Rui Hua^1,2, Yongwen Bao³, Shengan Chen², Ziyin Zhuang^1,*

Computer Systems Science and Engineering, Vol.34, No.4, pp. 215-223, 2019, DOI:10.32604/csse.2019.34.215

Abstract This paper improves the dynamic Feder model based on the characteristics of knowledge production and separates the direct effect and spillover effect of R&D in order to determine the relationship between spillover effect of R&D and economic growth, and accurately measure it by examining Chinese provincial panel data from 2008–2016. The theoretical analysis shows that the spillover effect of R&D promotes economic growth. Empirical analysis using a combination of OLS, sysGMM, 2SLS and GLS shows that basic research and application research have significant spillover effects; the marginal revenue of the basic research is lower than that of the production sector,… More >

Xu Xiaoli^1,∗, Li Dong¹, Jiang Zhanglei¹, Li Ning¹, Wu Guoxin¹, Wang Hongjun¹, Zhang Xu², Bai Feng²

Computer Systems Science and Engineering, Vol.34, No.4, pp. 191-199, 2019, DOI:10.32604/csse.2019.34.191

Abstract With the aim of protecting, bequeathing, and sharing globally the Dongba manuscripts of the Chinese Naxi minority, the memory and heritage of which is under threat, this paper proposes ideas and plans for building a digital sharing platform to fulfil this aim using computer technology, information processing, online dissemination, multimedia display and other technologies to build an international digital platform for the sharing of Dongba manuscripts. This platform provides digital resources comprising Dongba manuscripts and related literature, tools for deciphering Dongba manuscripts, an environment for undertaking and sharing research, and dynamic information on the research findings and inheritance. The platform… More >

Mucheol Kim^*, Junho Kim, Mincheol Shin

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 141-147, 2020, DOI:10.31209/2019.100000135

Abstract With the trends of big data era, many people want to acquire the reliable and refined information from web environments. However, it is difficult to find appropriate information because the volume and complexity of web information is increasing rapidly. So many researchers are focused on text mining and personalized recommendation for extracting users’ interests. The proposed approach extracted semantic relationship between scientific terminologies with word embedding approach. We aggregated science data in BT for supporting users’ wellness. In our experiments, query expansion is performed with relationship between scientific terminologies with user’s intention. More >

Isma Farah Siddiqui¹, Scott Uk-Jin Lee^2,*, Asad Abbas³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 107-119, 2020, DOI:10.31209/2019.100000132

Abstract The issue of battery drainage in the gigantic smart meters network such as semantic-aware IoT-enabled smart meter has become a serious concern in the smart grid framework. The grid core migrates existing tabular datasets i.e., Relational data to semantic-aware tuples in its Resource Description Framework (RDF) format, for effective integration among multiple components to work aligned with IoT. For this purpose, WWW Consortium (W3C) recommends two specifications as mapping languages. However, both specifications use entire RDB schema to generate data transformation mapping patterns and results large quantity of unnecessary transformation. As a result, smart meters use huge computing resources, maximum… More >

Tao Li, Hao Li^*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu

Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767

Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, and the items are sorted… More >

K. Donald Tham^a, Asad M. Madni^b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 17-26, 2018, DOI:10.1080/10798587.2017.1284411

Abstract The Sarbanes-Oxley (SOX) Act 2002 resulted from the mounting accounting and corporate scandals in the late 1990s and early 2000s. Since the passage of the SOX Act, companies are facing even greater challenges to meet raised expectations to provide accurate, visible, and timely information for SOX compliance. This research puts forth a systems design framework to achieve a real time, accurate, consistently traceable and easily verifiable SOX compliant technology. Our multidisciplinary and integrative systems design incorporates Overall Equipment Effectiveness (OEE) to ensure effective business performance within a knowledge represented company modeled as Enveloped Activity Based Enterprise Model (EABEM) that facilitates… More >

Yefeng Ma^a, Hui Zhang^b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 107-114, 2018, DOI:10.1080/10798587.2016.1267249

Abstract Emerging communication and computing technologies such as social media, Internet of Things and big data provide great opportunities to improve information management systems for emergency operations. This paper studies the issues of information management at China’s Emergency Operations Center (EOC), and proposes a data-driven knowledge management system (KMS) to support decisionmaking, coordination, and collaboration within EOCs and with the public. In the proposed KMS, big data analytics is employed to gather and analyze information from different knowledge domains and track how a crisis evolves in physical world and in cyber space. The proposed system aims at improving situation awareness of… More >

Zhang Min, Teng Haibin, Jiang Ming, Wen Tao, Tang Jingfan

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 625-635, 2019, DOI:10.31209/2019.100000117

Abstract Mapping from sentence phrases to knowledge graph resources is an important step for applications such as search engines, automatic question answering systems based on acknowledge base and knowledge graphs. The existing solution maps a simple phrase to a knowledge graph resource strictly or approximately from the text. However, it is difficult to detect phrases and map the composite semantic resource. This paper proposes a longest matching resource mapping scheme to solve this problem, namely, to find the longest substring in a sentence that can match the knowledge base resource. Based on this scheme, we propose an optimization algorithm based on… More >

Yonghong Xie^{1, 3}, Liangyuan Hu^{1, 3}, Xingxing Chen^{2, 3}, Jim Feng⁴, Dezheng Zhang^{1, 3, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297

Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the syndrome and get all possibilities… More >

Fulian Yin, Yanyan Wang, Jianbo Liu^*, Meiqi Ji

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 747-764, 2020, DOI:10.32604/cmes.2020.010579

Abstract Word similarity (WS) is a fundamental and critical task in natural language processing. Existing approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality corpus. However, it may suffer from poor performance for insuf- ficient corpus in some specific fields, and cannot capture rich semantic and sentimental information. To address these above problems, we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge, namely EWS-CS model, which can provide extra semantic information to enhance word similarity evaluation. The core of our approach contains… More >