Traditional POI recommendation systems use a centralized data storage approach to train models, posing significant risks of privacy breaches. Federated learning offers an effective solution to address user privacy con...
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Traditional POI recommendation systems use a centralized data storage approach to train models, posing significant risks of privacy breaches. Federated learning offers an effective solution to address user privacy concerns. However, in existing federated learning setups, client data remains isolated from each other, making it challenging to achieve cross -client collaborative training and severely limiting the performance of POI models. Additionally, the sparsity of local client data makes it difficult for local models to effectively learn local personalized knowledge, and low -quality local models further degrade the performance of the global model. Furthermore, the potential of semantic information in representing deep user behavior characteristics hasn't been fully explored in federated POI recommendation. Therefore, this paper proposes a semantic -based federated learning method (SFL), introducing edge devices to facilitate cross -client personalized knowledge collaboration. We design a semantic -based collaborative optimization strategy to learn and utilize semantic information from client trajectories without sensitive data, guiding edge devices to mine shared user knowledge for achieving knowledge collaboration among similar clients. Simultaneously, the semantic information from client trajectories is utilized to enhance local data, thereby improving the personalized capabilities of local models. Extensive experiments on public datasets demonstrate that SFL outperforms several strong baselines in terms of performance.
Video-based physiology, exemplified by remote photoplethysmography (rPPG), extracts physiological signals such as pulse and respiration by analyzing subtle changes in video recordings. This non-contact, real-time moni...
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As one of the most popular machine learning methods, random forests have been successfully applied to different data analysis tasks such as classification, regression and cluster analysis. Recently, the random forest ...
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As one of the most popular machine learning methods, random forests have been successfully applied to different data analysis tasks such as classification, regression and cluster analysis. Recently, the random forest clustering method has received much attention due to its simplicity, accuracy and robustness. However, we cannot directly employ the random forest clustering algorithm to solve the discrete sequence clustering problem because of the lack of explicit features and "negative"sequences. In this paper, we propose a new random forest clustering algorithm for discrete sequences. The proposed method firstly injects a set of decoy sequences and then constructs the random forest in a supervised and adaptive manner by generating features on the fly. Experimental results on real data sets show that the proposed method can achieve better performance than those state-of-the-art discrete sequence clustering algorithms.
The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolut...
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Industrial networks are vulnerable to various cyber threats that can compromise their Confidentiality, Integrity, and Availability (CIA). To counter the increasing frequency of such threats, we designed and developed ...
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Industrial networks are vulnerable to various cyber threats that can compromise their Confidentiality, Integrity, and Availability (CIA). To counter the increasing frequency of such threats, we designed and developed an Explainable Artificial Intelligence (XAI) integrated Deep Learning (DL)-based threat detection system (XDLTDS). We first employ a Long-Short Term Memory-AutoEncoder (LSTM-AE) to encode IIoT data and mitigate inference attacks. Then, we introduce an Attention-based Gated Recurrent Unit (AGRU) with softmax for multiclass threat classification in IIoT networks. To address the black-box nature of DL-based IDS, we use the Shapley Additive Explanations (SHAP) mechanism to provide transparency and trust for the system's decisions. This interpretation helps SOC analysts understand why specific events are flagged as malicious by the XDLTDS framework. Our approach reduces the risk of sensitive data and reputation loss. We also present a software-Defined Networking (SDN)-based deployment architecture for the XDLTDS framework. Extensive experiments with the N-BaIoT, Edge-IIoTset, and CIC-IDS2017 datasets confirm the effectiveness of XDLTDS against existing frameworks in addressing modern cybersecurity challenges and protecting industrial networks.
Aiming at the existing problems of metric-based methods, there are problems such as inadequate feature extraction, inaccurate class feature representation, and single similarity measurement. A new model based on atten...
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The advancements in technology and widespread internet access have revolutionized the way data collection and polling are conducted. Traditional polling methods are often hampered by significant challenges, including ...
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Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various paramet...
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Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant "semantic gap" problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module.& COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Topology optimization requires dozens or even hundreds of iterations, each requiring a complete finite element analysis (FEA). Significant computation cost limits the application of topology optimization in engineerin...
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Open relation extraction (ORE) aims to assign semantic relationships among arguments, essential to the automatic construction of knowledge graphs (KG). The previous ORE methods and some benchmark datasets consider a r...
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