Article
Computer Science, Theory & Methods
Marc Haerkoenen, Lisa Nicklasson, Bogdan Raita
Summary: We study linear PDE with constant coefficients and investigate the connection between the constant rank condition and primary decomposition. We also make progress in the study of weak lower semicontinuity of integral functionals defined on sequences of PDE constrained fields when the PDEs do not have constant rank.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Lynn Pickering, Tereso del Rio Almajano, Matthew England, Kelly Cohen
Summary: In recent years, there has been an increase in the use of machine learning techniques in mathematics, specifically in symbolic computation for optimizing and selecting algorithms. This paper explores the potential of using explainable AI techniques on these ML models to provide new insights for symbolic computation and inspire new implementations within computer algebra systems.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Ke-Ming Chang, Kuo-Chang Chen
Summary: In this paper, we develop symbolic computation algorithms to investigate the finiteness of central configurations for the planar n-body problem. We introduce matrix algebra to determine possible diagrams and asymptotic orders, devise criteria to reduce computational complexity, and determine possible zw-diagrams by automated deductions. For the planar six-body problem, we show that there are at most 86 zw-diagrams.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Yulia Alexandr, Serkan Hosten
Summary: In this study, the theory of logarithmic Voronoi cells is extended to Gaussian statistical models. The properties of logarithmic Voronoi cells are analyzed for models of ML degree one and linear covariance models. The decomposition theory of logarithmic Voronoi cells is introduced for the latter family. The characteristics of logarithmic Voronoi cells in covariance models are also studied.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Elisa Gorla, Flavio Salizzoni
Summary: The MacWilliams' Extension Theorem, proposed by Florence Jessie MacWilliams, investigates the extension of linear isometries between linear block-codes to linear isometries of the ambient space. This paper explores the applicability of this theorem to rank-metric codes, providing examples and results.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Julia Lindberg, Jose Israel Rodriguez
Summary: In this paper, we investigate the Shor relaxation of quadratic programs by fixing a feasible set and examining the space of objective functions for which the Shor relaxation is exact. We establish conditions for the invariance of this region under the choice of generators defining the feasible set and describe its characteristics when the feasible set is invariant under the action of a subgroup of the general linear group. Furthermore, we apply these findings to quadratic binary programs and present an algorithm that generates candidate solutions based on an explicit description of objective functions where the Shor relaxation is exact.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Martin Helmer, Elias Tsigaridas
Summary: We propose a probabilistic algorithm to test if a homogeneous polynomial ideal I defining a scheme X in Pn is radical. This algorithm utilizes Segre classes and other geometric notions from intersection theory and is applicable for certain classes of ideals. The algorithm terminates successfully with singly exponential complexity in n except in cases where all isolated primary components of X are reduced and there are no embedded root components outside of the singular locus of Xred = V(I), in which case it is unable to decide radically.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
S. G. Solodky, S. A. Stasyuk
Summary: The problem of numerical differentiation for periodic functions with finite smoothness is examined. Various truncation methods are developed for multivariate functions and their approximation properties are determined. Based on these findings, sharp bounds in terms of power scale are derived for the minimum radius of Galerkin information for the studied problem.
JOURNAL OF COMPLEXITY
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Nik Amir Syafiq, Mohamed Othman, Norazak Senu, Fudziah Ismail, Nor Asilah Wati Abdul Hamid
Summary: This research investigates the multi-core architecture for solving the fractional Poisson equation using the modified accelerated overrelaxation (MAOR) scheme. The feasibility of the scheme in a parallel environment was tested through experimental comparisons and measurements. The results showed that the scheme is viable in a parallel environment.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
