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Abstract 3We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7% (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5 absolute improvement), outperforming human performance by 2.0. (Ming et al. 2018)Reference: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT (1) 2019: 4171-4186.Identify the different strategies that are used in abstract 3.
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Abstract 2This paper presents a consensus-based robust cooperative control framework for a wide class of linear time-invariant (LTI) systems, namely Negative-Imaginary (NI) systems. Output feedback, dynamic, Strictly Negative-Imaginary (SNI) controllers are applied in positive feedback to heterogeneous multiinput–multi-output (MIMO) plants through the network topology to achieve robust output feedback consensus. Robustness to external disturbances and model uncertainty is guaranteed via NI system theory. Cooperative tracking control of networked NI systems is presented as a corollary of the derived results by adapting the proposed consensus algorithm. Numerical examples are also given to demonstrate the effectiveness of proposed robust cooperative control framework. (Wang et al. 2015:64)Reference: Jianan Wang, Alexander Lanzon, Ian R. Petersen Robust cooperative control of multiple heterogeneous Negative-Imaginary systems Automatica 61 (2015) 64–72.Analyze the linguistic features of the above abstract, identify the tenses, voices and statement styles and decide which one of the following underlined parts is not correct. The linguistic features of abstract 2 are mainly the present tense, the active voice, and the impersonal style which can be illustrated by “This paper presents…” in sentence 1, “…are applied…” in sentence 2, “…is guaranteed…” in sentence 3, “…is presented…” in sentence 4 and “…are also given…” in sentence 5 aiming mainly to describe the research objectively.
Abstract 1 Identifying new product opportunities must be a prerequisite for a firm's sustainable growth since it can help create new market segments. In this regard, a number of studies have attempted to suggest systematic methods to discover new technological opportunities. However, from these methods, it is difficult to figure out which products can come into the market as a result of the technological opportunities. Moreover, they have tried to measure generic potential values without considering a specific target firm so it is hard to judge whether the discovered opportunities are technically feasible to the target firm. These problems tend to reduce the practicality of the discovered technological opportunities. Therefore, this paper proposes a systematic approach to identify potential product opportunities by reflecting the target firm's internal capabilities. The capabilities are inherently unobservable so we need to figure out substitutes for the firm's capabilities. The existing products belonging to a firm can be generally a basis for developing new products. The firm is already good at dealing with the existing products so we consider the firm's existing product portfolios its internal capabilities. We first extract product information from patent database using text mining technique, and then generate product connection rules represented as directed pairs of products. Finally, we evaluate potential value of product opportunities taking into account a firm's internal capabilities. An empirical study is conducted to show the applicability of the presented approach using patents granted in the United States Patent and Trademark Office during 2009 and 2013.We expect that our approach can facilitate product-oriented R&D by presenting a front-end model for new product development and deriving feasible product opportunities according to the target firm's internal capabilities. Moreover, the presented systematic approach can be a basis for an R&D planning system that can help R&D planners in performing product-oriented technology planning activities.Reference: Wonchul Seo , Janghyeok Yoon , Hyunseok Park, Byoung-youl Coh , Jae-Min Lee, Oh-Jin Kwond. Product opportunity identification based on internal capabilities using text mining and association rule mining Technological Forecasting & Social Change [J]. 105 (2016) 94–104.Analyze the linguistic features of the above abstract, identify the tenses, voices and statement styles and decide which one of the following underlined parts is not correct. The linguistic features of abstract 1 is mainly the present tense, the active voice, and the impersonal style which can be illustrated by “we need to…” in sentence 7, “we consider…” in sentence 9 and “we first extract…and then generate…” in sentence 10 and “we evaluate…” in sentence 11 and “we expect…” in sentence 13 aiming mainly to highlight the authors’ contribution.
Choose the incorrect one from the underlined words in the following sentence. Participants identified the type of file they chose for each password (e.g., music file, document). These file types were aggregated into categories, as they were listed in Table IV.
Choose the incorrect one from the underlined words in the following sentence.Participants were asked using a different password object file for each website (as commonly advised), although this was not enforced.
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