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Broken Bar Fault Detection Using Taylor–Fourier Filters and Statistical Analysis
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Matter-Aggregating Low-Dimensional Nanostructures at the Edge of the Classical vs. Quantum Realm
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Multi-Qubit Bose–Einstein Condensate Trap for Atomic Boson Sampling
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Information and Agreement in the Reputation Game Simulation
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VeVaPy, a Python Platform for Efficient Verification and Validation of Systems Biology Models with Demonstrations Using Hypothalamic-Pituitary-Adrenal Axis Models
Journal Description
Entropy
Entropy
is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies, published monthly online by MDPI. The International Society for the Study of Information (IS4SI) and Spanish Society of Biomedical Engineering (SEIB) are affiliated with Entropy and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), MathSciNet, Inspec, PubMed, PMC, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Physics, Multidisciplinary) / CiteScore - Q1 (Mathematical Physics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the second half of 2022).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Entropy.
- Companion journals for Entropy include: Foundations, Thermo and MAKE.
Impact Factor:
2.738 (2021);
5-Year Impact Factor:
2.642 (2021)
Latest Articles
Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning
Entropy 2023, 25(2), 299; https://doi.org/10.3390/e25020299 (registering DOI) - 05 Feb 2023
Abstract
This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) formula, a Q-learning
[...] Read more.
This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) formula, a Q-learning function is calculated, and then the iteration IRQL method is developed. In contrast to mechanisms triggered by time, an event-triggered algorithm reduces the rate of transmission and computational load, since the controller may only be upgraded when the predetermined triggering circumstances are met. In addition, in order to implement the suggested system, a neutral reinforce-critic-actor (RCA) network structure is created that may assess the indices of performance and online learning of the event-triggering mechanism. This strategy is intended to be data-driven without having in-depth knowledge of system dynamics. We must develop the event-triggered weight tuning rule, which only modifies the parameters of the actor neutral network (ANN) in response to triggering cases. In addition, a Lyapunov-based convergence study of the reinforce-critic-actor neutral network (NN) is presented. Lastly, an example demonstrates the accessibility and efficiency of the suggested approach.
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(This article belongs to the Section Multidisciplinary Applications)
Open AccessArticle
Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting
Entropy 2023, 25(2), 298; https://doi.org/10.3390/e25020298 (registering DOI) - 05 Feb 2023
Abstract
Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion
[...] Read more.
Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.
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(This article belongs to the Special Issue Information Theory in Computer Vision and Pattern Recognition)
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Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling
Entropy 2023, 25(2), 297; https://doi.org/10.3390/e25020297 (registering DOI) - 04 Feb 2023
Abstract
How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterogeneous Graph Infomax). LHGI adopts the subgraph sampling technology under
[...] Read more.
How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterogeneous Graph Infomax). LHGI adopts the subgraph sampling technology under the guidance of metapaths, which can compress the network and retain the semantic information in the network as much as possible. At the same time, LHGI adopts the idea of contrastive learning, and takes the mutual information between normal/negative node vectors and the global graph vector as the objective function to guide the learning process. By maximizing the mutual information, LHGI solves the problem of how to train the network without supervised information. The experimental results show that, compared with the baseline models, the LHGI model shows a better feature extraction capability both in medium-scale unsupervised heterogeneous networks and in large-scale unsupervised heterogeneous networks. The node vectors generated by the LHGI model achieve better performance in the downstream mining tasks.
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(This article belongs to the Special Issue Information Network Mining and Applications)
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Molten Salt Corrosion Behavior of Dual-Phase High Entropy Alloy for Concentrating Solar Power Systems
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, , , , and
Entropy 2023, 25(2), 296; https://doi.org/10.3390/e25020296 (registering DOI) - 04 Feb 2023
Abstract
Dual-phase high entropy alloys have recently attracted widespread attention as advanced structural materials due to their unique microstructure, excellent mechanical properties, and corrosion resistance. However, their molten salt corrosion behavior has not been reported, which is critical in evaluating their application merit in
[...] Read more.
