Entropy最新文献 - Book学术

Goalie: Defending Against Correlated Value and Sign Encoding Attacks.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-20 DOI: 10.3390/e27030323

Rongfei Zhuang, Ximing Fu, Chuanyi Liu, Peiyi Han, Shaoming Duan

{"title":"Goalie: Defending Against Correlated Value and Sign Encoding Attacks.","authors":"Rongfei Zhuang, Ximing Fu, Chuanyi Liu, Peiyi Han, Shaoming Duan","doi":"10.3390/e27030323","DOIUrl":"10.3390/e27030323","url":null,"abstract":"In this paper, we propose a method, namely Goalie, to defend against the correlated value and sign encoding attacks used to steal shared data from data trusts. Existing methods prevent these attacks by perturbing model parameters, gradients, or training data while significantly degrading model performance. To guarantee the performance of the benign models, Goalie detects the malicious models and stops their training. The key insight of detection is that encoding additional information in model parameters through regularization terms changes the parameter distributions. Our theoretical analysis suggests that the regularization terms lead to the differences in parameter distributions between benign and malicious models. According to the analysis, Goalie extracts features from the parameters in the early training epochs of the models and uses these features to detect malicious models. The experimental results show the high effectiveness and efficiency of Goalie. The accuracy of Goalie in detecting the models with one regularization term is more than 0.9, and Goalie has high performance in some extreme situations. Meanwhile, Goalie takes only 1.1 ms to detect a model using the features extracted from the first 30 training epochs.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Scheduling of Energy Systems for Gas-to-Methanol Processes Using Operating Zone Models and Entropy Weights.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-20 DOI: 10.3390/e27030324

Xueteng Wang, Mengyao Wei, Jiandong Wang, Yang Yue

{"title":"Optimal Scheduling of Energy Systems for Gas-to-Methanol Processes Using Operating Zone Models and Entropy Weights.","authors":"Xueteng Wang, Mengyao Wei, Jiandong Wang, Yang Yue","doi":"10.3390/e27030324","DOIUrl":"10.3390/e27030324","url":null,"abstract":"In coal chemical industries, the optimal allocation of gas and steam is crucial for enhancing production efficiency and maximizing economic returns. This paper proposes an optimal scheduling method using operating zone models and entropy weights for an energy system in a gas-to-methanol process. The first step is to develop mechanistic models for the main facilities in methanol production, namely desulfurization, air separation, syngas compressors, and steam boilers. A genetic algorithm is employed to estimate the unknown parameters of the models. These models are grounded in physical mechanisms such as energy conservation, mass conservation, and thermodynamic laws. A multi-objective optimization problem is formulated, with the objectives of minimizing gas loss, steam loss, and operating costs. The required operating constraints include equipment capacities, energy balance, and energy coupling relationships. The entropy weights are then employed to convert this problem into a single-objective optimization problem. The second step is to solve the optimization problem based on an operating zone model, which describes a high-dimensional geometric space consisting of all steady-state data points that satisfy the operation constraints. By projecting the operating zone model on the decision variable plane, an optimal scheduling solution is obtained in a visual manner with contour lines and auxiliary lines. Case studies based on Aspen Hysys are used to support and validate the effectiveness of the proposed method.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-20 DOI: 10.3390/e27030322

Xin Xu, Xinya Lu, Jianan Wang

{"title":"DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification.","authors":"Xin Xu, Xinya Lu, Jianan Wang","doi":"10.3390/e27030322","DOIUrl":"10.3390/e27030322","url":null,"abstract":"This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a novel distance metric. Finally, a weighted aggregation operator fuses these representations into a unified low-dimensional space. By bridging the gap between random-walk-based and neural-network-based techniques, our framework enhances representation quality and improves classification accuracy. Extensive evaluations on real-world networks demonstrate that DeeWaNA outperforms four widely used unsupervised network representation learning methods, underscoring its effectiveness and broader applicability.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distribution Approach to Local Volatility for European Options in the Merton Model with Stochastic Interest Rates.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-19 DOI: 10.3390/e27030320

Piotr Nowak, Dariusz Gatarek

引用次数: 0

Generative Large Model-Driven Methodology for Color Matching and Shape Design in IP Products.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-19 DOI: 10.3390/e27030319

Fan Wu, Peng Lu, Shih-Wen Hsiao

{"title":"Generative Large Model-Driven Methodology for Color Matching and Shape Design in IP Products.","authors":"Fan Wu, Peng Lu, Shih-Wen Hsiao","doi":"10.3390/e27030319","DOIUrl":"10.3390/e27030319","url":null,"abstract":"The rise in generative large models has gradually influenced traditional product design processes, with AI-generated content (AIGC) playing an increasingly significant role. Globally, tourism IP cultural products are crucial for promoting sustainable tourism development. However, there is a lack of practical design methodologies incorporating generative large models for tourism IP cultural products. Therefore, this study proposes a methodology for the color matching and shape design of tourism IP cultural products using multimodal generative large models. The process includes four phases, as follows: (1) GPT-4o is used to explore visitors' emotional needs and identify target imagery; (2) Midjourney generates shape options that align with the target imagery, and the optimal shape is selected through quadratic curvature entropy method based on shape curves; (3) Midjourney generates colored images reflecting the target imagery, and representative colors are selected using AHP and OpenCV; and (4) color harmony calculations are used to identify the best color combination. These alternatives are evaluated quantitatively and qualitatively using a color-matching aesthetic measurement formula and a sensibility questionnaire. The effectiveness of the methodology is demonstrated through a case study on the harbor seal, showing a strong correlation between quantitative and qualitative evaluations, confirming its effectiveness in tourism IP product design.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy of Volatility Changes: Novel Method for Assessment of Regularity in Volatility Time Series.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-19 DOI: 10.3390/e27030318

