The traveling salesman problem (TSP) is among the most important combinatorial problems. Ant colony optimization (ACO) algorithm is a recently developed algorithm which has been successfully applied to several NP-hard...
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The traveling salesman problem (TSP) is among the most important combinatorial problems. Ant colony optimization (ACO) algorithm is a recently developed algorithm which has been successfully applied to several NP-hard problems, such as traveling salesman problem, quadratic assignment problem and job-shop problem. Association rule (AR) is the key in knowledge in data mining for finding the best data sequence. A new algorithm which integrates ACO and AR is proposed to solve TSP problems. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, the new algorithm is better than ACO.
In general,Chinese event factuality is determined by the specific vocabularies and syntactic structures of *** Chinese event factuality corpus is to annotate these specific vocabularies and syntactic *** these informa...
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In general,Chinese event factuality is determined by the specific vocabularies and syntactic structures of *** Chinese event factuality corpus is to annotate these specific vocabularies and syntactic *** these information is raw and complex,it will result in high computation complexity and low correct rate if we use these information to compute factuality *** paper proposes a 3D representation of Chinese event factuality based on the annotated factual information in a Chinese event factuality *** also presents the transformation rules between the factual information and 3D representation and those between the 3D representation and event *** experimental results demonstrate the effectiveness of our 3D representation.
To provide cost-effective protection for sensor networks, We introduce an immunization method where the percentage of required vaccinations for immunity are close to the optimal value of a targeted immunization scheme...
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Fountain code is a class of graph-based linear erasure codes, which can effectively solve the problems such as network congestion and feedback cracking for its characteristics of rateless and can resume when interrupt...
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ISBN:
(纸本)9781479973408
Fountain code is a class of graph-based linear erasure codes, which can effectively solve the problems such as network congestion and feedback cracking for its characteristics of rateless and can resume when interrupted, and has a lower complexity of encoding and decoding. However, there are still some problems in the process of encoding and decoding, including the degree distribution structure may be destroyed, parameters of the generating matrix are not fixed, and it cannot recover source datas from the remaining encoded packets with no degree one. So, the basic theory of fountain codes from three aspects are introduced in this paper, i.e., degree distribution, encoding and decoding principles. Therefore the improved algorithms according to the above three aspects are presented. Simulation results show that the proposed algorithm is more efficient than the previous one.
This book constitutes the proceedings of the joint International Conference APWeb/WAIM 2009 which was held in Suzhou, China, during April 1-4, 2009. The 42 full papers presented together with 26 short papers and the a...
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ISBN:
(数字)9783642006722
ISBN:
(纸本)9783642006715
This book constitutes the proceedings of the joint International Conference APWeb/WAIM 2009 which was held in Suzhou, China, during April 1-4, 2009. The 42 full papers presented together with 26 short papers and the abstracts of 2 keynote speeches were carefully reviewed and selected for inclusion in the book. The topics covered are query processing, topic-based techniques, Web data processing, multidimensional data analysis, stream data processing, data mining and its applications, and data management support to advanced applications.
Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic ...
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Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial...
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Online Social Networks (OSNs) are becoming popular and attracting lots of participants. In OSN based e-commerce platforms, a buyer’s review of a product is one of the most important factors for other buyers’ decisio...
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VQ (Learning Vector Quantization) is kind of supervised and competitive learning artificial neural network and adopted in many domains, such as pattern categorization, products classification, mechanical detection. Th...
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Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporatin...
Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporating structural, textual, and visual information. However, two critical challenges remain unresolved: (1) modal heterogeneity, where significant differences in feature distributions across modalities hinder effective fusion; and (2) spatial heterogeneity, where embedding knowledge graphs in a single geometric space fails to capture their complex topological structures. To address these challenges, we propose ChoicE, a unified framework that leverages a mixture of experts (MoE) design for both encoding and decoding. In the encoder, the multimodal chooser preprocesses multiple modalities to derive embedding representations for each modality. These representations are then processed by distinct experts specialized for structural, textual, and visual features, facilitating effective fusion while preserving modality-specific information. In the decoder, the geometric chooser projects the unified multimodal embeddings into Euclidean, complex, or hyperbolic space, dynamically selecting the most appropriate space to model the inference patterns inherent to each query. Extensive experiments on multiple benchmark datasets demonstrate that ChoicE effectively overcomes these dilemmas and achieves state-of-the-art performance in multimodal KGC. The data and code are released at https://***/r/ChiocE-master/ .
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