Dual-phase high entropy alloys have recently attracted widespread attention as advanced structural materials due to their unique microstructure, excellent mechanical properties, and corrosion resistance. However, their molten salt corrosion behavior has not been reported, which is critical in evaluating their application merit in the areas of concentrating solar power and nuclear energy. Here, the molten salt corrosion behavior of AlCoCrFeNi2.1 eutectic high-entropy alloy (EHEA) was evaluated in molten NaCl-KCl-MgCl2 salt at 450 °C and 650 °C in comparison to conventional duplex stainless steel 2205 (DS2205). The EHEA showed a significantly lower corrosion rate of ~1 mm/year at 450 °C compared to ~8 mm/year for DS2205. Similarly, EHEA showed a lower corrosion rate of ~9 mm/year at 650 °C compared to ~20 mm/year for DS2205. There was selective dissolution of the body-centered cubic phase in both the alloys, B2 in AlCoCrFeNi2.1 and α-Ferrite in DS2205. This was attributed to micro-galvanic coupling between the two phases in each alloy that was measured in terms of Volta potential difference using a scanning kelvin probe. Additionally, the work function increased with increasing temperature for AlCoCrFeNi2.1, indicating that the FCC-L12 phase acted as a barrier against further oxidation and protected the underlying BCC-B2 phase with enrichment of noble elements in the protective surface layer.
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(This article belongs to the Special Issue Advances in High-Entropy Alloys)
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A Novel Approach to Parameter Determination of the Continuous Spontaneous Localization Collapse Model
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Entropy 2023, 25(2), 295; https://doi.org/10.3390/e25020295 (registering DOI) - 04 Feb 2023
Abstract
Models of dynamical wave function collapse consistently describe the breakdown of the quantum superposition with the growing mass of the system by introducing non-linear and stochastic modifications to the standard Schrödinger dynamics. Among them, Continuous Spontaneous Localization (CSL) was extensively investigated both theoretically
[...] Read more.
Models of dynamical wave function collapse consistently describe the breakdown of the quantum superposition with the growing mass of the system by introducing non-linear and stochastic modifications to the standard Schrödinger dynamics. Among them, Continuous Spontaneous Localization (CSL) was extensively investigated both theoretically and experimentally. Measurable consequences of the collapse phenomenon depend on different combinations of the phenomenological parameters of the model—the strength and the correlation length —and have led, so far, to the exclusion of regions of the admissible ( ) parameters space. We developed a novel approach to disentangle the and probability density functions, which discloses a more profound statistical insight.
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(This article belongs to the Special Issue Selected Feature Papers from Italian Quantum Information Science Conference 2022)
Open AccessArticle
PBQ-Enhanced QUIC: QUIC with Deep Reinforcement Learning Congestion Control Mechanism
Entropy 2023, 25(2), 294; https://doi.org/10.3390/e25020294 (registering DOI) - 04 Feb 2023
Abstract
Currently, the most widely used protocol for the transportation layer of computer networks for reliable transportation is the Transmission Control Protocol (TCP). However, TCP has some problems such as high handshake delay, head-of-line (HOL) blocking, and so on. To solve these problems, Google
[...] Read more.
Currently, the most widely used protocol for the transportation layer of computer networks for reliable transportation is the Transmission Control Protocol (TCP). However, TCP has some problems such as high handshake delay, head-of-line (HOL) blocking, and so on. To solve these problems, Google proposed the Quick User Datagram Protocol Internet Connection (QUIC) protocol, which supports 0-1 round-trip time (RTT) handshake, a congestion control algorithm configuration in user mode. So far, the QUIC protocol has been integrated with traditional congestion control algorithms, which are not efficient in numerous scenarios. To solve this problem, we propose an efficient congestion control mechanism on the basis of deep reinforcement learning (DRL), i.e., proximal bandwidth-delay quick optimization (PBQ) for QUIC, which combines traditional bottleneck bandwidth and round-trip propagation time (BBR) with proximal policy optimization (PPO). In PBQ, the PPO agent outputs the congestion window (CWnd) and improves itself according to network state, and the BBR specifies the pacing rate of the client. Then, we apply the presented PBQ to QUIC and form a new version of QUIC, i.e., PBQ-enhanced QUIC. The experimental results show that the proposed PBQ-enhanced QUIC achieves much better performance in both throughput and RTT than existing popular versions of QUIC, such as QUIC with Cubic and QUIC with BBR.
