Ranking Of Objects Research Articles

HyperSOR: Context-Aware Graph Hypernetwork for Salient Object Ranking.

Salient object ranking (SOR) aims to segment salient objects in an image and simultaneously predict their saliency rankings, according to the shifted human attention over different objects. The existing SOR approaches mainly focus on object-based attention, e.g., the semantic and appearance of object. However, we find that the scene context plays a vital role in SOR, in which the saliency ranking of the same object varies a lot at different scenes. In this paper, we thus make the first attempt towards explicitly learning scene context for SOR. Specifically, we establish a large-scale SOR dataset of 24,373 images with rich context annotations, i.e., scene graphs, segmentation, and saliency rankings. Inspired by the data analysis on our dataset, we propose a novel graph hypernetwork, named HyperSOR, for context-aware SOR. In HyperSOR, an initial graph module is developed to segment objects and construct an initial graph by considering both geometry and semantic information. Then, a scene graph generation module with multi-path graph attention mechanism is designed to learn semantic relationships among objects based on the initial graph. Finally, a saliency ranking prediction module dynamically adopts the learned scene context through a novel graph hypernetwork, for inferring the saliency rankings. Experimental results show that our HyperSOR can significantly improve the performance of SOR.

Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods

The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development.

Evaluation of CNC lathe machine with fuzzy linguistic mcdm methods

CNC (Computerized Numerical Control) lathes have become integral to modern manufacturing and machining industries due to their ability to produce intricate parts with precision and efficiency. Not only do CNC lathes enhance productivity and accuracy, but they also minimize human error and enhance overall safety in the manufacturing process. Furthermore, the current market offers a wide array of diverse types of CNC lathes. Consequently, the evaluation and selection of CNC lathes pose a complex decision-making challenge as there are numerous types available, each with a variety of selection criteria for manufacturers to consider. It is crucial to make an informed choice, as improper evaluation and selection can have adverse effects on the overall performance of the production system. In this study, we propose using the fuzzy EDAS (Evaluation Based on Distance from Average Solution) model to evaluate and select CNC lathes. Initially, we employ the fuzzy analysis method, based on expert opinions, to establish a set of weights for the evaluation criteria. These criteria consist of seven factors: capital cost, spindle speed, distance between centers, rapid traverse rates in the X-axis and Z-axis, maximum machining diameter, and maximum machining length. Subsequently, the fuzzy EDAS object ranking model is utilized to evaluate and rank the CNC lathes, ultimately aiding in the selection of the most suitable machine for the manufacturer. The results obtained from our analysis reveal that the MICROTURN-300DX machine is the optimal choice, closely followed by the MICROTURN-300X machine. The study's findings serve as valuable guidelines for decision makers in selecting CNC lathes that align with the requirements of factory production. Moreover, the suggested approach can also be utilized to choose various other machine types as production demands become more intricate

Category-based depth incorporation for salient object ranking

Salient object ranking is a visual task that aims to mimic the human visual system’s ability to prioritize various salient objects in complex scenes. It integrates various computer vision tasks, including object detection and instance segmentation, and draws insights from psychology and biology. Despite recent advancements in salient object ranking research, there is still a need for a more in-depth exploration of certain underlying factors that influence the ranking. Previous studies have primarily focused on factors such as the scale and position of objects, as well as interactions between objects and their context. However, they often overlooked the crucial interaction between objects and observers. Unlike other visual tasks, salient object ranking is highly subjective and influenced by the observer. Therefore, establishing a meaningful connection between the observed object and the observer becomes crucial. Our model addresses this gap by placing emphasis on the distance between objects and observers. It investigates the implicit relationship between object categories and distance through the Category-Aware Attention (CAA) module. This innovative approach incorporates depth into salient object ranking, resulting in an improvement in ranking performance.

