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Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Hierarchical GraphCut Phase Unwrapping based on Invariance of Diffeomorphisms Framework

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 546-554页

作者： Gao, Xiang Wang, Xinmu Zhao, Zhou Huang, Junqi Gu, Xianfeng David Department of Computer science Stony Brook University NY USA Deparment of Applied Mathematics and Statistics Stony Brook University NY USA

Recent years have witnessed rapid advancements in 3D scanning technologies, with diverse applications spanning VR/AR, digital human creation, and medical imaging. Structured-light scanning with phase-shifting techniques is preferred for its use of non-radiative, low-intensity visible light and high accuracy, making it well suited for human-centric applications such as capturing 4D facial dynamics. A key step in these systems is phase unwrapping, which recovers continuous phase values from measurements that are inherently wrapped modulo 2π. The goal is to estimate the unwrapped phase count k, an integer-valued variable in the equation Φ = ∅ + 2πk, where ∅ is the wrapped phase and Φ is the true phase. However, the presence of noise, occlusions, and piecewise continuous phase functions induced by complex 3D surface geometry makes the inverse reconstruction of the true phase extremely challenging. This is because phase unwrapping is an inherently ill-posed problem: measurements only provide modulo 2π values, and recovering the correct unwrapped phase count requires strong assumptions about the smoothness or continuity of the underlying 3D surface. Existing methods typically involve a trade-off between speed and accuracy: Fast approaches lack precision, while accurate algorithms are too slow for real-time use. To overcome these limitations, this work proposes a novel phase unwrapping framework that reformulates GraphCut-based unwrapping as a pixel-labeling problem. This framework helps significantly improve the estimation of the unwrapped phase count k through the invariance property of diffeomorphisms applied in image space via conformal and optimal transport (OT) maps. An odd number of diffeomorphisms are precomputed from the input phase data, and a hierarchical GraphCut algorithm is applied in each corresponding domain. The resulting label maps are fused via majority voting to efficiently and robustly estimate the unwrapped phase count k at each pixel, using an odd nu

关键词： Transillumination

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Synthesizing 3D Axon Morphology: Springs are All We Need 15th

Synthesizing 3D Axon Morphology: Springs are All We Need

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Cui, Ruiqi Bærentzen, J. Andreas Dyrby, Tim B. Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager and Hvidovre Hvidovre2650 Denmark

ISBN: (纸本)9783031869198

The realism of digital phantoms for the white matter microstructure is highly valued. Realistic synthesis provides reliable input to generate synthetic diffusion MRI signals for evaluating biophysical models or training machine learning models of microstructure features, such as axon diameter, shapes, and cellular structures. Inspired by the popular spring-mass systems used in physical simulation, we propose a novel and flexible method for synthesizing axon morphology and its dynamics with physical constraints. Specifically, starting with an initial axon configuration, our method constructs a spring-mass system based on specific sampling rules inspired by the real 3D axons and cell morphology observed in X-ray synchrotron imaging. By minimizing the spring potential energy, our method optimizes the positions of sampled mass points, thereby deforming the axon morphology from its physical surroundings. After the optimization, a triangle mesh of the axon surfaces is obtained and can be used as input for Monte Carlo diffusion MRI simulations. Experimental results demonstrate that our approach successfully mimics a range of axon morphologies and the dynamic environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image coding

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Integrative Analysis of Gene Co-Expression Networks Combined with Flux Balance Analysis of Metabolic Models for Insights into Chronic Obstructive Pulmonary Disease

Integrative Analysis of Gene Co-Expression Networks Combined...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Thummarat Paklao Apichat Suratanee Kitiporn Plaimas Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Chronic obstructive pulmonary disease (COPD) is a complex respiratory condition involving multifaceted molecular mechanisms. Differential gene expression analysis has identified genes with significant up-regulation and down-regulation in COPD patients compared to healthy controls, as well as those exhibiting high expression variability. However, relying solely on gene expression analysis is insufficient to fully understand these mechanisms. Therefore, in this study, we propose combining gene expression analysis, its co-expression network, and flux balance-based metabolic models to gain a deeper understanding of the disease’s complexity. This integration has revealed several metabolic subsystems associated with COPD and established a concordance between flux values and gene expression in COPD-related clusters. It demonstrates how effectively computational approaches can capture the molecular and metabolic alterations underlying COPD and align with experimental outcomes. The findings also offer biomarkers and pathways that can serve as a foundation for the development of COPD treatments. In summary, this study highlights the efficacy of an integrative computational approach that merges gene expression analysis, co-expression networks, and flux balance-based metabolic models. This methodology establishes connections between gene expression and metabolic flux while providing a valuable framework for future computational investigations and therapeutic strategies targeting COPD.

关键词： Analytical models Computational modeling Biological system modeling Biomarkers Chronic obstructive pulmonary disease Biochemistry Complexity theory Gene expression

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One- and two-sided prediction intervals for future Pareto record values with applications

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Journal of Statistical Theory and Applications 2025年 1-26页

作者： Schmiedt, Anja B. Empacher, Christina Kamps, Udo Department of Computer Science and Mathematics Regensburg University of Applied Sciences Regensburg 93053 Germany Institute of Statistics RWTH Aachen University Aachen 52056 Germany

Based on upper record values in a series of observations over time from a Pareto distribution, an exact and several approximate one-sided and two-sided prediction intervals for the next record value (or for another future record value) to appear are studied and compared by means of an extensive simulation study. The performances of the prediction intervals are evaluated and recommendations are proposed for what method should be used in a given situation with respect to the form of the prediction interval on the one hand and to the tail behaviour on the other. The proposed methods are applied to insurance, environmental and sports data, where the respectively fitted Pareto distributions show a different tail behaviour. As a result, it is seen that although the number of observed record values in the presented real data applications is rather small, as it is usually the case, the selected prediction intervals are of practical use. © The Author(s) 2025.

