Rapid Development Of Services Research Articles

Цифровая экономика и экономика данных

Whether we are talking about the digital economy, the data economy, or big data, it is always meant to rep-resent information (be it signals, commands, or data) in digital format or, more precisely, in binary code. Under-standing this is the key to understanding many events and facts. In particular, this concerns the rapid develop-ment of network services based on digital technologies and many other realities of the modern economy. This is shown in the article using specific examples from the past of leading companies and specific individuals who have become symbols of the new economy.

中国托育服务发展的政策支持与完善路径研究

The childcare service in our country has just started and is faced with many problems and challenges. At present, there are some urgent problems to be solved in the development of childcare services in China, such as the contradiction between supply and demand of childcare services, regional differences in the implementation of childcare service policies, insufficient national and local financial investment and subsidies, unclear responsibilities of management departments, and uneven quality of childcare services. In order to address the negative impact of market-oriented childcare services, the government has included childcare services in the strategic planning of establishing a sound social welfare system. In the path of the development of childcare services, the policy intention of promoting the development of childcare services can be promoted by advocating new and changing childcare institutions, breaking the traditional dual type system of childcare services, implementing price control of childcare services and improving the comprehensive supervision system of childcare services, so as to promote the healthy and rapid development of childcare services in China and meet the needs of society and families.

Greenwashing and sustainability assurance: a review and call for future research

PurposeThe purpose of this paper is to synthesize insights from the emerging work in accounting on greenwashing and sustainability assurance and propose an agenda for future research in this area.Design/methodology/approachThis article offers an original analysis of papers published on greenwashing and sustainability assurance research in the field of accounting. It adopts a systematic literature review and a narrative approach to analyse the dominant themes and key findings in this new and rapidly evolving field. From this overview, specific avenues for future research are identified.FindingsIn the past few years there has been a substantial spike in concern relating to greenwashing among academics, practitioners, regulators and society. This growing concern has only partly been reflected in the research literature. To date, research has primarily focused on: (1) the characteristics of firms adopting sustainability assurance, (2) the challenges facing sustainability auditors, (3) the development of appropriate assurance standards and regulations, and (4) capital market responses to greenwashing and sustainability auditing/assurance. Three key future research issues with respect to greenwashing are identified: (1) the future of standard-setter attempts to regulate greenwashing, (2) professional jockeying in sustainability reporting assurance, and (3) capital market opportunities and challenges relating to greenwashing and assurance.Originality/valueDespite the profound economic and reputational impact of greenwashing and the rapid development of sustainability assurance services, research in accounting remains fragmented and emergent. This review identifies avenues offering considerable scope for inter-disciplinarity and bridging the divide between academia and practice.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Practical and privacy-preserving geo-social-based POI recommendation

With the rapid development of location-based services and online social networks, POI recommendation services considering geographic and social factors have received extensive attention. Meanwhile, the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services. However, there is a degree of distrust of the cloud by service providers. To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. Some privacy-preserving schemes for geo-social-based POI recommendation have been presented, but they have some limitations in supporting group query, considering both geographic and social factors, and query accuracy, making these schemes impractical. To solve this issue, we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users, which are named GSPR-S and GSPR-G. Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. Then, we apply BGV fully homomorphic encryption to design some private algorithms, including a private max/min operation algorithm, a private rectangular set operation algorithm, and a private rectangular overlapping detection algorithm. After that, we use these algorithms as building blocks in our schemes for efficiency improvement. According to security analysis, our schemes are proven to be secure against the honest-but-curious cloud servers, and experimental results show that our schemes have good performance.

