This paper presents the development of two distinct real-time procedural planet generators within the Godot engine: one employing Fractal Brownian Motion (FBM) with Perlin Noise, and another adapting Minecraft-inspired layered noise techniques. We detail their implementation, including a quadtree-based Level of Detail (LOD) system and solutions for planetary mesh generation. A comparative user study (N=15) was conducted where participants explored unique instances generated by our two algorithms alongside two existing procedural planet projects.
This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These can result in subjectivity and significant information loss, particularly for countries with systems near categorical borders. To overcome these shortcomings, the present paper proposes distance-based indices for objective categorization, considering economic foundations and using hard data. Accordingly, the paper introduces institutional similarity indices--Capitalism Similarity Index (CapSI), Communism Similarity Index (ComSI), and Socialism Similarity Index (SocSI)-which reflect countries' positions along the economic system continuum. These indices adhere to mathematical rigor and are grounded in the mathematical fields of real analysis, metric spaces, and distance functions. By classifying 135 countries and creating GIS maps, the practical applicability of the proposed approach is demonstrated. Results show a high explanatory power of the introduced indices, suggesting their beneficial usage in comparative economic studies. The paper advocates for their adoption due to their objectivity and ability to capture structural and institutional nuances without subjective judgments while also considering the continuous nature of the spectrum of economic systems.
In choice under risk, there is a standard notion of 'less risk-averse than', due to Yaari (1969). In the theory of comparative statics, the single-crossing property is satisfied by all weighted averages of a family of single-crossing functions if and only if the family satisfies a property called signed-ratio monotonicity (Quah & Strulovici, 2012). We establish a close link between 'less risk-averse than' and signed-ratio monotonicity.
This thesis concerns multivariate phylogenetic comparative methods. We investigate two aspects of them. The first is the bias caused by measurement error in regression studies of comparative data. We calculate the formula for the bias and show how to correct for it. We also study whether it is always advantageous to correct for the bias as correction can increase the mean square error of the estimate. We propose a criterion, which depends on the observed data, that indicates whether it is beneficial to correct or not. Accompanying the results is an R program that offers the bias correction tool. The second topic is a multivariate model for trait evolution which is based on an Ornstein-Uhlenbeck type stochastic process, often used for studying trait adaptation, co-evolution, allometry or trade-offs. Alongside the description of the model and presentation of its most important features we present an R program estimating the model's parameters. To the best of our knowledge our program is the first program that allows for nearly all combinations of key model parameters providing the biologist with a flexible tool for studying multiple interacting traits in the Ornstein-Uhlenbeck framework. There are numerous packages available that include the Ornstein-Uhlenbeck process but their multivariate capabilities seem limited. [COMMENT: Please note that this abstract and thesis is from 2011]
This is a list of problems that were collected from participants at the Comparative Prime Number Theory Symposium held at UBC from June 17 to June 21, 2024. Its goal is to stimulate research and future collaborations in this growing field. This event was part of the PIMS (Pacific Institute of Mathematical Sciences) Collaborative Research Group L-functions in Analytic Number Theory: 2022- 2025.
This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Experts in natural language processing (NLP) are becoming more and more interested in sentiment analysis (SA) of text changes. The objective of employing NLP text analysis techniques is to recognize and categorize feelings related to twitter users utterances. In this examination, issues with SA and the libraries utilized are also looked at. provides a number of cooperative methods to classify emotional polarity. The Naive Bayes Classifier, Decision Tree Classifier, Maxent Classifier, Sklearn Classifier, Sklearn Classifier MultinomialNB, and other conjoint learning algorithms, according to recent research, are very effective. In the project will use Five Python and R libraries NLTK, TextBlob, Vader, Transformers (GPT and BERT pretrained), and Tidytext will be used in the study to apply sentiment analysis techniques. Four machine learning models Tree of Decisions (DT), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) will also be used. To evaluate how well libraries for SA operate in the social network environment, comparative study was also carried out. The measures to assess the best algorithms in this experiment, which used a single data set for each method, were precision, recall, and F1 score. We conclude that the BERT transformer method with an Accuracy: 0.973 is recommended for sentiment analysis.
