Distance-based clustering challenges for unbiased benchmarking studies

Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clustering solution. Data sets might not have cluster structures. Clustering yields arbitrary labels and often depends on the trial, leading to varying results. Moreover, recent research indicated that all partition comparison measures can yield the same results for different clustering solutions. Consequently, algorithm selection and parameter optimization by unsupervised quality measures (QM) are always biased and misleading. Only if the predefined structures happen to meet the particular clustering criterion and QM, can the clusters be recovered. Results are presented based on 41 open-source algorithms which are particularly useful in biomedical scenarios. Furthermore, comparative analysis with mirrored density plots provides a significantly more detailed benchmark than that with the typically used box plots or violin plots.

A Zonal Displacement Approach via Grid Point Weighting in Building Generalization

When generalizing a group of objects, displacement is an essential operation to resolve the conflicts arising between them due to enlargement of their symbol sizes and reduction of available map space. Although there are many displacement methods, most of them are rather complicated. Therefore, more practical methods are still needed. In this article, a new building displacement approach is proposed. For this purpose, buildings are grouped and zones are created for them in the blocks via Voronoi tessellation and buffering. Linear patterns are then detected through buffer analyses and the respective zones are narrowed to be able to preserve these patterns. After all the buildings are displaced inside their zones, grid points are generated and then weighted through kernel density estimation and buffer analyses to find suitable locations. Accordingly, the buildings are displaced toward the computed locations iteratively. The proposed approach directly enforces minimum distance and positional accuracy constraints while several indirect mechanisms are used for preserving spatial patterns and relationships. For the quality evaluation of the displacement, the angle, length and shape comparison measures are introduced, computed based on the (Delaunay) triangles or the azimuth comparison measure of the connection lines, generated for the buildings. The quality evaluation criteria are yielded according to the visual assessment of the displacement quality and the quantitative analysis of the measures. The findings demonstrate that the proposed approach is quite effective and practical for zonal building displacement.

Edit distance-based search approach for retrieving element-wise prosody/rhymes in Hindi-Urdu poetry

Background: Prosody (rhyming words) is a connatural element of poetry, throughout its reach, across thousands of languages in the world. Since medieval era, the Indic poetry (principally the Hindi/Urdu poetry) has created an impactful flamboyance w.r.t the subjects, styles, and other creative aspects in poetry. Besides the message of heartfelt poetry, we see the Qafiya (i.e., rhyming words) is the core element, without which we may not consider anything Hindi/Urdu poetry but merely a piece of writing; alongside it, Radif (i.e., a phrasal suffix to qafiya) is also considered next to the intrinsic part in Ghazals. In this regard, the contributions of this paper are one–the development of an optimal technique for the prosodic (qafiya) suggestions/retrieval in Hindi/Urdu poetry; and two–the qafiya suggestions based on the attached subsequent radif. Methods: The work in this paper involves usage of a 13.46 M tokens tri-script corpus of poetry. Instead of phonetic value matching, the proposed methodology employs four different Edit Distances (i.e., Levenshtein, Damerau–Levenshtein, Jaro–Winkler, and Hamming distance) as the comparison measures for prosodic suggestions. Findings: The proposed work shows better results in comparison to ‘Qaafiya Dictionary’ powered by rekhta.org. Moreover, w.r.t the inter-metric similarity and running time Jaro–Winkler appears to be the most optimal algorithm for the rhyme suggestion, whereas the Levenshtein distance is the laziest technique. Novelty/Applications: This work benefits researchers of Indic natural language processing for lexical look-ups and analysis of creative literature, especially poetry.

Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing

The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.

Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing

The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.

Analysis of Stochastic Reserving Models By Means of NAIC Claims Data

In the past two decades increasing computational power resulted in the development of more advanced claims reserving techniques, allowing the stochastic branch to overcome the deterministic methods, resulting in forecasts of enhanced quality. Hence, not only point estimates, but predictive distributions can be generated in order to forecast future claim amounts. The significant expansion in the variety of models requires the validation of these methods and the creation of supporting techniques for appropriate decision making. The present article compares and validates several existing and self-developed stochastic methods on actual data applying comparison measures in an algorithmic manner.

Aristotelian Time

In this paper we offer a critical account of Aristotelian theory of time. After a brief presentation of the main views of Aristotle on the infinite, we focus the attention to the status of points with respect to the potentiality-actuality distinction. Then we address Aristotle’s views on time on the basis of the Aristotelian notion of continuity. We construe the “nows” as potentialities awaiting to be actualized. We show that it is the intervention of an agent (soul), who, through finitely many unitary mental acts of noticing or perceiving, guarantees the actualization of particular “nows”, constructs or brings into existence time intervals and through their comparison measures them, so producing what Aristotle calls time.

An Initial Investigation of the Use of Comparative Referents to Assess Social Anxiety

The current study examined the validity of two self-report measures of social anxiety constructed using social comparative referent points. It was hypothesized that these comparison measures would be both reliable and valid. Results indicated that two different comparative versions – one invoking injunctive norms and another invoking descriptive norms – showed good reliability, excellent internal consistency, and acceptable convergent and discriminant validity. The comparative measures also predicted positive functioning, some aspects of social quality of life, and social anxiety as measured by an independent self-report. These findings suggest that adding a comparative reference point to instructions on social anxiety measures may aid in the assessment of social anxiety.