Full article
(This article belongs to the Special Issue Entropy in Machine Learning Applications)
Open AccessArticle
Random Walks on Networks with Centrality-Based Stochastic Resetting
Entropy 2023, 25(2), 293; https://doi.org/10.3390/e25020293 (registering DOI) - 04 Feb 2023
Abstract
We introduce a refined way to diffusely explore complex networks with stochastic resetting where the resetting site is derived from node centrality measures. This approach differs from previous ones, since it not only allows the random walker with a certain probability to jump
[...] Read more.
We introduce a refined way to diffusely explore complex networks with stochastic resetting where the resetting site is derived from node centrality measures. This approach differs from previous ones, since it not only allows the random walker with a certain probability to jump from the current node to a deliberately chosen resetting node, rather it enables the walker to jump to the node that can reach all other nodes faster. Following this strategy, we consider the resetting site to be the geometric center, the node that minimizes the average travel time to all the other nodes. Using the established Markov chain theory, we calculate the Global Mean First Passage Time (GMFPT) to determine the search performance of the random walk with resetting for different resetting node candidates individually. Furthermore, we compare which nodes are better resetting node sites by comparing the GMFPT for each node. We study this approach for different topologies of generic and real-life networks. We show that, for directed networks extracted for real-life relationships, this centrality focused resetting can improve the search to a greater extent than for the generated undirected networks. This resetting to the center advocated here can minimize the average travel time to all other nodes in real networks as well. We also present a relationship between the longest shortest path (the diameter), the average node degree and the GMFPT when the starting node is the center. We show that, for undirected scale-free networks, stochastic resetting is effective only for networks that are extremely sparse with tree-like structures as they have larger diameters and smaller average node degrees. For directed networks, the resetting is beneficial even for networks that have loops. The numerical results are confirmed by analytic solutions. Our study demonstrates that the proposed random walk approach with resetting based on centrality measures reduces the memoryless search time for targets in the examined network topologies.
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(This article belongs to the Topic Complex Systems and Network Science)
Open AccessFeature PaperArticle
On the Kaniadakis Distributions Applied in Statistical Physics and Natural Sciences
Entropy 2023, 25(2), 292; https://doi.org/10.3390/e25020292 (registering DOI) - 04 Feb 2023
Abstract
Constitutive relations are fundamental and essential to characterize physical systems. By utilizing the -deformed functions, some constitutive relations are generalized. We here show some applications of the Kaniadakis distributions, based on the inverse hyperbolic sine function, to some topics belonging to the
[...] Read more.
Constitutive relations are fundamental and essential to characterize physical systems. By utilizing the -deformed functions, some constitutive relations are generalized. We here show some applications of the Kaniadakis distributions, based on the inverse hyperbolic sine function, to some topics belonging to the realm of statistical physics and natural science.
Full article
(This article belongs to the Special Issue Twenty Years of Kaniadakis Entropy: Current Trends and Future Perspectives)
Open AccessArticle
Learning Pathways and Students Performance: A Dynamic Complex System
Entropy 2023, 25(2), 291; https://doi.org/10.3390/e25020291 (registering DOI) - 03 Feb 2023
Abstract
In this study, learning pathways are modelled by networks constructed from the log data of student–LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students
[...] Read more.