SeqRank: Sequential Ranking of Salient Objects

Salient Object Ranking (SOR) is the process of predicting the order of an observer's attention to objects when viewing a complex scene. Existing SOR methods primarily focus on ranking various scene objects simultaneously by exploring their spatial and semantic properties. However, their solutions of simultaneously ranking all salient objects do not align with human viewing behavior, and may result in incorrect attention shift predictions. We observe that humans view a scene through a sequential and continuous process involving a cycle of foveating to objects of interest with our foveal vision while using peripheral vision to prepare for the next fixation location. For instance, when we see a flying kite, our foveal vision captures the kite itself, while our peripheral vision can help us locate the person controlling it such that we can smoothly divert our attention to it next. By repeatedly carrying out this cycle, we can gain a thorough understanding of the entire scene. Based on this observation, we propose to model the dynamic interplay between foveal and peripheral vision to predict human attention shifts sequentially. To this end, we propose a novel SOR model, SeqRank, which reproduces foveal vision to extract high-acuity visual features for accurate salient instance segmentation while also modeling peripheral vision to select the object that is likely to grab the viewer’s attention next. By incorporating both types of vision, our model can mimic human viewing behavior better and provide a more faithful ranking among various scene objects. Most notably, our model improves the SA-SOR/MAE scores by +6.1%/-13.0% on IRSR, compared with the state-of-the-art. Extensive experiments show the superior performance of our model on the SOR benchmarks. Code is available at https://github.com/guanhuankang/SeqRank.

Methods for Thematic Analysis and Ranking in Information Analysis Systems Based on Relational DBMS

The paper presents the models and algorithms for searching, filtering and ranking data in an information analysis system, based on the ontological description of its structure. The rules for filtering and ranking objects based on their boolean, numeric or enumerable properties are specified by the users, and based on these rules SQL queries are evaluated. Another problem considered is taking subject area into account when searching for objects. Adding this allows one to solve many tasks that occur in current research information systems, such as searching for experts in given subject areas or generating reports. A search query can be specified in the form of a set of keywords, and a statistical approach is used for comparing keyword sets.

Multi-perspective analysis of sustainability metrics characterising the debris environment

To assess the effectiveness of mitigation measures, long-term simulations of the debris environment are often analysed and evaluated using simple metrics such as the total number of objects or the frequency of fragmentation events. In addition, more complex criticality metrics have been developed in the past to rank objects according to their environmental impact and evaluate the overall state of the debris environment. In this context, three different perspectives are particularly worth highlighting: object-specific metrics that characterise the criticality of individual objects and orbits, spatial metrics that can be used to identify particularly vulnerable orbital regions, and global metrics which describe the sustainability of the environment as a whole. Typically, the criticality metrics are specific to one of the respective perspectives, but unification across and convertibility between perspectives would allow for a more comprehensive assessment of sustainability. In this context, a methodology is developed to transform object-specific and spatial metrics into one another. The conversion concept enables a comparison of two structurally different metrics from their respective initial object-specific and spatial domain. Furthermore, the third perspective is taken into account by using different aggregation methods to retrieve a global metric from the initial object-specific and spatial description. The three perspectives then are compared by conducting environment evolution simulations for various baseline scenarios using a common space debris population in low Earth orbit. The comparative analysis identifies commonalities and differences of the chosen metrics in the three different perspectives and how the conversion between them affects their quantification of sustainability. The multi-perspective convertibility and aggregation of single-perspective metrics represents a contribution to the generalisation of sustainability metrics for different scopes of application.

Effective signal reconstruction from multiple ranked lists via convex optimization

The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it is of interest to estimate the usually unobservable latent signals that inform a consensus ranking. Under the only assumption of independent assessments, which can be incomplete, we introduce indirect inference via convex optimization in combination with computationally efficient Poisson Bootstrap. Two different objective functions are suggested, one linear and the other quadratic. The mathematical formulation of the signal estimation problem is based on pairwise comparisons of all objects with respect to their rank positions. Sets of constraints represent the order relations. The transitivity property of rank scales allows us to reduce substantially the number of constraints associated with the full set of object comparisons. The key idea is to globally reduce the errors induced by the rankers until optimal latent signals can be obtained. Its main advantage is low computational costs, even when handling n<<p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n < < p$$\\end{document} data problems. Exploratory tools can be developed based on the bootstrap signal estimates and standard errors. Simulation evidence, a comparison with the state-of-the-art rank centrality method, and two applications, one in higher education evaluation and the other in molecular cancer research, are presented.