Systems that support users in the automatic creation of visualizations must address several subtasks - understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. In this work, we pose visualization generation as a multi-stage generation problem and argue that well-orchestrated pipelines based on large language models (LLMs) such as ChatGPT/GPT-4 and image generation models (IGMs) are suitable to addressing these tasks. We present LIDA, a novel tool for generating grammar-agnostic visualizations and infographics. LIDA comprises of 4 modules - A SUMMARIZER that converts data into a rich but compact natural language summary, a GOAL EXPLORER that enumerates visualization goals given the data, a VISGENERATOR that generates, refines, executes and filters visualization code and an INFOGRAPHER module that yields data-faithful stylized graphics using IGMs. LIDA provides a python api, and a hybrid user interface (direct manipulation and multilingual natural language) for interactive chart, infographics and data story generation. Learn more about the project here - https://microsoft.github.io/lida/
This paper presents a comparative study three numerical schemes such as Linear, Quadratic and Quadratic-Linear scheme for the fractional integro-differential equations defined in terms of the Caputo fractional derivatives. The error estimates of the respective approximations are also established. Numerical tests of the discussed schemes show that all schemes work well, and when the number of terms approximating the solution are increased, the desired solution is achieved. The accuracy of the numerical schemes with respect to the step size h is analyzed and illustrated through various tables. Finally, comparative performances of the schemes are discussed.
There is a heated debate about how far computers can map the complexity of text analysis compared to the abilities of the whole team of human researchers. A "deep" analysis of a given text is still beyond the possibilities of modern computers. In the heart of the existing computational text analysis algorithms there are operations with real numbers, such as additions and multiplications according to the rules of algebraic fields. However, the process of "comparing" has a very precise mathematical structure, which is different from the structure of an algebraic field. The mathematical structure of "comparing" can be expressed by using Boolean rings. We build on this structure and define the corresponding algebraic equations lifting algorithms of comparative text analysis onto the "correct" algebraic basis. From this point of view, we can investigate the question of {\em computational} complexity of comparative text analysis.
Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid
et al.
This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzing. Pythia uses a statistical model to learn common usage patterns of a target REST API from structurally valid seed inputs. It then generates learning-based mutations by injecting a small amount of noise deviating from common usage patterns while still maintaining syntactic validity. Pythia's mutation strategy helps generate grammatically valid test cases and coverage-guided feedback helps prioritize the test cases that are more likely to find bugs. We present experimental evaluation on three production-scale, open-source cloud services showing that Pythia outperforms prior approaches both in code coverage and new bugs found. Using Pythia, we found 29 new bugs which we are in the process of reporting to the respective service owners.
Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh
et al.
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary classification of malware versus benign, as well as the multiclass problem, where we classify malware samples into their respective families. Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, AdaBoost, and artificial neural networks. We find that permissions are a strong feature and that by careful feature engineering, we can significantly reduce the number of features needed for highly accurate detection and classification.
The present study was a comparative analysis of the inductive and the deductive methods in teaching English. Indeed, the major aim of the study was to compare the efficiency of these two approaches in teaching English grammar by inspecting students’ performance. To this purpose, two identical groups of German pre-service teachers were randomly selected to participate in this research. Then, two English grammar topics (future tense and conditional sentences) were taught to them by the usage of PPP method as the deductive approach and the guided discovery technique as the inductive approach. Regarding the methodology, the design of the study was comparison group design (between-subjects design) and the TTT (Test-Teach-Test) method was obtained in which a grammar pre-test and post-test comparison were executed to check the level of improvement in the students. The achieved scores in the tests indicated that both the inductive and the deductive methods were equal in terms of efficiency.
In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. as well as modifications over the original model and provide a comparative analysis of these models.