In this study, learning pathways are modelled by networks constructed from the log data of student–LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students showed a fractal property; meanwhile, the networks of students who failed showed an exponential pattern. This research aims to provide empirical evidence that students’ learning pathways have the properties of emergence and non-additivity from a macro level; meanwhile, equifinality (same end of learning process but different learning pathways) is presented at a micro level. Furthermore, the learning pathways of 422 students enrolled in a blended course are classified according to learning performance. These individual learning pathways are modelled by networks from which the relevant learning activities (nodes) are extracted in a sequence by a fractal-based method. The fractal method reduces the number of nodes to be considered relevant. A deep learning network classifies these sequences of each student into passed or failed. The results show that the accuracy of the prediction of the learning performance was 94%, the area under the receiver operating characteristic curve was 97%, and the Matthews correlation was 88%, showing that deep learning networks can model equifinality in complex systems.
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(This article belongs to the Special Issue Dynamics of Complex Networks)
Open AccessArticle
Can the Sci-Tech Innovation Increase the China’s Green Brands Value?—Evidence from Threshold Effect and Spatial Dubin Model
Entropy 2023, 25(2), 290; https://doi.org/10.3390/e25020290 (registering DOI) - 03 Feb 2023
Abstract
Based on the perspective of the innovation value chain, sci-tech innovation is divided into two stages: R&D and achievement transformation. This paper uses panel data from 25 provinces in China as the sample. We utilize a two-way fixed effect model, spatial Dubin model,
[...] Read more.
Based on the perspective of the innovation value chain, sci-tech innovation is divided into two stages: R&D and achievement transformation. This paper uses panel data from 25 provinces in China as the sample. We utilize a two-way fixed effect model, spatial Dubin model, and panel threshold model to discuss the impact of two-stage innovation efficiency on the value of the green brand, the spatial effect of this impact, and the threshold role of intellectual property protection in the process. The results indicate that: (1) the two stages of innovation efficiency have a positive impact on the value of green brands, and the effect of the eastern region is significantly better than that of the central and western regions. (2) The spatial spillover effect of the two stages of regional innovation efficiency on the value of green brands is evident, especially in the eastern region. (3) The innovation value chain has a pronounced spillover effect. (4) The single threshold effect of intellectual property protection is significant. When the threshold is crossed, the positive impact of the two stages of innovation efficiency on the value of green brands is significantly enhanced. (5) The influence of economic development level, openness, market size, and marketization degree on the value of green brands shows remarkable regional differences. In conclusion, this study contributes to understanding green brands’ growth and provides important implications for developing independent brands in various regions of China.
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(This article belongs to the Special Issue Statistical Physics and Its Applications in Economics and Social Sciences)
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Open AccessCorrection
Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134
Entropy 2023, 25(2), 289; https://doi.org/10.3390/e25020289 (registering DOI) - 03 Feb 2023
Abstract
In the original publication [...]
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Open AccessArticle
Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
Entropy 2023, 25(2), 288; https://doi.org/10.3390/e25020288 (registering DOI) - 03 Feb 2023
Abstract
Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of
[...] Read more.
Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.
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(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)
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Open AccessReview
Quantum Machine Learning: A Review and Case Studies
Entropy 2023, 25(2), 287; https://doi.org/10.3390/e25020287 - 03 Feb 2023
Abstract
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an
[...] Read more.
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist’s perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.
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(This article belongs to the Special Issue Quantum Machine Learning 2022)
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Complexity and Evolution
Entropy 2023, 25(2), 286; https://doi.org/10.3390/e25020286 - 03 Feb 2023
Abstract
Understanding the underlying structure of evolutionary processes is one the most important issues of scientific enquiry of this century [...]
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(This article belongs to the Special Issue Complexity and Evolution)
Open AccessArticle
Diversity-Aware Marine Predators Algorithm for Task Scheduling in Cloud Computing
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Entropy 2023, 25(2), 285; https://doi.org/10.3390/e25020285 - 02 Feb 2023
Abstract
With the increase in cloud users and internet of things (IoT) applications, advanced task scheduling (TS) methods are required to reasonably schedule tasks in cloud computing. This study proposes a diversity-aware marine predators algorithm (DAMPA) for solving TS in cloud computing. In DAMPA,
[...] Read more.
