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This paper aims to define and describe test methods and metrics to assess industrial robot system agility in both simulation and in reality.

The paper describes test methods and associated quantitative and qualitative metrics for assessing robot system efficiency and effectiveness, which can then be used for the assessment of system agility.

The paper describes how the test methods were implemented in a simulation environment and real-world environment. It also shows how the metrics are measured and assessed as they would be in a future competition.

The test methods described in this paper will push forward the state of the art in software agility for manufacturing robots, allowing small and medium manufacturers to better utilize robotic systems.

The paper fulfills the identified need for standard test methods to measure and allow for improvement in software agility for manufacturing robots.

With the increasing demand for surveillance and smart transportation, drone technology has become the center of attraction for robotics researchers. This study aims to introduce a new path planning approach to drone navigation based on topology in an uncertain environment. The main objective of this study is to use the Ricci flow evolution equation of metric and curvature tensor over angular Riemannian metric, and manifold for achieving navigational goals such as path length optimization at the minimum required time, collision-free obstacle avoidance in static and dynamic environments and reaching to the static and dynamic goals. The proposed navigational controller performs linearly and nonlinearly both with reduced error-based objective function by Riemannian metric and scalar curvature, respectively.

Topology and manifolds application-based methodology establishes the resultant drone. The trajectory planning and its optimization are controlled by the system of evolution equation over Ricci flow entropy. The navigation follows the Riemannian metric-based optimal path with an angular trajectory in the range from 0° to 360°. The obstacle avoidance in static and dynamic environments is controlled by the metric tensor and curvature tensor, respectively. The in-house drone is developed and coded using C++. For comparison of the real-time results and simulation results in static and dynamic environments, the simulation study has been conducted using MATLAB software. The proposed controller follows the topological programming constituted with manifold-based objective function and Riemannian metric, and scalar curvature-based constraints for linear and nonlinear navigation, respectively.

This proposed study demonstrates the possibility to develop the new topology-based efficient path planning approach for navigation of drone and provides a unique way to develop an innovative system having characteristics of static and dynamic obstacle avoidance and moving goal chasing in an uncertain environment. From the results obtained in the simulation and real-time environments, satisfactory agreements have been seen in terms of navigational parameters with the minimum error that justifies the significant working of the proposed controller. Additionally, the comparison of the proposed navigational controller with the other artificial intelligent controllers reveals performance improvement.

In this study, a new topological controller has been proposed for drone navigation. The topological drone navigation comprises the effective speed control and collision-free decisions corresponding to the Ricci flow equation and Ricci curvature over the Riemannian metric, respectively.

The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.

First, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.

The novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.

The rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.

The algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

This paper presents a mixed methodology to map and analyse the spatial connectivity of the everyday pathways that link the doorway of an individual's home–work locations to the local main commercial street. These pathways include public streets, semi-private lanes, alleys and stairs.

Pathways within different morphological areas in Hanoi, Vietnam, are used as examples to illustrate the development and application of the methodology. The methodology, adapted from Conzenian, typological, and space syntax methods, combined with observations and interviews, seeks to overcome several identified limitations of each of these approaches for understanding pedestrians' horizontal and vertical movement patterns within urban settings.

Analytical diagrams of pathways are developed on figure-ground maps of the neighbourhoods and three-dimensional projections of circulation spaces within buildings. Scatter plots are used to analyse the distribution of collected samples according to their business types and distances to local main streets. Field observations and interviews with homeowners revealed the critical influence of the pathways' spatial characteristics on home-based businesses' operations.

The methods developed here are potentially useful for urban morphologists and urban designers in decoding the intricacies of informal urban settings and understanding their socio-economic significance for users.

This paper aims to propose a fast and robust 3D point set registration method for pose estimation of assembly features with few distinctive local features in the manufacturing process.

The distance between the two 3D objects is analytically approximated by the implicit representation of the target model. Specifically, the implicit B-spline surface is adopted as an interface to derive the distance metric. With the distance metric, the point set registration problem is formulated into an unconstrained nonlinear least-squares optimization problem. Simulated annealing nested Gauss-Newton method is designed to solve the non-convex problem. This integration of gradient-based optimization and heuristic searching strategy guarantees both global robustness and sufficient efficiency.

The proposed method improves the registration efficiency while maintaining high accuracy compared with several commonly used approaches. Convergence can be guaranteed even with critical initial poses or in partial overlapping conditions. The multiple flanges pose estimation experiment validates the effectiveness of the proposed method in real-world applications.

The proposed registration method is much more efficient because no feature estimation or point-wise correspondences update are performed. At each iteration of the Gauss–Newton optimization, the poses are updated in a singularity-free format without taking the derivatives of a bunch of scalar trigonometric functions. The advantage of the simulated annealing searching strategy is combined to improve global robustness. The implementation is relatively straightforward, which can be easily integrated to realize automatic pose estimation to guide the assembly process.

The purpose of this paper is to describe a large-scale algorithm for generating a catalogue of scientific publication records (citations) from a crowd-sourced data, demonstrate how to learn an optimal combination of distance metrics for duplicate detection and introduce a parallel duplicate clustering algorithm.

The authors developed the algorithm and compared it with state-of-the art systems tackling the same problem. The authors used benchmark data sets (3k data points) to test the effectiveness of our algorithm and a real-life data ( > 90 million) to test the efficiency and scalability of our algorithm.

