Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

DEVELOPMENT PROSPECTS OF INCENTIVE TAXATION MECHANISMS OF BUSINESS ENTITIES IN AGRICULTURAL SECTOR OF THE ECONOMY

In the contemporary world, the agricultural industry faces a system call, which priority is to resolve the problem of a new high-quality model of development with the use of state financial support, which main tool is tax incentives based on a differentiated approach depending on the specific amounts of direct state financial support that is received by each business entity. The contemporary nature of development mechanism for incentive taxation of economic entities in agricultural sector of the economy is under constant review of both theorists and practitioners who study the taxation problems of farmers. However, solution to this issue for national farmers in current situation has not yet been completely researched; they require further study, using both foreign and native experience in order to implement it in national tax system. To justify the theoretical aspects of the development prospects of tax incentive mechanisms, best practices have been used, developed by both international and native practice of the tax system and scientific progress of foreign and native scientists who researched the development of tax incentive mechanisms for business entities in agricultural sector of the economy. It was proved that in order to improve taxation and tax incentives for farmers, it becomes necessary to clarify the criteria for classifying business entities in the industry as agricultural manufacturers. Unreasonably, economic entities that process agricultural products are not included in the Tax Code. Considering this, within years, the number of imported agricultural products, including livestock products, has been increasing. The result was a new author’s approach to dealing with the development prospects of tax incentives mechanism for business entities in the agricultural sector of the economy. So, at the legislative level, it is necessary to make amendments to the Tax Code of Ukraine on taxation of agrarians with a single tax (fourth group), depending on their specialization, by providing preferences to agricultural producers for the production of livestock products and their processing.

A Monetary Incentive DTN Routing For Wireless Sensor Networks

The DTN (Delay/Interrupt Tolerant Network) protocol that relies on nodes to handle network interruptions is one of the important components of the wireless sensor network (WSN) routing protocol. However, due to resource consumption, nodes may be unable to unconditionally relay data. To address this issue, several incentive mechanisms have recently been proposed to encourage node participation. However, the existing solutions either do not fully consider the sender’s budget or do not consider the relay cost limitation, which violated the practical incentive mechanism requirements of the DTN protocol. In this paper, we focus on developing a new incentive mechanism for DTN routing that specifically address the challenges brought up by budget and relay cost limitation. Our core idea is to define the payoff functions of the sender and the next hops, then optimize the strategies under the constraints of the sender’s budget and the relay’s cost. Our experimental results demonstrate that the maximized social welfare for all participants can be realized under these constraints.

Green roofs – good regulatory practices with potential to be applied in Bulgaria and North Macedonia

Green roofs are used worldwide to mitigate the impacts of extensive urbanization, bringing benefits on social, economic, and environmental levels. In order to promote and facilitate the construction of green roofs by private investors, many countries have developed specific legislative requirements and incentives. However, there still are countries where the construction of green roofs is not properly addressed in the legislation, and where no incentive mechanisms are developed.  The good practices in three European countries, leaders in regards to the implementation of green roofs – the Netherlands, Belgium, and Germany, are analyzed in this article. A variety of incentives is introduced to accelerate the construction of green roofs. Different requirements are also set to ensure that the roofs will be designed and maintained to provide the desired benefits. The existing local regulations in Bulgaria and North Macedonia were analyzed as well. The only incentive in Bulgaria is the possibility of reducing the legally required green area by compensating it with a green roof. In North Macedonia, no legislative documents or incentives related to green roofs were found. The regulations, applied in the Netherlands, Belgium, Germany, and other countries can be used as good practice examples, modified, and applied from the authorities of countries that still have not developed their own, in order to motivate the investors and facilitate the construction of green roofs.

Service outsourcing contract design under asymmetric information

PurposeUpstream suppliers attempt to outsource product after-sales services to midstream third-party service providers while selling the product directly to downstream sellers, forming a networked supply chain. However, a problem of information asymmetry in the market demand among supply chain members exists. The authors investigate the impact of demand information asymmetry among third-party service providers, upstream suppliers and downstream sellers in the supply chain on the supplier's contract selection under the networked framework.Design/methodology/approachThe authors establish a model in which the supplier can use a wholesale price contract and facilitate a signaling game between the third-party service provider and the seller. Conversely, the supplier could use a menu contract to establish an incentive mechanism to solve information asymmetry. The authors propose heuristic algorithms to quickly estimate a supplier's optimal profit.FindingsThe results show that when the demand forecasting bias is relatively small, the use of a menu contract by the supplier could eliminate information asymmetry; when the demand forecasting bias is large enough, the signaling mechanism between the third-party service provider and the seller could alleviate the double marginalization effect in the supply chain. Although it is common to solve the asymmetric information problem by establishing incentive mechanisms, the authors found that in the latter case, the supplier is better off when no incentive mechanisms are implemented in the networked supply chain.Originality/valueThis study compares screening and signaling effects and compares firms' profits in both cases.