With the increase in cloud users and internet of things (IoT) applications, advanced task scheduling (TS) methods are required to reasonably schedule tasks in cloud computing. This study proposes a diversity-aware marine predators algorithm (DAMPA) for solving TS in cloud computing. In DAMPA, to enhance the premature convergence avoidance ability, the predator crowding degree ranking and comprehensive learning strategies were adopted in the second stage to maintain the population diversity and thereby inhibit premature convergence. Additionally, a stage-independent control of the stepsize-scaling strategy that uses different control parameters in three stages was designed to balance the exploration and exploitation abilities. Two case experiments were conducted to evaluate the proposed algorithm. Compared with the latest algorithm, in the first case, DAMPA reduced the makespan and energy consumption by 21.06% and 23.47% at most, respectively. In the second case, the makespan and energy consumption are reduced by 34.35% and 38.60% on average, respectively. Meanwhile, the algorithm achieved greater throughput in both cases.
Full article
(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)
Open AccessArticle
Efficient Video Watermarking Algorithm Based on Convolutional Neural Networks with Entropy-Based Information Mapper
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Entropy 2023, 25(2), 284; https://doi.org/10.3390/e25020284 - 02 Feb 2023
Abstract
This paper presents a method for the transparent, robust, and highly capacitive watermarking of video signals using an information mapper. The proposed architecture is based on the use of deep neural networks to embed the watermark in the luminance channel in the YUV
[...] Read more.
This paper presents a method for the transparent, robust, and highly capacitive watermarking of video signals using an information mapper. The proposed architecture is based on the use of deep neural networks to embed the watermark in the luminance channel in the YUV color space. An information mapper was used to enable the transformation of a multi-bit binary signature of varying capacitance reflecting the entropy measure of the system into a watermark embedded in the signal frame. To confirm the effectiveness of the method, tests were carried out for video frames with a resolution of 256 × 256 pixels, with a watermark capacity of 4 to 16,384 bits. Transparency metrics (SSIM and PSNR) and a robustness metric—the bit error rate (BER)—were used to assess the performance of the algorithms.
Full article
(This article belongs to the Special Issue Entropy Based Data Hiding and Its Applications)
Open AccessArticle
A Glimpse into Quantum Triplet Structures in Supercritical 3He
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Entropy 2023, 25(2), 283; https://doi.org/10.3390/e25020283 - 02 Feb 2023
Abstract
A methodological study of triplet structures in quantum matter is presented. The focus is on helium-3 under supercritical conditions (4 < T/K < 9; 0.022 < ρNÅ-3 < 0.028), for which strong quantum diffraction effects dominate the behavior. Computational results
[...] Read more.
A methodological study of triplet structures in quantum matter is presented. The focus is on helium-3 under supercritical conditions (4 < T/K < 9; 0.022 < ρNÅ-3 < 0.028), for which strong quantum diffraction effects dominate the behavior. Computational results for the triplet instantaneous structures are reported. Path integral Monte Carlo (PIMC) and several closures are utilized to obtain structure information in the real and the Fourier spaces. PIMC involves the fourth-order propagator and the SAPT2 pair interaction potential. The main triplet closures are: AV3, built as the average of the Kirkwood superposition and the Jackson–Feenberg convolution, and the Barrat–Hansen–Pastore variational approach. The results illustrate the main characteristics of the procedures employed by concentrating on the salient equilateral and isosceles features of the computed structures. Finally, the valuable interpretive role of closures in the triplet context is highlighted.