Joanna Olbryś

{"title":"Entropy of Volatility Changes: Novel Method for Assessment of Regularity in Volatility Time Series.","authors":"Joanna Olbryś","doi":"10.3390/e27030318","DOIUrl":"10.3390/e27030318","url":null,"abstract":"The goal of this research is to introduce and thoroughly investigate a new methodology for the assessment of sequential regularity in volatility time series. Three volatility estimators based on daily range data are analyzed: (1) the Parkinson estimator, (2) the Garman-Klass estimator, and (3) the Rogers-Satchell estimator. To measure the level of complexity of time series, the modified Shannon entropy based on symbol-sequence histograms is utilized. Discretization of the time series of volatility changes into a sequence of symbols is performed using a novel encoding procedure with two thresholds. Five main stock market indexes are analyzed. The whole sample covers the period from January 2017 to December 2023 (seven years). To check the robustness of our empirical findings, two sub-samples of equal length are investigated: (1) the pre-COVID-19 period from January 2017 to February 2020 and (2) the COVID-19 pandemic period from March 2020 to April 2023. An additional formal statistical analysis of the symbol-sequence histograms is conducted. The empirical results for all volatility estimators and stock market indexes are homogeneous and confirm that the level of regularity (in terms of sequential patterns) in the time series of daily volatility changes is high, independently of the choice of sample period. These results are important for academics and practitioners since the existence of regularity in the time series of volatility changes implies the possibility of volatility prediction.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weibull-Type Incubation Period and Time of Exposure Using γ-Divergence.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-19 DOI: 10.3390/e27030321

Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi

{"title":"Weibull-Type Incubation Period and Time of Exposure Using γ-Divergence.","authors":"Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi","doi":"10.3390/e27030321","DOIUrl":"10.3390/e27030321","url":null,"abstract":"Accurately determining the exposure time to an infectious pathogen, together with the corresponding incubation period, is vital for identifying infection sources and implementing targeted public health interventions. However, real-world outbreak data often include outliers-namely, tertiary or subsequent infection cases not directly linked to the initial source-that complicate the estimation of exposure time. To address this challenge, we introduce a robust estimation framework based on a three-parameter Weibull distribution in which the location parameter naturally corresponds to the unknown exposure time. Our method employs a γ-divergence criterion-a robust generalization of the standard cross-entropy criterion-optimized via a tailored majorization-minimization (MM) algorithm designed to guarantee a monotonic decrease in the objective function despite the non-convexity typically present in robust formulations. Extensive Monte Carlo simulations demonstrate that our approach outperforms conventional estimation methods in terms of bias and mean squared error as well as in estimating the incubation period. Moreover, applications to real-world surveillance data on COVID-19 illustrate the practical advantages of the proposed method. These findings highlight the method's robustness and efficiency in scenarios where data contamination from secondary or tertiary infections is common, showing its potential value for early outbreak detection and rapid epidemiological response.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Resistance Coefficients for Heat Conduction in Anisotropic Bodies at the Limit of Linear Extended Thermodynamics.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-18 DOI: 10.3390/e27030314

Devyani Thapliyal, Raj Kumar Arya, Dimitris S Achilias, George D Verros

引用次数: 0

What Is Ontic and What Is Epistemic in the Quantum Mechanics of Spin?

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-18 DOI: 10.3390/e27030315

Ariel Caticha

引用次数: 0

Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN-TCN Architecture with Integrated Entropy Regularization and Pooling.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-03-18 DOI: 10.3390/e27030317

Jiangang Chen, Junbo Han, Pei Su, Gaoquan Zhou

{"title":"Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN-TCN Architecture with Integrated Entropy Regularization and Pooling.","authors":"Jiangang Chen, Junbo Han, Pei Su, Gaoquan Zhou","doi":"10.3390/e27030317","DOIUrl":"10.3390/e27030317","url":null,"abstract":"Groove, a complex aspect of music perception, plays a crucial role in eliciting emotional and physical responses from listeners. However, accurately quantifying and predicting groove remains challenging due to its intricate acoustic features. To address this, we propose a novel framework for groove rating that integrates Convolutional Neural Networks (CNNs) with Temporal Convolutional Networks (TCNs), enhanced by entropy regularization and entropy-pooling techniques. Our approach processes audio files into Mel-spectrograms, which are analyzed by a CNN for feature extraction and by a TCN to capture long-range temporal dependencies, enabling precise groove-level prediction. Experimental results show that our CNN-TCN framework significantly outperforms benchmark methods in predictive accuracy. The integration of entropy pooling and regularization is critical, with their omission leading to notable reductions in R2 values. Our method also surpasses the performance of CNN and other machine-learning models, including long short-term memory (LSTM) networks and support vector machine (SVM) variants. This study establishes a strong foundation for the automated assessment of musical groove, with potential applications in music education, therapy, and composition. Future research will focus on expanding the dataset, enhancing model generalization, and exploring additional machine-learning techniques to further elucidate the factors influencing groove perception.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0