关键词： Interval prediction Pareto distribution Real data analyses Record values Simulation study

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Identifying Competence Gaps and Student Struggle by Monitoring Testing Performance 25

Identifying Competence Gaps and Student Struggle by Monitori...

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Proceedings of the 6th European Conference on Software Engineering Education

作者： Axel Böttcher Veronika Thurner Department of Computer Science and Mathematics HM Hochschule München University of Applied Sciences München Germany

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On Erlang mixture approximations for differential equations with distributed time delays

arXiv

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arXiv 2025年

作者： Ritschel, Tobias K.S. Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

In this paper, we propose a general approach for approximate simulation and analysis of delay differential equations (DDEs) with distributed time delays based on methods for ordinary differential equations (ODEs). The key innovation is that we 1) approximate the kernel by the probability density function of an Erlang mixture and 2) use the linear chain trick to transform the approximate DDEs to ODEs. Furthermore, we prove that an approximation with infinitely many terms converges for continuous and bounded kernels and for specific choices of the coefficients. We compare the steady states of the original DDEs and their stability criteria to those of the approximate system of ODEs, and we propose an approach based on bisection and least-squares estimation for determining optimal parameter values in the approximation. Finally, we present numerical examples that demonstrate the accuracy and convergence rate obtained with the optimal parameters and the efficacy of the proposed approach for bifurcation analysis and Monte Carlo simulation. The numerical examples involve a modified logistic equation and a point reactor kinetics model of a molten salt nuclear fission *** Codes 37M05, 39B99, 41A30, 65D15, 65P99, 65Q20 Copyright © 2025, The Authors. All rights reserved.

关键词： Probability density function

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TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models

arXiv

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arXiv 2025年

作者： Bulathwela, Sahan Van Niekerk, Daniel Shipton, Jarrod Perez-Ortiz, Maria Rosman, Benjamin Shawe-Taylor, John Centre for Artificial Intelligence Department of Computer Science University College London United Kingdom School of Computer Science and Applied Mathematics University of The Witwatersrand South Africa

Personalised education is one of the domains that can greatly benefit from the most recent advances in Artificial Intelligence (AI) and Large Language Models (LLM). However, it is also one of the most challenging applications due to the cognitive complexity teaching effectively while personalising the learning experience to suite independent learners. We hypothesise that one promising approach to excelling such demanding use-cases is using a society of minds. In this chapter, we present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models that can mimic micro skills that are composed together by a LLM to operationalise planning and reasoning. The architecture of the initial prototype is presented while describing two micro skills that have been incorporated in the prototype. The proposed system demonstrates the first step in building sophisticated AI systems that can take up very complex cognitive tasks that are demanded by domains such as education. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization

arXiv

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arXiv 2025年

作者： Cheng, Nuojin Papenmeier, Leonard Becker, Stephen Nardi, Luigi Department of Applied Mathematics University of Colorado Boulder United States Department of Computer Science Lund University Sweden DBtune Sweden

Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function’s optimum and are often considered fundamentally distinct from EI. In this work, we challenge this prevailing perspective by introducing a unified theoretical framework, Variational Entropy Search, which reveals that EI and information-theoretic acquisition functions are more closely related than previously recognized. We demonstrate that EI can be interpreted as a variational inference approximation of the popular information-theoretic acquisition function, named Max-value Entropy Search. Building on this insight, we propose VES-Gamma, a novel acquisition function that balances the strengths of EI and MES. Extensive empirical evaluations across both low- and high-dimensional synthetic and real-world benchmarks demonstrate that VES-Gamma is competitive with state-of-the-art acquisition functions and in many cases outperforms EI and MES. Copyright © 2025, The Authors. All rights reserved.

关键词： Information theory

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Exploring Bidirectional Bounds for Minimax-Training of Energy-Based Models

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International Journal of computer Vision 2025年 1-22页

作者： Geng, Cong Wang, Jia Chen, Li Gao, Zhiyong Frellsen, Jes Hauberg, Søren China Mobile Research Institute Beijing 100032 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Kgs.@ Lyngby 2800 Denmark

Energy-based models (EBMs) estimate unnormalized densities in an elegant framework, but they are generally difficult to train. Recent work has linked EBMs to generative adversarial networks, by noting that they can be trained through a minimax game using a variational lower bound. To avoid the instabilities caused by minimizing a lower bound, we propose to instead work with bidirectional bounds, meaning that we maximize a lower bound and minimize an upper bound when training the EBM. We investigate four different bounds on the log-likelihood derived from different perspectives. We derive lower bounds based on the singular values of the generator Jacobian and on mutual information. To upper bound the negative log-likelihood, we consider a gradient penalty-like bound, as well as one based on diffusion processes. In all cases, we provide algorithms for evaluating the bounds. We compare the different bounds to investigate, the pros and cons of the different approaches. Finally, we demonstrate that the use of bidirectional bounds stabilizes EBM training and yields high-quality density estimation and sample generation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bidirectional bounds Density estimation Energy-based model Minimax-training

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