THE IMPACT OF HOME-BASED CARE SERVICES ON ADULT CHILDREN AS CAREGIVERS OF OLDER ADULTS IN CHINA

Abstract The family has always been the primary resource for elder care support in China, and adult children are the main caregivers of older parents. In the background of rapid development of home-based care services for older adults, this study aims to explore the impacts of the home-based care services on the adult children as caregivers for their older parents. Based on a national representative sample of aged 60 years or older (N=8674) from the China Longitudinal Aging Social Survey (CLASS) in 2018 and 2020, this study used propensity score matching difference-in-differences method to analyze the data. Firstly, we found that home-based care services for older adults could reduce the care burden of family caregivers by sharing part of the instrumental support effectively for the elderly. Secondly, this substitution effect is more significant among the families with more dependable and vulnerable older adults. Finally, the use of home-based care services appeared not to affect the intergenerational relationships between older parents and their adult children caregivers. Home-based care services may relieve the burden of family caregivers by providing instrumental care support for older adults. Therefore, it is very necessary to accelerate the implementation of an elder care programs in community to benefit the older and younger generations in Chinese family.

Internet of Things and Digital Twin Technology-Based Management System of Medical Equipment

Background: In recent years medical technology has progressed with the rapid development of medical services and required optimization of medical equipment. However, a lack of effective management methods has led to the inefficient use of medical equipment. Therefore, an effective medical equipment management mode is urgently needed to address these problems and challenges. Methods: The Internet of Things and digital twin technology are applied to intelligent medical equipment management as the current standard of medical equipment management. Results: The intelligent perception terminal can realize the dynamic acquisition of real data, such as the location, process, and efficient use of medical equipment, and help carry out digital, networked, and intelligent monitoring and analysis. Meanwhile, applications such as dynamic management software, real-time positioning software, and space-environment quality monitoring software are being developed. Conclusion: Automatic, intelligent, and visual management of medical equipment configurations, operations, and performance evaluation, combined with good management based on digital twinning, can improve collaborative management efficiency and operation resource support.

Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation.

The rapid development of Internet services and social platforms encourages users to share their opinions. To help users give valuable comments, content providers expect the recommender system to offer appropriate suggestions, including specific features of the item described in texts and emojis, which are all considered aspects of the user reviews. Hence, the review aspect recommendation task has become significant, where the key lies in handling personal preferences and semantic correlations between suggested items. This article proposes a correlation-aware review aspect recommender (CARAR) system model by constructing self-representation correlations between different views of review aspects, including textual aspects and emojis to make a personalized recommendation. The dependencies between different textual aspects and emojis can be identified and utilized to facilitate the factorization process to learn user and item latent factors. The cross-view correlation mapping between textual aspects and emojis can be built to enhance the recommendation performance. Moreover, the additional information in the real-world environment is also applied to our model to adjust the recommendation results. We constructed experiments on five self-collected and public datasets and compared with six existing models. The results show that our model can outperform the existing models on review aspects recommendation tasks, validating the effectiveness of our approach.

Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.

For Security and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-Than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes the variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity than the conventional Nyquist-criterion transmission with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). More importantly, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data.

With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data.

Alternative Method for OTP based Authentication in Areas with Weak Mobile Signal

Abstract: The rapid development of Internet-based applications and services has made websites a crucial part of our lives. More and more users are using online office services instead of desktop office software, online social services to engage in social activities, online information boards to post their business products, and online shopping to do their purchasing. To make all these online activities more secure One Time Password (OTP) are used. Due to weak mobile signal receiving this OTP becomes difficult.

LDPORR: A localized location privacy protection method based on optimized random response

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

Impact of BCA E-Banking Service Quality on Customer E-Satisfaction and E- Loyalty

The development of banking services through electronic media was increasingly widespread along with the rapid growth of information technology. Technological developments through digital transformation in the banking system require banking services in Indonesia to switch to e-banking services. E-banking is a form of service innovation by utilizing technological developments. E-banking services that are currently developing rapidly are mobile banking and internet banking. The increase in mobile banking and internet banking transactions in Indonesia has increased by more than 300% from 2016 to August 2021. One of the banking companies that has the largest number of e-banking users in Indonesia is BCA. With the rapid development of e-banking services, it is necessary to evaluate the quality of e-banking services including reliability, privacy and security, website design and customer service and support. Therefore, this study aims to determine impact of the quality of BCA e-banking services on customer satisfaction (e-satisfaction) and loyalty (e-loyalty). The sample used in this study was 230 data taken by purposive sampling. The data obtained were analyzed using structural equation model analysis (SEM) with the help of the AMOS 24 application. The results showed that there was a significant impact between reliability, website design and customer service and support on e-satisfaction. Meanwhile privacy and security have a significant impact on e-loyalty. In addition, this study also found that there was a significant impact of e-satisfaction on e-loyalty.