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(This article belongs to the Special Issue Statistical Mechanics and Thermodynamics of Liquids and Crystals II)
Open AccessArticle
ShrewdAttack: Low Cost High Accuracy Model Extraction
Entropy 2023, 25(2), 282; https://doi.org/10.3390/e25020282 - 02 Feb 2023
Abstract
Machine learning as a service (MLaaS) plays an essential role in the current ecosystem. Enterprises do not need to train models by themselves separately. Instead, they can use well-trained models provided by MLaaS to support business activities. However, such an ecosystem could be
[...] Read more.
Machine learning as a service (MLaaS) plays an essential role in the current ecosystem. Enterprises do not need to train models by themselves separately. Instead, they can use well-trained models provided by MLaaS to support business activities. However, such an ecosystem could be threatened by model extraction attacks—an attacker steals the functionality of a trained model provided by MLaaS and builds a substitute model locally. In this paper, we proposed a model extraction method with low query costs and high accuracy. In particular, we use pre-trained models and task-relevant data to decrease the size of query data. We use instance selection to reduce query samples. In addition, we divided query data into two categories, namely low-confidence data and high-confidence data, to reduce the budget and improve accuracy. We then conducted attacks on two models provided by Microsoft Azure as our experiments. The results show that our scheme achieves high accuracy at low cost, with the substitution models achieving 96.10% and 95.24% substitution while querying only 7.32% and 5.30% of their training data on the two models, respectively. This new attack approach creates additional security challenges for models deployed on cloud platforms. It raises the need for novel mitigation strategies to secure the models. In future work, generative adversarial networks and model inversion attacks can be used to generate more diverse data to be applied to the attacks.
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(This article belongs to the Special Issue Trustworthy AI: Information Theoretic Perspectives)
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Open AccessFeature PaperArticle
Sample, Fuzzy and Distribution Entropies of Heart Rate Variability: What Do They Tell Us on Cardiovascular Complexity?
Entropy 2023, 25(2), 281; https://doi.org/10.3390/e25020281 - 02 Feb 2023
Abstract
Distribution Entropy (DistEn) has been introduced as an alternative to Sample Entropy (SampEn) to assess the heart rate variability (HRV) on much shorter series without the arbitrary definition of distance thresholds. However, DistEn, considered a measure of cardiovascular complexity, differs substantially from SampEn
[...] Read more.
Distribution Entropy (DistEn) has been introduced as an alternative to Sample Entropy (SampEn) to assess the heart rate variability (HRV) on much shorter series without the arbitrary definition of distance thresholds. However, DistEn, considered a measure of cardiovascular complexity, differs substantially from SampEn or Fuzzy Entropy (FuzzyEn), both measures of HRV randomness. This work aims to compare DistEn, SampEn, and FuzzyEn analyzing postural changes (expected to modify the HRV randomness through a sympatho/vagal shift without affecting the cardiovascular complexity) and low-level spinal cord injuries (SCI, whose impaired integrative regulation may alter the system complexity without affecting the HRV spectrum). We recorded RR intervals in able-bodied (AB) and SCI participants in supine and sitting postures, evaluating DistEn, SampEn, and FuzzyEn over 512 beats. The significance of “case” (AB vs. SCI) and “posture” (supine vs. sitting) was assessed by longitudinal analysis. Multiscale DistEn (mDE), SampEn (mSE), and FuzzyEn (mFE) compared postures and cases at each scale between 2 and 20 beats. Unlike SampEn and FuzzyEn, DistEn is affected by the spinal lesion but not by the postural sympatho/vagal shift. The multiscale approach shows differences between AB and SCI sitting participants at the largest mFE scales and between postures in AB participants at the shortest mSE scales. Thus, our results support the hypothesis that DistEn measures cardiovascular complexity while SampEn/FuzzyEn measure HRV randomness, highlighting that together these methods integrate the information each of them provides.
Full article
(This article belongs to the Special Issue Assessing Complexity in Physiological Systems through Biomedical Signals Analysis II)
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Open AccessReview
Contextuality or Nonlocality: What Would John Bell Choose Today?
