• Journal of Korea Technology Innovation Society
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• v.15 no.2
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• pp.262-276
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• 2012

Transactions activation of intangible assets has become became a key factor for corporate survival and successful business in the knowledge economy. Thus, demand in correctly valuating technology has increased. The market approach is the reliable method because of the premise that the market value of an asset is directly related to the prices of comparable, competitive ones. However, it can be practically impossible in many cases because it is hard to find identical transactions, which are generally closely guarded business secrets. As a result, most of technology valuation is conducted by the income approach and this approach is used to derive estimates for such unobserved variables. In September 2011, the Ministry of Knowledge Economy enacted a law for operational guideline standards for technology valuation to encourage the domestic technology valuation market. The enforcement recommended that the market approach have precedence over other approaches. If this approach cannot be applied, then the valuator should writing that he used other approaches. In practice, it is hard to know whether or not information about comparable transactions exists. The proposed process provides valuators to assess suitability for practical use of market approach through the observation process of technology transactions information. At the same time, it offers them the opportunity to gain validity when using other approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.