A novel evaluation method for renewable energy development based on improved sparrow search algorithm and projection pursuit model

With global climate change posing a major threat to human society, a growing number of countries have taken “carbon-neutral” as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans.

Decomposition heuristics for multiobjective problems. The Food bank network redesign case

Large MO-MILP problems are often very hard to solve by exact methods. In this study we consider the Food bank network redesign (FBNR) problem to introduce three decompose-and-fix heuristics that successfully solve large real-life based instances of the problem. The FBNR problem is modeled as a multi-period, multi-product supply chain redesign problem that accounts for economic, environmental and social objectives in three distinct functions. Each new heuristic decomposes the FBNR problem into two MO-MILP problems, which are sequentially solved following the lexicographic concept. Decomposition observe the nature and terms of the decisions involved. The first MO-MILP problem concerns only the longer term decisions. After the corresponding decisions are fixed, the second MO-MILP problem referring to shorter term decisions is solved respecting the ranking of objectives followed for each solution obtained in the first problem. The difference among the heuristics lies in what is considered as longer or shorter term decisions. Besides solving large instances of the problem, which the exact method used could not do, CPU time savings generated by the heuristics range from 80% to 97% of the time required by the exact method. Compromise between the computational effort and the quality of solutions obtained by each heuristic is discussed. The solving methodology proposed in this study can be adapted to other large multiobjective problems.

A hybrid AHP-TOPSIS, MOORA technique for multi-objective optimization of thermal, mechanical, and water absorption behavior of epoxy/hemp, pine apple, and palm fiber composites

ABSTRACT Natural fiber composites are capable of replacing synthetic fiber composites in aeronautics, transportation, architecture, and sports. This work used epoxy matrices to produce natural hybrid fiber composites from hemp, pineapple, and palm fiber. To choose the best material for a design or component, a thorough and successful approach is needed. Material selection judgments are best handled by multi-criteria decision-making (MCDM) procedures. Integrated MCDM approaches like AHP, TOPSIS, and MOORA rank epoxy-hemp-pineapple-palm fiber composites. TOPSIS and MOORA use AHP weights to rank objects. AHP weights are used to rank objects in both the TOPSIS and MOORA methods. Selection is based on a variety of properties of the generated composites, including water absorption and specific heat capacity, in addition to density, hardness, tensile strength, and toughness. The thermal degradation of fiber polymerization is assessed by thermo-gravimetric analysis (TGA). According to a thorough evaluation of MCDM methodologies, a hybrid composite made of palm, hemp, and pineapple performed best, followed by a hybrid composite made of palm and pineapple. This study found that a palm fiber composite has subpar results. Scanning electron microscopy (SEM) is used to examine fiber form and interfacial bonding in composite samples that have been exposed to tension fracture.

Inferring Attention Shifts for Salient Instance Ranking

AbstractThe human visual system has limited capacity in simultaneously processing multiple visual inputs. Consequently, humans rely on shifting their attention from one location to another. When viewing an image of complex scenes, psychology studies and behavioural observations show that humans prioritise and sequentially shift attention among multiple visual stimuli. In this paper, we propose to predict the saliency rank of multiple objects by inferring human attention shift. We first construct a new large-scale salient object ranking dataset, with the saliency rank of objects defined by the order that an observer attends to these objects via attention shift. We then propose a new deep learning-based model to leverage both bottom-up and top-down attention mechanisms for saliency rank prediction. Our model includes three novel modules: Spatial Mask Module (SMM), Selective Attention Module (SAM) and Salient Instance Edge Module (SIEM). SMM integrates bottom-up and semantic object properties to enhance contextual object features, from which SAM learns the dependencies between object features and image features for saliency reasoning. SIEM is designed to improve segmentation of salient objects, which helps further improve their rank predictions. Experimental results show that our proposed network achieves state-of-the-art performances on the salient object ranking task across multiple datasets. Code and data are available at https://github.com/SirisAvishek/Attention_Shift_Ranks.