The authors show that duplicate detection can be improved by an additional step we call duplicate clustering. The authors also show how to improve the efficiency of map/reduce similarity calculation algorithm by introducing a sampling step. Finally, the authors find that the system is comparable to the state-of-the art systems for duplicate detection, and that it can scale to deal with hundreds of million data points.

Academic researchers can use this paper to understand some of the issues of transitivity in duplicate detection, and its effects on digital catalogue generations.

Industry practitioners can use this paper as a use case study for generating a large-scale real-life catalogue generation system that deals with millions of records in a scalable and efficient way.

In contrast to other similarity calculation algorithms developed for m/r frameworks the authors present a specific variant of similarity calculation that is optimized for duplicate detection of bibliographic records by extending previously proposed e-algorithm based on inverted index creation. In addition, the authors are concerned with more than duplicate detection, and investigate how to group detected duplicates. The authors develop distinct algorithms for duplicate detection and duplicate clustering and use the canopy clustering idea for multi-pass clustering. The work extends the current state-of-the-art by including the duplicate clustering step and demonstrate new strategies for speeding up m/r similarity calculations.

The purpose of this paper is to provide classification confidence value to every individual sample classified by decision trees and use this value to combine the classifiers.

The proposed system is first theoretically explained, and then the use and effectiveness of the proposed system is demonstrated on sample datasets.

In this paper, a novel method is proposed to combine decision tree classifiers using calculated classification confidence values. This confidence in the classification is based on distance calculation to the relevant decision boundary (distance conditional), probability density estimation and (distance conditional) classification confidence estimation. It is shown that these values – provided by individual classification trees – can be integrated to derive a consensus decision.

The proposed method is not limited to axis‐parallel trees, it is applicable not only to oblique trees, but also to any kind of classifier system that uses hyperplanes to cluster the input space.

A novel method is presented to extend decision tree like classifiers with confidence calculation and a voting system is proposed that uses this confidence information. The proposed system possesses several novelties (e.g. it not only gives class probabilities, but also classification confidences) and advantages over previous (traditional) approaches. The voting system does not require an auxiliary combiner or gating network, as in the mixture of experts structure and the method is not limited to decision trees with axis‐parallel splits; it is applicable to any kind of classifiers that use hyperplanes to cluster the input space.

This study introduces a new approach, called the social structure approach, for ranking academic journals by focusing on hospitality and tourism journals; and a hybrid metric, including the combination of the journal impact factor via citations and a social network metric, called the journal knowledge domain index (JKDI).

Twenty-five hospitality and tourism journals were selected to test this approach. Collaboration-based metrics, productivity-based metrics, and network-based metrics are considered components of the social structure approach. Additionally, a hybrid metric, including the combination of the journal impact factor via citations and a social network metric, JKDI, is developed.

The study’s findings show that top or leading journals have a weaker position in some social structure approach metrics compared to other (or follower) journals. However, according to the JKDI, leading journals have remained constant with the other ranking studies.

The ranking of academic journals is vital for the stakeholders of academia. Consequently, the findings of this study may help stakeholders to design an optimal ranking system and formulate and implement effective research strategies for knowledge creation and dissemination.

As one of the first in the journal-ranking literature, this study has significant implications, as it introduces a new ranking approach.

Financial access is key to achieving several economic goals in developing countries. This paper aims to construct a longitudinal village-level measure of financial access in India and understand the role of RBI's policies and village characteristics in influencing the access.

The authors adopt a spatial approach in developing a metric of financial access. In particular, they measure the distance of each unbanked village in India to the nearest banked-centre from 1951 to 2019. The authors use this measure to conduct two exercises. First, a descriptive study is undertaken to assess how RBI's policies on bank branch expansion from 1951 to 2019 influenced the proximity to bank branches. Second, the authors conduct regression analyses to investigate how socio-economic and demographic characteristics of villages influence their proximity to bank branches.

The average distance of an unbanked village to the nearest banked-centre has declined from 43.5 km in 1951 to 4.2 km in 2019. The gain in bank access has varied geographically and over time. In 2001, bank branches were relatively distant from villages with under-privileged caste groups and proximate to areas with better infrastructure. This relationship worsened after 2005 when RBI introduced liberalized branch expansion policies. By 2019, proximity responds much more adversely to the presence of underprivileged groups. At the same time, banks have moved closer to economically better-off villages and villages with workforce in non-farm enterprises rather than agriculture.

First, studies in the Indian context focus on state-level determinants of bank branching, this is the first study to develop a longitudinal measure of financial access at the village level. This helps to understand spatial heterogeneity in bank branch access within states, which other studies are unable to do. Second, the paper analyses the role of village-level socio-economic and demographic characteristics in proximity to bank branches. This analysis helps in discovering micro-foundations of growth of bank branch network. The granularity of the approach adopted here overcomes the confoundedness problems that the studies at a more aggregate level face.

Constrained clustering is an important recent development in clustering literature. The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge. The purpose of this paper is to suggest a new perspective for constrained clustering, by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must- and cannot-link constraints.

Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space. Unlike previous efforts, the author presents a non-linear method for constraint clustering, whose basic idea is to use different non-linear functions for each dimension in target space.

The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linear functions for each dimension in target space. The proposed method for a particular case is formulated and explained for quadratic functions. To reduce the number of optimization parameters, the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.

This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions. This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.