: It provides insight in the relationship between pediatrics and clinical research and how pediatric participation in CT translates to clinical significance in form of drug labels, which inform clinicians on how to prescribe pediatric medications. OBJECTIVES/GOALS: Assessing the extent that the Best Pharmaceuticals for Children Act (BPCA) advances pediatric inclusion in clinical trials (CTs) and the availability of pediatric-specific drug information METHODS/STUDY POPULATION: The BPCA provides the U.S. Food and Drug Administration (FDA) authority to solicit sponsors whose drugs may benefit pediatric populations. Participation is voluntary and provides additional market exclusivity and pediatric information. CTs that received marketing exclusivity from 2016-2018 under BPCA were reviewed using Clinicaltrials.gov to access the legislation’s impact. CTs were categorized according to eligibility: (1) pediatric and adult groups, (2) pediatrics, and (3) pediatric sub-groups. Studies were excluded for ambiguous age data. Studies open to both groups were evaluated for pediatric participation. Each drug was searched in DailyMed.com for published pediatric indications. RESULTS/ANTICIPATED RESULTS: Between 2016 - 2018, 22 drugs received marketing exclusivity under BPCA. Of the 196 CTs conducted for these drugs, 135 were available to adults and pediatrics, 10 were available to the entire pediatric population, and 51 were available to specific pediatric sub-populations. Exclusion criteria permitted only 118 of the CTs for assessment where eligibility included both pediatric and adult populations, of which 65 of these had less than 1% pediatric representation. Of the 22 drugs, 20 have pediatric indications. Over this three-year period, the number of CTs where adults and pediatrics were eligible were greater than CTs for pediatric only or pediatric subpopulations. DISCUSSION/SIGNIFICANCE OF FINDINGS: It is prevalent for BPCA compliant CTs to include both; 65% of drugs (13/20) with pediatric indications had more studies involving both groups than only pediatrics. Adequate pediatric CT representation is necessary for developing pediatric drug labeling with meaningful data for clinical indications.

A Critical Review of Morphologic Findings and Data From 14 Toxicological Studies Involving Fish Exposures to Diclofenac.

A number of studies have investigated the potential toxicity of the analgesic agent diclofenac (DCF) in various fish species under a diverse array of experimental conditions. Reported evidence of toxicity in these investigations is often strongly reliant on morphologic end points such as histopathology, immunohistochemistry, and transmission electron microscopy. However, it may be challenging for scientists who perform environmental hazard or risk determination to fully appreciate the intricacies of these specialized endpoints. Therefore, the purpose of the current review was to critically assess the quality of morphologic data in 14 papers that described the experimental exposure of fish to DCF. Areas of focus during this review included study design, diagnostic accuracy, magnitude of reported changes, data interpretation and presentation, and the credibility of individual reported findings. Positive attributes of some studies included robust experimental designs, accurate diagnoses, and straightforward and transparent data reporting. Issues identified in certain articles included diagnostic errors, failure to account for sampling and/or observer bias, failure to evaluate findings according to sex, exaggeration of lesion severity, interstudy inconsistencies, unexplained phenomena, and incomplete or ambiguous data presentation. It is hoped that the outcome of this review will be of value for personnel involved in regulatory decision-making.

Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

Models and Methods for Discovery of Local Area Network Topology with Incomplete Data

A lot of network management tasks require a description of the logical and physical computer network topology. Obtaining such a description in an automatic way is complicated due to the possibility of incompleteness and incorrectness of the initial data on the network structure. This article provides a study on the properties of incomplete initial data on network device connectivity on the link layer. Methods for generalized handling of the heterogeneous input data on the link layer are included. We describe models and methods for deriving a missing part of the data, as well as the condition in which it is possible to get a single correct network topology description. The article includes algorithms for building a link layer topology description from incomplete data when this data is possible to fulfill up to the required level. Also, we provide methods for detecting and resolving an ambiguity in the data and methods for improving incorrect initial data. The tests and evaluations provided in the article demonstrate the applicability and effectiveness of the build methods for discovering various heterogeneous real-life networks. Additionally, we show the advantages of the provided methods over the previous analogs: our methods are able to derive up to 99% data on link layer connectivity in polynomial time; able to provide a correct solution from an ambiguous data.