Multi-operator Image Retargeting based on Saliency Object Ranking and Similarity Evaluation Metric

Image Retargeting (IR) technology is proposed to flexibly display images on various display devices while protecting the important content of the images undistorted. IR methods mainly use Salient Object Detection (SOD) to obtain important content, however, most existing SOD methods treat multiple salient objects with the same saliency degrees, which makes IR methods assign the same retargeting ratios for different objects and leads to producing information-loss retargeted results. Multi-operator IR demonstrates better generalization than single operator by using multiple operators to find the optimal sequence of operators. Meanwhile, the tremendous processing time limits its practical use. To address these problems, we propose a multi-operator IR method based on Salient Object Ranking (SOR) and Similarity Evaluation Metric (SORSEM-IR), which includes two stages: importance map generation and multi-operator IR. In the first stage, a SOR module with Context-aware Semantic Refinement (SORCSR) is proposed, which extracts the salient instances and infers their saliency ranks with a context-aware semantic refinement module, then the SOR map, face map, and gradient map are fused as the importance map. In the second stage, to speed up multiple operations, a similarity evaluation metric is proposed to measure the similarity between the original image and the seam-removal image by Seam Carving (SC) operation, and switch SC to uniform scaling to meet the aspect ratio when distortion caused by SC arrives at a certain extent. Experimental results show that the SORCSR network achieves state-of-the-art performance on the ASSR dataset subjectively and objectively, and the SORSEM-IR guided by SORCSR can not only protect the salient objects with minimum deformation but also meet human aesthetic perception.

A trust-region approach for computing Pareto fronts in multiobjective optimization

Multiobjective optimization is a challenging scientific area, where the conflicting nature of the different objectives to be optimized changes the concept of problem solution, which is no longer a single point but a set of points, namely the Pareto front. In a posteriori preferences approach, when the decision maker is unable to rank objectives before the optimization, it is important to develop algorithms that generate approximations to the complete Pareto front of a multiobjective optimization problem, making clear the trade-offs between the different objectives. In this work, an algorithm based on a trust-region approach is proposed to approximate the set of Pareto critical points of a multiobjective optimization problem. Derivatives are assumed to be known, allowing the computation of Taylor models for the different objective function components, which will be minimized in two main steps: the extreme point step and the scalarization step. The goal of the extreme point step is to expand the approximation to the Pareto front, by moving towards the extreme points of it, corresponding to the individual minimization of each objective function component. The scalarization step attempts to reduce the gaps on the Pareto front, by solving adequate scalarization problems. The convergence of the method is analyzed and numerical experiments are reported, indicating the relevance of each feature included in the algorithmic structure and its competitiveness, by comparison against a state-of-art multiobjective optimization algorithm.

A Three-Way Decision Methodology With Regret Theory via Triangular Fuzzy Numbers in Incomplete Multiscale Decision Information Systems

The three-way decision theory provides a three-way philosophical thinking to solve problems, and the regret theory quantifies the risk preferences of decision makers under different psychological behaviors. On the one hand, the combination of these two theories makes models more practical by considering the psychological behaviors of decision makers. On the other hand, we can effectively combine the advantages of the three-way decision theory with the regret theory to highlight the interpretability of decision making processes. In this paper, we propose a novel approximate estimation method for incomplete utility values via the regret theory and establish a wide sense of three-way decision model on incomplete multi-scale decision information systems. First, the degree of consistency for each scale is measured via using the dependency degree, then the optimal subsystem is selected by evaluating the scale selection cost. Further, the incomplete multi-scale evaluation information is transformed into triangular fuzzy numbers via linguistic term sets. Second, in light of fuzzy evaluation values and trade-off factors, an estimation method for incomplete fuzzy subsystems is constructed, which can be used to calculate the utility difference and regret-rejoicing values for pairwise comparisons. Finally, from the perspective of human cognition, the tri-partition and the corresponding decision rules are built by the tolerance degree, and the ranking of objects is calculated by the relative closeness degree. Additionally, multi-aspect comparative and experimental analyses are performed by extensive experiments, and the feasibility, validity and stability of the constructed model are shown by parametric analyses.