Dany Vohl, David G. Barnes, Christopher J. Fluke
et al.
We present encube $-$ a qualitative, quantitative and comparative visualisation and analysis system, with application to high-resolution, immersive three-dimensional environments and desktop displays. encube extends previous comparative visualisation systems by considering: 1) the integration of comparative visualisation and analysis into a unified system; 2) the documentation of the discovery process; and 3) an approach that enables scientists to continue the research process once back at their desktop. Our solution enables tablets, smartphones or laptops to be used as interaction units for manipulating, organising, and querying data. We highlight the modularity of encube, allowing additional functionalities to be included as required. Additionally, our approach supports a high level of collaboration within the physical environment. We show how our implementation of encube operates in a large-scale, hybrid visualisation and supercomputing environment using the CAVE2 at Monash University, and on a local desktop, making it a versatile solution. We discuss how our approach can help accelerate the discovery rate in a variety of research scenarios.
This paper presents a novel research problem on joint discovery of commonalities and differences between two individual documents (or document sets), called Comparative Document Analysis (CDA). Given any pair of documents from a document collection, CDA aims to automatically identify sets of quality phrases to summarize the commonalities of both documents and highlight the distinctions of each with respect to the other informatively and concisely. Our solution uses a general graph-based framework to derive novel measures on phrase semantic commonality and pairwise distinction}, and guides the selection of sets of phrases by solving two joint optimization problems. We develop an iterative algorithm to integrate the maximization of phrase commonality or distinction measure with the learning of phrase-document semantic relevance in a mutually enhancing way. Experiments on text corpora from two different domains---scientific publications and news---demonstrate the effectiveness and robustness of the proposed method on comparing individual documents. Our case study on comparing news articles published at different dates shows the power of the proposed method on comparing document sets.
The article presents a comparative analysis of the accuracy of the distance to the observed object for geometric methods in noisy observations of bearings-only information.
Rigidity analysis using the "pebble game" has been applied to protein crystal structures to obtain information on protein folding, assembly and t he structure-function relationship. However, previous work using this technique has not made clear how the set of hydrogen-bond constraints included in the rigidity analysis should be chosen, nor how sensitive the results of rigidity analysis are to small structural variations. We present a comparative study in which "pebble game" rigidity analysis is applied to multiple protein crystal structures, for each of six differen t protein families. We find that the mainchain rigidity of a protein structure at a given hydrogen-bond energy cutoff is quite sensitive to small structural variations, and conclude that the hydrogen bond constraints in rigidity analysis should be chosen so as to form and test specific hypotheses about the rigidity o f a particular protein. Our comparative approach highlights two different characteristic patterns ("sudden" or "gradual") for protein rigidity loss as constraints are re moved, in line with recent results on the rigidity transitions of glassy networks.
This paper presents a comparative analysis of complexes made of one Rare Gas (Rg) atom and molecular hydrogen, for all five stable Rg atoms. In particular, the vibrational band origins have been calculated, as well as the structural properties of the associated wavefunctions. The study is extended to cold Rg-H$_2$ scattering. The molecular systems are studied variationally using a very simple, yet effective, trial wavefunction. A large number of Potential Energy Surfaces available from the literature is considered. A comparative analysis shows that differences of up to two orders of magnitude exist for the zero energy elastic cross sections of the five complexes. Corrections to the model have also been considered, showing no significant effect.
Parallel kinematic mechanisms are interesting alternative designs for machining applications. Three 2-DOF parallel mechanism architectures dedicated to machining applications are studied in this paper. The three mechanisms have two constant length struts gliding along fixed linear actuated joints with different relative orientation. The comparative study is conducted on the basis of a same prescribed Cartesian workspace for the three mechanisms. The common desired workspace properties are a rectangular shape and given kinetostatic performances. The machine size of each resulting design is used as a comparative criterion. The 2-DOF machine mechanisms analyzed in this paper can be extended to 3-axis machines by adding a third joint.