Inner Product Functional Encryption (IPFE) offers strong privacy protection for smart devices by outputting only the results of function computations, minimizing data leakage. This makes it well-suited for privacy-pre...
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Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics s...
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Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.
With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and ***...
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With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and ***,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their *** address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree *** algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical *** our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical *** results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.
Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emo...
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Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from bigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user's unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.
This study aims to improve the accuracy of click-through rate prediction for push ads through machine learning methods. Using the dataset released by Tianchi, we synthesized basic user information, ad features and use...
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Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential f...
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Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain ***,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural *** address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity *** particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear *** conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting *** code is available at .
In this paper, under the condition that the Choquet expectations exist, we study the complete moment convergence for weighted sums of arrays of rowwise negatively dependent random variables in sublinear expectation sp...
In this paper, under the condition that the Choquet expectations exist, we study the complete moment convergence for weighted sums of arrays of rowwise negatively dependent random variables in sublinear expectation space (?, H,ê). Some general results on complete moment convergence for weighted sums of arrays of rowwise negatively dependent random variables under sub-linear expectations are established, which extend and improve some previous known ones.
Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to var...
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Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection *** this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized *** considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per *** is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the *** evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering *** results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art *** study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.
The discourse surrounding energy transition is intensifying due to insufficient global energy resources. Nonetheless, given that the capitalist economy prioritizes expansion and growth, it remains uncertain whether gr...
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While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some e...
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