A Probabilistic Linguistic Three-Way Decision Method With Regret Theory via Fuzzy c-Means Clustering Algorithm

Aiming at multi-attribute decision-making (MADM) problems with probabilistic linguistic term sets (PLTSs), and considering the effective rationality of a decision-maker (DM) in complex decision environments, this paper proposes a probabilistic linguistic three-way decision (TWD) method based on the regret theory (RT), namely PL-TWDR. First, a probabilistic linguistic attribute weight determination method is developed that considers probabilistic linguistic information entropies and the weighted total deviation of all objects from the negative ideal solution (NIS). Then, a new group satisfaction index is designed to replace the utility function in RT, which overcomes the limitation of the RT calculation in PLTSs. Second, the fuzzy c-means (FCM) algorithm is extended to PLTSs for obtaining equivalent objects under different clusters and calculate conditional probabilities in corresponding TWD models, which makes up for the shortage of the PLTS evaluation matrix when dividing equivalence classes. Third, RT is introduced into PLTSs to rank objects according to utility perception values. At the same time, a new TWD model constructed by average utility perception values is used to realize object domains in probabilistic linguistic environments. Finally, the proposed method is applied to realistic cases, and the effectiveness and superiority of the PL-TWDR method are verified via comparative analysis and sensitivity analysis in terms of other nine popular decision-making methods.

Regret-Theoretic Multiattribute Decision-Making Model Using Three-Way Framework in Multiscale Information Systems.

The existing multiattribute decision-making (MADM) methods on multiscale information systems (MSISs) are generally studied from the utility point of view, which may cause two problems: 1) the objects are strictly classified into good or bad, which may lead to misclassification and 2) the risk attitude and psychological behaviors of decision makers are difficult to be reflected. In light of this, this article proposes a wide three-way decision (3WD) model on an MSIS, which combines 3WD theory and regret theory and can precisely make up for these two shortcomings. First, by virtue of regret theory, an outranking relation on the comprehensive MSIS is constructed according to the regret-rejoicing index. Second, objects in the outranking relation are classified into three different domains by a clustering method. In each domain, the ranking of objects can be calculated by using the relative closeness coefficient. Finally, we use the cases in the database to simulate the experiment to verify the decision-making effect of the proposed model. Comparative analysis and experimental analysis also show the effectiveness, superiority, and stability of the proposed model.

LEON: A New Framework for ML-Aided Query Optimization

Query optimization has long been a fundamental yet challenging topic in the database field. With the prosperity of machine learning (ML), some recent works have shown the advantages of reinforcement learning (RL) based learned query optimizer. However, they suffer from fundamental limitations due to the data-driven nature of ML. Motivated by the ML characteristics and database maturity, we propose LEON -a framework for ML-aidEd query OptimizatioN. LEON improves the expert query optimizer to self-adjust to the particular deployment by leveraging ML and the fundamental knowledge in the expert query optimizer. To train the ML model, a pairwise ranking objective is proposed, which is substantially different from the previous regression objective. To help the optimizer to escape the local minima and avoid failure, a ranking and uncertainty-based exploration strategy is proposed, which discovers the valuable plans to aid the optimizer. Furthermore, an ML model-guided pruning is proposed to increase the planning efficiency without hurting too much performance. Extensive experiments offer evidence that the proposed framework can outperform the state-of-the-art methods in terms of end-to-end latency performance, training efficiency, and stability.

