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With the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.

Research in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.

The proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.

E-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result.

The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software.

The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak.

During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.

Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

The aim of this paper is to investigate the gap between cost-based and time-based revenue recognition schemes in the accounting of ship-owning corporations, and to propose cost-based revenue recognition (as in general accounting practice) in connection with the performance obligations.

For a comparative analysis of time-based (traditional approach) and cost-based schemes, a sample of dry bulk ships is selected and voyage estimations are performed by certified professional shipbrokers (Fellow of the Institute of Chartered Shipbrokers) (data collection and voyage estimation by practitioner). Performance obligations are also defined by certified shipbrokers (i.e. survey and expert opinion) and certified public accountant based on common shipping business practice and accounting practice in general.

Empirical results indicate the significant gap between two alternative schemes. Cost-based revenue recognition accelerates the revenue recognition (benefit of shipowner), and it enables comparability among other industries since cost-based allocation is the common practice in accounting (matching principle, Generally Accepted Accounting Principles).

It is obviously impossible to observe all kinds of freight market transactions for all different kinds of vessel particulars. The sample size does not undervalue the current study since the central idea of this paper is not the verification of the cost-based recognition in all possible transactions.

The proposed approach debiases the existing recognition practice as well as improving the speed of revenue recognition. In the existing practice, time-based recognition is still based on voyage estimations (time estimation). Voyage estimations conventionally answer two questions: “What is the cost of the voyage?” and “What is the duration of the voyage?” Therefore, the proposed approach does not require any additional work done. Common practice also clarifies the cost-based schedule for revenue recognition.

This paper addresses the unconventional accounting practice and its incomparability problem for the first time. To the best of the authors’ knowledge, this paper is also the first study on accounting economics of the shipping business. This paper proposes a practical solution to the debate raised by Financial Accounting Standards Board 2014-09 regulation on accounting standards by utilizing a staging approach and cost-based revenue allocation.

This study aims to identify European positioning on the use of remote customer onboarding solutions in combating financial crime.

This study is a desktop research that examines European Banking Authority (EBA) policy statements relating to the use of innovative solutions in combating financial crime.

Technological advancements in biometric data and software tools provide a unique opportunity to address potential paper customer onboarding process deficiencies. Electronic remote customer onboarding solutions equip credit, financial institutions and investment firms with an alternative FTE cost-saving solution, in their pursuit of revenue generation. Whilst the EBA and Financial Action Task Force have provided approval for the utilisation of innovative solutions and AML technologies in combatting financial crime. Hesitancy remains on the ability of credit and financial institutions to use technological solutions as a “magic solution” in preventing the materialisation of money laundering/terrorist financing related risks. Analysis of policy suggests a gravitation towards the increased use of the aforementioned technologies in the interim.

Capitalisation of European banking authority.

Blockchain is a distributed ledger, in which the blocks containing transaction details are connected chronologically to form a series of chains, thus raising the possibility of improving the process and innovating business model for the financial institutions. The purpose of this paper is to study the actual cases of Blockchain applied in Korea in 2017, so that a vision of business model innovation of financial institutions can be drawn.

The financial institutions in Korea are in the technology verification stage to introduce Blockchain technology. Since there is an insufficient amount of actual measurement data, case study method was adopted. The authors interviewed ICT officers of major banks in Korea. The purpose of the interview was to understand the relationship between Blockchain and business models of financial institutions, and the effects and challenges that Blockchain has on the business model of financial institutions.

From the perspective of financial institutions, the emergence of Blockchain does not just have technical significance – emergence of highly efficient database system – but has the possibility that if the business model of existing financial intermediaries disappears or get reduced, the financial services relying on them can disappear altogether, or some of them can be replaced, and financial transaction patterns of consumers can be changed. As a case studies researched for this paper, it was discovered that the distributed characteristic of Blockchain cannot be applied when actually developing financial services.

The purpose of this study is to examine the South African banks’ customer due diligence (CDD) practices in the fintech era to mitigate money laundering (ML) risks and ensure financial stability. Financial technologies have brought substantial transformations to the financial services sector. However, such technologies have exposed the sector to emerging risks that threaten the integrity and stability of the financial system globally. Before any bank–customer relationship is established, proper customer background checks must be conducted. These background checks enable financial institutions to validate information provided and ensure customers are properly risk profiled. Failure to risk profile customers could result in financial institutions being used as conduits for ML. Undoubtedly, CDD procedures are pivotal to overall anti-money laundering efforts and curbing financing terrorism in a regulatory framework.

A qualitative research approach was adopted to address the research questions of the study. Given the confidentiality associated with the financial services sector, data triangulation was used in blending mainly secondary and primary data sources. Secondary data sources used in the study were published reports available in the public domain that were corroborated with subject matter experts’ interviews.

Based on the findings of this study, it is concluded that in South Africa, technological solutions have been incorporated into CDD functions, which is now risk-based (enhanced due diligence). Also, legally, South Africa has incorporated the biometrics, integration with Department of Home Affairs and Companies and Intellectual Property Commission databases, customer consent to third-party sources with the Financial Intelligence Centre Act and the Protection of Personal Information Act.

The shift towards digital banking in South Africa results in increased data and dynamic risk profiling. This study advocates a policy shift requiring a risk-based approach to mitigating emerging ML risks (in particular digital laundering), especially in the wake of South Africa’s recent greylisting by the Financial Action Task Force.