Kingdee (China)

Abstract Digital employee that integrates various digital technologies according to the optimization needs of specific business processes plays an important role for the digital transformation of enterprises. The purpose of digital employee is to help enterprises reduce costs and increase efficiency. Nowadays, digital employee has been widely applied in various enterprise fields including human resource, operations, purchase and supply chain management, etc. In this paper, we propose a practical architecture of digital employee in finance and introduce how digital employee can be used for saving labor costs and improving efficiency. In the 10,000 reimbursement audits tasks per month, the processing time has shortened from the original 27 days to 55 h by using digital employee, greatly improving the efficiency of financial approval and providing a positive role in promoting the transformation of digital economy.KeywordsDigital employeeRobotic Process Automation (RPA)Financial audit

Abstract Blockchain as an emerging computing paradigm has revolutionized the next generation internet. The design and realization of blockchain in multiple industries are very challenging tasks due to the complexity of blockchain. Firstly, it refers to the integrations of multiple technologies within communications, distributed system and security. Secondly, it lacks systematic method and theory to guide developers to design the business solutions. To facilitate the down-to-earth application of blockchain, we propose a BlockChain Open Architecture (BCOA) as a reference architecture which divides the blockchain into nine layers: interactions, scenarios, services, components, infrastructure, integration, data architecture, security and privacy and governance. It allows the designers and developers to use this methodology to speedily design solutions for blockchain applications. Finally we use two cases to demonstrate the feasibility and effectiveness of the proposed architecture.KeywordsBlockchainOpen architectureReference architecture

Chinese named entity recognition (NER) has been an important problem in natural language processing (NLP) field. Most existing methods mainly use traditional deep learning models which cannot fully leverage contextual dependencies that are very important for capturing the relations between words or characters for modeling. To address this problem, various language representation methods such as BERT have been proposed to learn the global context information. Although these methods can achieve good results, the large number of parameters limited the efficiency and application in real-world scenarios. To improve both of the performance and efficiency, this paper proposes an ALBERT-based Chinese NER method which uses ALBERT, a Lite version of BERT, as the pre-trained model to reduce model parameters and to improve the performance through sharing cross-layer parameters. Besides, it uses conditional random field (CRF) to capture the sentence-level correlation information between words or characters to alleviate the tagging inconsistency problems. Experimental results demonstrate that our method outperforms the comparison methods over 4.23–11.17% in terms of relative F1-measure with only 4% of BERT’s parameters.

The papers in this special section focus on data centric big data services. As an overwhelming amount of data is generated at a faster rate every day from all sources, and applications such as cloud services, the Internet of Things (IoT), social network services and intelligent terminals, it has become more urgent than ever to design, deploy and provision services more wisely so that the provisioned services could support effective acquisition, storage, transformation, process, management and utilization of such data. Manipulating and getting the most out of the Big Data can bring unprecedented value and new opportunities that are critical to business success. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, and improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of the Big Data. Datacentric big service is an inevitable evolution of services with the emergence of big data in the last decade.