The Influence of BCA E-Banking Service Quality on Customer E-Satisfaction and E- Loyalty

The development of banking services through electronic media was increasingly widespread along with the rapid growth of information technology. Technological developments through digital transformation in the banking system require banking services in Indonesia to switch to e-banking services. E-banking is a form of service innovation by utilizing technological developments. E-banking services that are currently developing rapidly are mobile banking and internet banking. The increase in mobile banking and internet banking transactions in Indonesia has increased by more than 300% from 2016 to August 2021. One of the banking companies that has the largest number of e-banking users in Indonesia is BCA. With the rapid development of e-banking services, it is necessary to evaluate the quality of e-banking services including reliability, privacy and security, website design and customer service and support. Therefore, this study aims to determine impact of the quality of BCA e-banking services on customer e-satisfaction and e-loyalty. The sample used in this study was 230 data taken by purposive sampling. The data obtained were analyzed using structural equation model analysis (SEM) with the help of the AMOS 24 application. The results showed that there was a significant impact between reliability, website design and customer service and support on e-satisfaction. Meanwhile privacy and security have a significant impact on e-loyalty. In addition, this study also found that there was a significant impact of e-satisfaction on e-loyalty.

Comparison of three participation modes on ride-hailing platforms

With the rapid development of ride-hailing services, various participation modes have emerged on ride-hailing platforms that are recruiting drivers to serve the growing number of passengers. This paper investigates three popular participation modes for a person joining the ride-hailing platform as a driver: car buying mode (BM), short-term leasing mode (SLM), and financial leasing mode (FLM). We compare these three modes and study which is the best choice from the perspective of the driver and platform, considering different types of drivers (myopic or strategic). We develop an analytical model to explore this problem. Interestingly, even when the driver bears a high cost of maintenance, BM always dominates the other two modes for the driver. We find that the platform’s optimal strategy depends on the driver type. FLM will never be an optimal choice for the platform if the driver is strategic. However, if the driver is myopic, FLM will be the best mode under certain conditions. We also find that when the maintenance cost is moderate, regardless of the driver type, the driver and platform can realise a win-win situation with SLM, but otherwise, they are better off adopting BM. Moreover, with moderate maintenance cost, FLM can be a win-win strategy if the driver is myopic. Lastly, our study discloses that increasing the new-driver rewards benefits both the driver and platform only if the driver is myopic and the reward rate exceeds a certain threshold.

The Partition Bridge (PB) tree: Efficient nearest neighbor query processing on road networks

With the rapid development of location-based services, many types of applications in urban areas such as transportation planning, traffic management, and deployment of infrastructure depend on spatial objects that are distributed along the road network. As current place datasets include millions of spatial objects and human mobility datasets capture billions of locations, it is an important challenge to answer k-nearest neighbor queries efficiently. However, shortest-path distance calculations are the computational bottleneck for a k-nearest neighbor queries on road networks. Existing query algorithms partition the network to mitigate this bottleneck, but they do not take the data distribution into account, which leads to inefficiencies in dense areas and sparse areas. In this paper, an efficient and scalable indexing method called the Partition Bridge tree (PB-tree), is proposed based on hierarchical network partitions that consider network connectivity and data distribution. The structure of the PB-tree mainly includes distance matrices, union-bridges, bridges, and active network nodes component. Based on the PB-tree, a k nearest neighbor query processing algorithm is proposed by combining “bottom-up”, “top-down”, and adjacent extension methods. By using different road networks and datasets, the effectiveness and practicability of PB-tree are evaluated; the experimental results show that PB-tree outperforms the state-of-the-art methods and the classical approach.

Multi-Relay Cognitive Network With Anti-Fragile Relay Communication for Intelligent Transportation System Under Aggregated Interference

The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.