With the social networks getting increasingly larger, fast community detection algorithms like the label propagation algorithm, are attracting more attention. But the label propagation algorithm deals vertices with no...
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With the rapid development and application of cloud computing, there exist plenty of clouds that are distributed on the open Internet, decentralized in the management, evolving with various services providing diverse ...
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Interconnection network plays an essential role in the architecture of large-scale high performance computing (HPC) systems. In the paper, we construct a novel family of lowdiameter topologies, Galaxyfly, using techni...
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The key escrow problem and high computational cost are the two major problems that hinder the wider adoption of hierarchical identity-based signature (HIBS) scheme. HIBS schemes with either escrow-free (EF) or online/...
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Spin-transfer torque random access memory (STT-RAM) is one of the most promising substitutes for universal main memory and cache due to its excellent scalability, high density and low leakage power. Nevertheless, the ...
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In this paper, we consider a wide class of constrained nonconvex regularized minimization problems, where the constraints are linearly constraints. It was reported in the litera- ture that nonconvex regularization usu...
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High baud rate optical transceiver based on time division multiplexing technology is proposed. A communication channel at 80GBoud with 4 bit streams at 20Gbps is realized by 4-stage cascaded high speed switches with s...
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Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is co...
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Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information ...
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ISBN:
(纸本)9781509006212
Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method. First, ELM, which has randomly chosen weights of hidden and output nodes, is applied to thoroughly extract features from data by generating different numbers of hidden nodes and using different functions. Second, an MKKM algorithm with diversity-induced regularization is utilized to explore the complementary information among kernels constructed from features. The problem could be solved efficiently by alternating optimization. Experimental results demonstrate that the proposed method outperforms state-of-the-art kernel methods.
The global open source software resources have become an Internet-scale repository,which provide abundant resources for software ***,how to locate the desired resource efficiently and accurately from such large amount...
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The global open source software resources have become an Internet-scale repository,which provide abundant resources for software ***,how to locate the desired resource efficiently and accurately from such large amounts is quite a challenge *** of recent works barely focus on the semantic similarity by analyzing the software itself,which often result in inappropriate *** this paper,we propose Dolphin,a global OSS search engine,which leverage the crowd discussions around OSS across communities for optimizing software *** employs the crowd wisdom as an important factor and combines it with the semantic similarity to optimize search results *** implement an OSS search engine based on the proposed approach,which provides online service *** experiments and user studies suggest the effectiveness of our approach.
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