The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candida...
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Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Intern...
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Grid-based simulations of hot fusion plasmas are often severely limited by computational and memory resources;the grids live in four- to six-dimensional space and thus suffer the curse of dimensionality. However, high...
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Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances i...
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The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerfu...
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The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candida...
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parallel algorithms relying on synchronous parallelization libraries often experience adverse performance due to global synchronization barriers. Asynchronous many-task runtimes offer task futurization capabilities th...
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With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support th...
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ISBN:
(纸本)9798350337488
With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t
Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the del...
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In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorith...
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ISBN:
(数字)9798350385557
ISBN:
(纸本)9798350385564
In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.
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