Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA t...
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With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags...
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Mining interesting patterns in data streams has attracted special attention recently. This study revealed the principles behind observations, through variation of intervention events to analyze the trends in the data ...
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A challenging issue in fast-evolving pure P2P networks is the design of an appropriate mechanism for processing queries. Since both the data content of the peers as well as their acquaintances change rapidly the typic...
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ISBN:
(纸本)1581138644
A challenging issue in fast-evolving pure P2P networks is the design of an appropriate mechanism for processing queries. Since both the data content of the peers as well as their acquaintances change rapidly the typical P2P querying techniques become inappropriate. In this dynamic context the usage of a Mobile Agent framework appears very promising. The paper investigates the issues related to the above problem and proposes a P2P and Mobile Agent architecture based on Active database technology. We argue that, the employment of ECA rules both for answering queries and deploying agents leads to an efficient as well as simple query processing technique. Furthermore, the proposed mobile agent system architecture offers a number of advantages due to the performance and scalability that can be achieved using Active databases.
With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags...
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With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags is avid requirement for Web2.0 application. However, no proper algorithm can tackle this task very well. This paper proposes a core-tag oriented clustering method to handle the task. The main contributions include: (1) Proposing the concept of core-tag oriented space; (2) Proposing a method called Core-Tag oriented Spectral Clustering (CTSC) to cluster tags in the new space; (3) Designing experiments to evaluate the algorithm, and the results show that CTSC algorithm performs well on clustering tags.
Multi-dimensional major medicines analysis is one of the most important tasks in the data analysis of Traditional Chinese Medicine (TCM) prescriptions. In this paper, an effective method is proposed to mine multi-dime...
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Multi-dimensional major medicines analysis is one of the most important tasks in the data analysis of Traditional Chinese Medicine (TCM) prescriptions. In this paper, an effective method is proposed to mine multi-dimensional major medicines from TCM prescriptions. The main contributions include: (1) proposing the concept of multi-dimensional major medicines, (2) borrowing the concept of multidimensional frequent patterns and improving the approach of Multi-dimensional Index Tree, (3) applying it in the TCM major medicines mining, (4) implementing the algorithms in the major medicines discovery module of TCMiner 1.0. (5) Extensive experiments show the effectiveness and efficiency of the proposed approach.
Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension ...
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