Speech emotion recognition (SER) is one of the most important and active areas of. research in speech processing. Numerous approaches have been proposed to address various limitations in this field, but the sheer dive...
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The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by t...
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The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.
As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
Nowadays, Virtual and Augmented Reality technologies play a supportive role in many research fields. In cultural heritage, various examples are available, including storytelling and narratives, where they can provide ...
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We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum featu...
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We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.
Graph Neural Networks (GNNs) have emerged as a widely used and effective method across various domains for learning from graph data. Despite the abundance of GNN variants, many struggle with effectively propagating me...
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Fairness is not foreign to competition law and fairness considerations are not new to it. However, the endemic uncertainty on its notion has traditionally made fairness unsuitable to act as a stand-alone applicable le...
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Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both th...
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Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya
In addressing the limitations of traditional short text similarity calculation methods, this paper presents SMSABLC, a deep learning-based approach that takes into account polysemy, character order, and contextual sem...
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The recently developed relativistic-mean-field in complex momentum representation with the functional NL3^(*)was used to explore the exotic properties of neutron-rich Pd,Cd,Te,and Xe *** results were compared with tho...
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The recently developed relativistic-mean-field in complex momentum representation with the functional NL3^(*)was used to explore the exotic properties of neutron-rich Pd,Cd,Te,and Xe *** results were compared with those obtained using the relativistic Hartree-Bogoliubov(RHB)calculations and available experimental *** single-particle levels were obtained for the bound and resonant *** two neutron separation energies and root mean square(rms)radii agree with the experimental *** is shown that there is a halo structure in extremely neutron-rich^(164-180)Te and^(164-182))Xe,,as well as a thick neutron skin in extremely neutron-rich Pd and Cd *** the numbers of neutrons(N_(λ))Te and(N_(0)),occupying the levels above the Fermi surface and zero-potential energy level,it was found that pairing correlations play an important role in the formation of halo *** findings are further supported by investigating S_(2n),rms radii,occupation probabilities,contributions of single-particle levels to the neutron rms radii,and density *** neutron rms radii increased sharply,evidently deviating from the traditional rule r■N^(1/3),and the density distributions were very ***,the contributions of different single-particle levels to the total neutron density and wavefunction are *** was found that the sudden increase in the neutron rms radii and diffuse density distributions mainly arise from the resonant levels with a lower orbital angular momentum near the continuum threshold.
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