Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Pa...
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Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging *** innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed *** combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network *** cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection *** seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,*** results demonstrate the advantage of the proposed work over cutting-edge techniques.
With the development of cyber-physical systems,system security faces more risks from cyber-attacks. In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resour...
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With the development of cyber-physical systems,system security faces more risks from cyber-attacks. In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints(the total resource consumption of the attacks is not greater than a given initial resource of the attacker) to mislead a discrete event system under supervisory control to reach unsafe states. We consider that the attacker can implement two types of attacks: One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor. Each attack has its corresponding resource consumption and remains covert. To solve this problem, we first introduce a notion of combined-attackability to determine whether a closedloop system may reach an unsafe state after receiving attacks with resource constraints. We develop an algorithm to construct a corrupted supervisor under attacks, provide a verification method for combined-attackability in polynomial time based on a plant, a corrupted supervisor, and an attacker's initial resource, and propose a corresponding attack synthesis algorithm. The effectiveness of the proposed method is illustrated by an example.
High entropy compounds were proven to exhibit excellent catalytic ***,a series of amorphous CrMnFeCoNi Oxy-carbide films were successfully synthesized by one-step *** demonstrated,the film presented superior electroca...
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High entropy compounds were proven to exhibit excellent catalytic ***,a series of amorphous CrMnFeCoNi Oxy-carbide films were successfully synthesized by one-step *** demonstrated,the film presented superior electrocatalytic activity for oxygen evolution reaction(OER)with an overpotential of 295 mV at a current density of 10 mA/cm^(2).Uniquely,selective dissolution of Chromium(Cr)was observed,which increased the catalytic activity and showed high stability under a large current density of up to 400 mA/cm^(2).Cr dissolution not only increased the surface area but also improved the conductivity due to newly formed metal-metal bonding,promoting electron transfer and improving OER *** revealed by density functional theory(DFT)calculations,Cr-dissolution mediates the bonding of OER intermediates over surface active sites and ultimately reduces OER *** one-step electrodeposition method and the micro-dissolution mechanism provided a potential way to design and prepare high entropy compound electrodes,aiming to achieve efficient water electrolysis.
Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency respo...
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In this paper we provided an insightful exploration into the critical role of feature matching in enhancing the efficacy of e-commerce recommendation systems. By meticulously analyzing user data and product characteri...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to i...
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software *** feeds potentially syntactically or semantically malformed test data to a target program to mi...
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As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software *** feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the *** recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the *** paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its ***,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.
The chemical equilibrium equations utilized in reactive transport modeling are complex and nonlinear,and are typically solved using the Newton-Raphson *** this algorithm is known for its quadratic convergence near the...
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The chemical equilibrium equations utilized in reactive transport modeling are complex and nonlinear,and are typically solved using the Newton-Raphson *** this algorithm is known for its quadratic convergence near the solution,it is less effective far from the solution,especially for ill-conditioned *** such cases,the algorithm may fail to converge or require excessive *** address these limitations,a projected Newton method is introduced to incorporate the concept of *** method constrains the Newton step by utilizing a chemically allowed interval that generates feasible descending ***,we utilize the positive continuous fraction method as a preconditioning technique,providing reliable initial values for solving the *** numerical results are compared with those derived using the regular Newton-Raphson method,the Newton-Raphson method based on chemically allowed interval updating rules,and the bounded variable least squares method in six different test *** numerical results highlight the robustness and efficacy of the proposed algorithm.
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