Showing papers on "Service provider published in 2019"

Advances and Open Problems in Federated Learning

Abstract: Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Ridesourcing systems: A framework and review

Abstract: With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

An intrusion detection system for connected vehicles in smart cities

Abstract: In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%.

FactSheets: Increasing trust in AI services through supplier's declarations of conformity

Abstract: Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers’ trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multidimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers’ trust. In this article, inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI in the Appendix of this article.

Conceptualizing smart service systems

Abstract: Recent years have seen the emergence of physical products that are digitally networked with other products and with information systems to enable complex business scenarios in manufacturing, mobility, or healthcare. These “smart products”, which enable the co-creation of “smart service” that is based on monitoring, optimization, remote control, and autonomous adaptation of products, profoundly transform service systems into what we call “smart service systems”. In a multi-method study that includes conceptual research and qualitative data from in-depth interviews, we conceptualize “smart service” and “smart service systems” based on using smart products as boundary objects that integrate service consumers’ and service providers’ resources and activities. Smart products allow both actors to retrieve and to analyze aggregated field evidence and to adapt service systems based on contextual data. We discuss the implications that the introduction of smart service systems have for foundational concepts of service science and conclude that smart service systems are characterized by technology-mediated, continuous, and routinized interactions.

Platforms in the peer-to-peer sharing economy

Abstract: The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.,This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.,First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.,This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.,This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

Abstract: A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.

Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity

Abstract: Motivated by recent innovations in service delivery such as ride-sharing services and work-from-home call centers, we study capacity management when workers self-schedule. Our service provider chooses capacity to maximize its profit (revenue from served customers minus capacity costs) over a horizon. Because demand varies over the horizon, the provider benefits from flexibility to adjust its capacity from period to period. However, the firm controls its capacity only indirectly through compensation. The agents have the flexibility to choose when they will or will not work and they optimize their schedules based on the compensation offered and their individual availability. To guarantee adequate capacity, the firm must offer sufficiently high compensation. An augmented newsvendor formula captures the tradeoffs for the firm and the agents. If the firm could keep the flexibility but summon as many agents as it wants (i.e., have direct control) for the same wages it would not only generate higher profit, as is expected, but would also provide better service levels to its customers. If the agents require a “minimum wage” to remain in the agent pool they will have to relinquish some of their flexibility. To pay a minimum wage the firm must restrict the number of agents that can work in some time intervals. The costs to the firm are countered by the self-scheduling firm’s flexibility to match supply to varying demand. If the pool of agents is sufficiently large relative to peak demand, the firm earns more than it would if it had control of agents’ schedules but had to maintain a fixed staffing level over the horizon.

A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT

Abstract: Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency.

A healthcare monitoring system using random forest and internet of things (IoT)

Abstract: The Internet of Things (IoT) enabled various types of applications in the field of information technology, smart and connected health care is notably a crucial one is one of them. Our physical and mental health information can be used to bring about a positive transformation change in the health care landscape using networked sensors. It makes it possible for monitoring to come to the people who don't have ready access to effective health monitoring system. The captured data can then be analyzed using various machine learning algorithms and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. These scenarios already exist, but we intend to enhance it by analyzing the past data for predicting future problems using prescriptive analytics. It will allow us to move from reactive to visionary approach by rapidly spotting trends and making recommendations on behalf of the actual medical service provider. In this paper, the authors have applied different machine learning techniques and considered public datasets of health care stored in the cloud to build a system, which allows for real time and remote health monitoring built on IoT infrastructure and associated with cloud computing. The system will be allowed to drive recommendations based on the historic and empirical data lying on the cloud. The authors have proposed a framework to uncover knowledge in a database, bringing light to disguise patterns which can help in credible decision making. This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease. Experimental results are conducted using a few machine learning algorithms considered in this paper like K-NN, Support Vector Machine, Decision Trees, Random Forest, and MLP.

Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

Abstract: Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services

Abstract: Algorithmic decision-making systems are increasingly being adopted by government public service agencies. Researchers, policy experts, and civil rights groups have all voiced concerns that such systems are being deployed without adequate consideration of potential harms, disparate impacts, and public accountability practices. Yet little is known about the concerns of those most likely to be affected by these systems. We report on workshops conducted to learn about the concerns of affected communities in the context of child welfare services. The workshops involved 83 study participants including families involved in the child welfare system, employees of child welfare agencies, and service providers. Our findings indicate that general distrust in the existing system contributes significantly to low comfort in algorithmic decision-making. We identify strategies for improving comfort through greater transparency and improved communication strategies. We discuss the implications of our study for accountable algorithm design for child welfare applications.

Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey

Abstract: With the growth in computing technologies, cloud computing has added a new paradigm to user services that allows accessing Information Technology services on the basis of pay-per-use at any time and any location. Owing to flexibility in cloud services, numerous organizations are shifting their business to the cloud and service providers are establishing more data centers to provide services to users. However, it is essential to provide cost-effective execution of tasks and proper utilization of resources. Several techniques have been reported in the literature to improve performance and resource use based on load balancing, task scheduling, resource management, quality of service, and workload management. Load balancing in the cloud allows data centers to avoid overloading/underloading in virtual machines, which itself is a challenge in the field of cloud computing. Therefore, it becomes a necessity for developers and researchers to design and implement a suitable load balancer for parallel and distributed cloud environments. This survey presents a state-of-the-art review of issues and challenges associated with existing load-balancing techniques for researchers to develop more effective algorithms.

