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Entity tasks, such as linking, integration, and translation, are crucial for many search and NLP applications. For this purposed entity graphs have been manually built or automatically harvested. In this paper, we survey existing approaches abstracting these problems into a graph-based iterative matching on a pair of entity graphs.
Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial...
In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or rank items precisely. Recently, transfer learning techniques...
With the complexity big data platform architectures, data engineer provides the infrastructure with computation and storage resource for data scientist and data analyst. With those supports, data scientists can focus their domain problem and design the intelligence module (e.g., prepare the data, select/train/tune the machine learning modules and validate the result). However, there is still a gap...
In response to globalization, International Financial Reporting Standards (IFRS) has become the norm of the global capital markets. Companies preparing financial statements using IFRS may make the financial situation fully disclosed. Nevertheless, an overestimated accrual expense of a balance sheet may not only underestimate the earnings data, but also increase the cash outflows of the statement of...
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