Комплексная оценка геотехнических рисков в шахтном и подземном строительстве

Introduction. Projects for the construction of underground structures and mines often involve significant geotechnical risks. The uncertainty associated with geotechnical conditions implies the development of a specific approach to making design and constructive decisions. Even after a thorough geotechnical study, the ability to accurately predict the behavior of adjacent rock massif is often difficult due to the large number of engineering-geological and hydro-geological variables. Most of the identified risks can be eliminated at the design stage. Residual risks should be subjected to a comprehensive assessment, including qualitative and quantitative analysis and calculations of damage from potential risk and technical and economic efficiency of the adopted design decision. Objective. Complex estimation of geotechnical risks in mine and underground construction based on the analysis of indicators of the implementation of accidents by mining-geological and mining-technical factors, taking into account the use of special methods of construction. Methodology. A сomplex estimation of geotechnical risks was carried out on the basis of the theory of emergency risk using methods of mathematical statistics, probability theory; the method of expert assessments to obtain the values of vulnerability coefficients of the applied technologies of mine and underground construction. The developed methodology includes several stages. At the first stage of a сomplex risk estimation, it is necessary to rank the geotechnical risk factors selected for analysis, for which a concordance coefficient is introduced. When analyzing each of the emerging geotechnical figures during the construction of a specific underground or mine construction facility, one of the defining indicators is the numerical expression of the potential damage from the occurrence of an emergency situation, which represents the percentage ratio of the excess of the construction period in real practice to the same indicator laid down in the project, or the ratio of the increase in the cost of construction to the estimated cost. Next, the numerical value of each type of risk is determined and a scale of numerical values of risks is compiled, on the basis of which decisions are made on the management of each specific risk. At the end, the vulnerability of a mine or underground construction facility under construction is determined, i.e. the degree of its possible damage and destruction when exposed to abnormal situations of a natural and man-made nature. Results and discussion. A methodology has been developed for the сomplex estimation of geotechnical risk in mine and underground construction based on the ranking of risks according to the degree of vulnerability of the technologies used. Ranking of construction objects by their vulnerability to various impacts allows us to assess the correlation between the values of the parameters and the qualitative characteristics of the elements of the “mining – rock mass – construction technology” system and the factors that lead to the development of potential damage. In the course of the research, a generalized indicator of the predicted geotechnical risk was identified, which allows taking into account the impact of construction methods on the risk. Since mine workings are currently being built mainly in complex hydro-geological and engineering-geological conditions, the calculation of the generalized indicator focuses on the use of special construction methods, which required the introduction of correction factors to calculate the indicator of the predicted geotechnical risk. The values of correction coefficients for the most used special methods were obtained by the method of expert assessments. Conclusions. One of the important aspects of a сomplex estimation of geotechnical risks in mine and underground construction is to determine the vulnerability of the “mining - rock mass – technology” system. Vulnerability depends on the location of the mine, its cross-section, engineering-geological and hydro-geological conditions, mode of operation, reliability of load-bearing and fencing, etc. By the method of сomplex estimation, vulnerability coefficients of underground structures under construction and mine workings were obtained, as well as correction coefficients taking into account the construction method.

Attributes Reduction on SE-ISI Concept Lattice for an Incomplete Context Using Object Ranking

The formal concept of lattice plays a vital role in knowledge discovery. Reduction of the attribute has many applications in machine learning technology and data mining fields. In this paper, we introduce an object ranking concept to define a consistency set and the reduction of the attributes by structural features. An incomplete information system works on the three-way concepts using the SE-ISI Context. The granular was emphasized with join (meet) irreducible sets using the object ranking concepts. A dual operator is defined based on the object ranking concepts and its properties and conditions are verified. Hence, this elaborates on the four kinds of reduction of the attributes. The ordered pairs give the knowledge of the attributes that deal with the interval set of both the approximation of rough set theory concerning the objects. Therefore, the relationship between four kinds of reduction of the attribute was appropriate to access the consistency set using the object ranking concepts by some of the theorems and examples.