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Abstract: With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

eHealth Cloud Security Challenges: A Survey.

Abstract: Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Abstract: As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform.

Service Function Chaining Use Cases in Mobile Networks

Abstract: This document provides some exemplary use cases for service function chaining in mobile service provider networks. The objective of this draft is not to cover all conceivable service chains in detail. Rather, the intention is to localize and explain the application domain of service chaining within mobile networks as far as it is required to complement the SFC problem statement and architecture framework of the working group. Service function chains typically reside in a LAN segment which links the mobile access network to the actual application platforms located in the carrier's datacenters or somewhere else in the Internet. Service function chains (SFC) ensure a fair distribution of network resources according to agreed service policies, enhance the performance of service delivery or take care of security and privacy. SFCs may also include Value Added Services (VAS). Commonly, SFCs are typical middle box based services. General considerations and specific use cases are presented in this document to demonstrate the different technical requirements of these goals for service function chaining in mobile service provider networks. The specification of service function chaining for mobile networks must take into account an interaction between service function chains and the 3GPP Policy and Charging Control (PCC) environment.

Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA

Abstract: Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third-party reverse logistics providers (3PRLPs). This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi-criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.

Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application

Abstract: Attaining superior service quality is a primary concern for all service providers, since they face a constant demand for providing high-quality customer-oriented services. It becomes utmost important to understand consumer expectations and their needs effectively so as to survive in this competitive market. The demand for better service quality is also rising due to the increased aspiration level of customers with an increase in their per capita income. Hospital management needs to recognise and make a match with patients’ perception of what service quality (SQ) is and deliver better healthcare services. This paper presents an integration of Fuzzy set theory and SERVQUAL methodology to measure the SQ of four hospitals from Punjab state of India. We have used Fuzzy Analytical Hierarchy Process to find out the priority of each of the dimensions and sub-dimensions of healthcare SQ attributes. The priority is then used for ranking the best hospital from the patient perspective.

Factors influencing adoption of payments banks by Indian customers: extending UTAUT with perceived credibility

Abstract: The purpose of this paper is to investigate the factors influencing the behavioral intention to adopt payments banks services by Indian underbanked and unbanked population.,The proposed model has assimilated factors from the Unified Theory of Acceptance and Use of Technology (UTAUT) along with perceived credibility. The factors of UTAUT include performance expectancy, effort expectancy, facilitation of conditions and social influence. Apart from testing the direct relationships of the model constructs with the behavioral intention to adopt payments banks services, the study has also explored mediating and moderating effects of certain constructs. The research model has been empirically tested using 660 responses from a field survey conducted in New Delhi – the capital city of India – by using the structured equation modeling (SEM) technique. The target respondents of the study are small businessmen and migrant laborers who are either underbanked or unbanked.,The findings of the study reveal that the model is able to explain 67.5 per cent of the variance in behavioral intention. The results indicate that all the factors are direct determinants of behavioral intention. Perceived credibility is found to be the strongest influencer of behavioral intention. The findings also indicate that perceived credibility partially mediates the relationships between “social influence and behavioral intention” and “performance expectancy and behavioral intention.” The relationship between performance expectancy and behavioral intention is also found to be moderated by facilitating conditions and effort expectancy.,As this study is based on a convenience sample of respondents of only one city of India, this could negatively reflect on the generalizability of results across other cities. Moreover, the study has only focused on the perceptions of small businessmen and migrant laborers. This raises concerns regarding the applicability of the results for other segments of the current population that have different demographic characteristics (e.g. occupation, income, education level and technology experience). Modifying the conceptual model presented in this research to include “experience” and “age” as moderators can also be worth considering in future. Although this study has extended the UTAUT to include perceived credibility, the results of the explanatory power of the model indicate that there is still room for improvement. Therefore, including other constructs, e.g. hedonic motivation, perceived risks and trialability, could be a fruitful path forward. Future studies may also examine the factors influencing the actual use behavior of payments banks, rather than just behavioral intention.,The study looks forward to providing the payments banks service providers in India with suitable guidelines for effectively implementing and designing payments banks services. Specifically, the results of this study have provided clues for Indian payments banks service providers about the crucial role of perceived credibility in influencing the behavioral intention to adopt payments banks. Therefore, service providers have to initially be sure that payments banks are able to conduct financial transactions efficiently, securely and within less time, along with the availability of information required by customers to successfully use the services. Service providers should enhance customer confidence and trust by providing secure and reliable services. They should also emphasize on the positive safety measures of the payments banks during any marketing campaign rather than just creating brand awareness.,The study represents a substantial contribution to the existing knowledge regarding mobile payment channels in particular and technology acceptance area in general. In fact, this study presents a worthwhile direction by examining payments banks services, which, so far, have not been well evaluated in the Indian context. To the best of the authors’ knowledge, this is an early attempt toward a holistic and integrative approach to explain adoption of payments banks in India. Although prior studies have addressed mobile banking and mobile payment adoption, the strength of this research lies in combining the UTAUT constructs with perceived credibility. This is evidenced by the high explanatory power (67.5 per cent) of the research model adopted in this study.

A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Abstract: Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.