Entropy 2023, 25(2), 280; https://doi.org/10.3390/e25020280 - 02 Feb 2023
Abstract
A violation of Bell-CHSH inequalities does not justify speculations about quantum non-locality, conspiracy and retro-causation. Such speculations are rooted in a belief that setting dependence of hidden variables in a probabilistic model (called a violation of measurement independence (MI)) would mean a violation
[...] Read more.
A violation of Bell-CHSH inequalities does not justify speculations about quantum non-locality, conspiracy and retro-causation. Such speculations are rooted in a belief that setting dependence of hidden variables in a probabilistic model (called a violation of measurement independence (MI)) would mean a violation of experimenters’ freedom of choice. This belief is unfounded because it is based on a questionable use of Bayes Theorem and on incorrect causal interpretation of conditional probabilities. In Bell-local realistic model, hidden variables describe only photonic beams created by a source, thus they cannot depend on randomly chosen experimental settings. However, if hidden variables describing measuring instruments are correctly incorporated into a contextual probabilistic model a violation of inequalities and an apparent violation of no-signaling reported in Bell tests can be explained without evoking quantum non-locality. Therefore, for us, a violation of Bell-CHSH inequalities proves only that hidden variables have to depend on settings confirming contextual character of quantum observables and an active role played by measuring instruments. Bell thought that he had to choose between non-locality and the violation of experimenters’ freedom of choice. From two bad choices he chose non-locality. Today he would probably choose the violation of MI understood as contextuality.
Full article
(This article belongs to the Special Issue Quantum Information and Probability: From Foundations to Engineering)

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Topics
Topic in
Applied Sciences, Entropy, Sustainability, Electronics, Energies
Artificial Intelligence and Sustainable Energy Systems
Topic Editors: Luis Hernández-Callejo, Sergio Nesmachnow, Sara Gallardo SaavedraDeadline: 28 February 2023
Topic in
Applied Sciences, Electronics, Entropy, Mathematics, Symmetry
Quantum Information and Quantum Computing
Topic Editors: Durdu Guney, David PetrosyanDeadline: 20 March 2023
Topic in
Applied Sciences, BDCC, Mathematics, Electronics, Entropy
Machine and Deep Learning
Topic Editors: Andrea Prati, Luis Javier García Villalba, Vincent A. CicirelloDeadline: 31 March 2023
Topic in
Entropy, Applied Sciences, Healthcare, J. Imaging, Computers, BDCC, AI
Recent Trends in Image Processing and Pattern Recognition
Topic Editors: KC Santosh, Ayush Goyal, Djamila Aouada, Aaisha Makkar, Yao-Yi Chiang, Satish Kumar Singh, Alejandro Rodríguez-GonzálezDeadline: 22 April 2023

Conferences
Special Issues
Special Issue in
Entropy
Statistical Physics of Collective Behavior
Guest Editor: Bryan DanielsDeadline: 15 February 2023
Special Issue in
Entropy
Advances in Information Sciences and Applications
Guest Editor: Jaesung LeeDeadline: 28 February 2023
Special Issue in
Entropy
Lectures on Recent Experimental Achievements in Quantum-Enhanced Technologies
Guest Editors: Valentina Parigi, Fabio Sciarrino, Rosario Lo FrancoDeadline: 20 March 2023
Special Issue in
Entropy
Women’s Special Issue Series: Entropy
Guest Editors: Eun-jin Kim, Amelia Carolina SparavignaDeadline: 31 March 2023
Topical Collections
Topical Collection in
Entropy
Algorithmic Information Dynamics: A Computational Approach to Causality from Cells to Networks
Collection Editors: Hector Zenil, Felipe Abrahão
Topical Collection in
Entropy
Wavelets, Fractals and Information Theory
Collection Editor: Carlo Cattani
Topical Collection in
Entropy
Entropy in Image Analysis
Collection Editor: Amelia Carolina Sparavigna