In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video ca...
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Reinforcement learning (RL)-based multi-hop knowledge graph reasoning has achieved remarkable success in real-world applications and can effectively handle knowledge graph completion tasks. This approach involves a po...
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Reinforcement learning (RL)-based multi-hop knowledge graph reasoning has achieved remarkable success in real-world applications and can effectively handle knowledge graph completion tasks. This approach involves a policy-based agent navigating the graph environment to extend reasoning paths and identify the target entity. However, most existing multi-hop reasoning models are typically constrained to stepwise inference, which inherently disrupts the global information of multi-hop paths. To overcome this limitation, we introduce discriminative features between valid and invalid paths as global information. Here, we propose a multi-hop path encoder specifically designed to extract these discriminative features. Firstly, a multi-hop path encoding module is employed to derive each path's hidden features, using cross-attention mechanisms to strengthen the interaction between triple context and path features. Secondly, a discriminative feature extraction module is used to capture the differences between valid and invalid paths. Thirdly, an attention-enhanced gated fusion mechanism is implemented to integrate these discriminative features into the multi-hop inference decoder. We further evaluate our method on five standard datasets. Our method outperforms the baseline models, demonstrating the effectiveness of discriminative features in improving prediction performance, learning speed, and path interpretability.
Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some ...
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Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mech...
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Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical const...
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Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.
In hyperspectral remote sensing imagery, pixel interactions within defined spatial extents result in the mixing of adjacent pixels. Additionally, the high similarity of adjacent spectra leads to information redundancy...
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The maritime industry is undergoing a major transformation to achieve reduction of greenhouse gas emissions. Many new options, such as alternative propulsion systems and fuels, optimized routes, or auxiliary propulsio...
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Recent research highlights the advantages of leveraging complementary strengths of both human expert and model in decision-making processes. Learning to Defer(L2D) is proposed to build a system consisting of both and ...
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In the realm of medical imaging, a scarcity of reliable, sizable datasets for training supervised deep learning models persists. One solution involves leveraging Generative Adversarial Networks (GANs) to fabricate syn...
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Simulink has been widely used in embedded software development, which supports simulation to validate the correctness of the constructed models. However, as the scale and complexity of models in industrial application...
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