Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-...
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Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bi...
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Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.
Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds...
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Recent advances in wireless sensor networks (WSNs) have brought the sensor based monitoring developments to the surface in many applications. In such a scenario, the security of communication is a major challenge in t...
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Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated me...
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Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication c...
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Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE
CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computationa...
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CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.
Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the desig...
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Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.
In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent C...
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