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

2022 ◽  
Vol 22 (1) ◽  
pp. 1-23
Author(s):  
Jia Xu ◽  
Yuanhang Zhou ◽  
Gongyu Chen ◽  
Yuqing Ding ◽  
Dejun Yang ◽  
...  
Keyword(s):  
Social Network ◽  
Diffusion Model ◽  
Large Scale ◽  
Incentive Mechanism ◽  
Estimation Algorithm ◽  
Task Completion ◽  
Incentive Mechanisms ◽  
Mobile Crowdsourcing ◽  
Real World Datasets ◽  
Budget Feasible

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.

scholarly journals The User Participation Incentive Mechanism of Mobile Crowdsensing Network Based on User Threshold

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hua Su ◽  
Qianqian Wu ◽  
Xuemei Sun ◽  
Ning Zhang
Keyword(s):  
Large Scale ◽  
Incentive Mechanism ◽  
User Participation ◽  
Task Completion ◽  
Processing Efficiency ◽  
Mobile Crowdsensing ◽  
Budget Control ◽  
Experiment Validation ◽  
Incentive Strategies ◽  
Incentive Strategy

Mobile crowdsensing (MCS) network means completing large-scale and complex sensing tasks in virtue of the mobile devices of ordinary users. Therefore, sufficient user participation plays a basic role in MCS. On the basis of studying and analyzing the strategy of user participation incentive mechanism, this paper proposes the user threshold-based cognition incentive strategy against the shortcomings of existing incentive strategies, such as task processing efficiency and budget control. The user threshold and the budget of processing subtasks are set at the very beginning. The platform selects the user set with the lowest threshold, and the best user for processing tasks according to users’ budget. The incentive cost of the corresponding users is calculated based on the user threshold at last. In conclusion, through the experiment validation and comparison with the existing user participation incentive mechanism, it was found that the user threshold-based incentive strategy is advantageous in improving the proportion of task completion and reducing the platform’s budget cost.

scholarly journals Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd

2019 ◽  
Vol 33 ◽  
pp. 1996-2003 ◽  
Author(s):  
Naman Goel ◽  
Boi Faltings
Keyword(s):  
Gold Standard ◽  
Large Scale ◽  
Incentive Mechanism ◽  
Incentive Compatibility ◽  
The Other ◽  
Important Class ◽  
Incentive Mechanisms ◽  
Game Theoretic

An important class of game-theoretic incentive mechanisms for eliciting effort from a crowd are the peer based mechanisms, in which workers are paid by matching their answers with one another. The other classic mechanism is to have the workers solve some gold standard tasks and pay them according to their accuracy on gold tasks. This mechanism ensures stronger incentive compatibility than the peer based mechanisms but assigning gold tasks to all workers becomes inefficient at large scale. We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers’ accuracy. We show that the resulting mechanism ensures a dominant notion of incentive compatibility and fairness.

scholarly journals A Financial Incentive Mechanism for Truthful Reporting Assurance in Online Crowdsourcing Platforms

2021 ◽  
Vol 16 (6) ◽  
pp. 2014-2030
Author(s):  
Alireza Mohammadi ◽  
Seyyed Alireza Hashemi Golpayegani
Keyword(s):  
Mechanism Design ◽  
Financial Incentive ◽  
Incentive Mechanism ◽  
High Volume ◽  
Effective Strategy ◽  
Budget Management ◽  
Management Mechanism ◽  
Price Management ◽  
Budget Feasible

In today’s world, crowdsourcing is regarded as an effective strategy to deal with a high volume of small issues whose solutions can have their own complexities in systems. Moreover, requesters are currently providing hundreds of thousands of tasks in online job markets and workers need to perform these tasks to earn money. Thus far, various aspects of crowdsourcing including budget management, mechanism design for price management, forcing workers to behave truthfully in bidding prices, or maximized gains of crowdsourcing have been considered in different studies. One of the main existing challenges in crowdsourcing is how to ensure truthful reporting is provided by contributing workers. Since the amount of pay to workers is directly correlated with the number of tasks performed by them over a period of time, it can be predicted that strong incentives encourage them to carry out more tasks by giving untruthful answers (providing the first possible answer without examining it) in order to increase the amount of pay. However, crowdsourcing requesters need to obtain truthful reporting as an output of tasks assigned to workers. In this study, a mechanism was developed whose implementation in crowdsourcing could ensure truthful reporting by workers. The mechanism provided in this study was evaluated as more budget feasible and it was also fairer for requesters and workers due to its well-defined procedure.

scholarly journals UVLens

2021 ◽  
Vol 5 (2) ◽  
pp. 1-26
Author(s):  
Longbiao Chen ◽  
Chenhui Lu ◽  
Fangxu Yuan ◽  
Zhihan Jiang ◽  
Leye Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Floating Population ◽  
Open Government ◽  
Residential Areas ◽  
Rapid Urbanization ◽  
Urban Village ◽  
Open Government Data ◽  
Government Data ◽  
Real World Datasets ◽  
Urban Villages

Urban villages refer to the residential areas lagging behind the rapid urbanization process in many developing countries. These areas are usually with overcrowded buildings, high population density, and low living standards, bringing potential risks of public safety and hindering the urban development. Therefore, it is crucial for urban authorities to identify the boundaries of urban villages and estimate their resident and floating populations so as to better renovate and manage these areas. Traditional approaches, such as field surveys and demographic census, are time consuming and labor intensive, lacking a comprehensive understanding of urban villages. Against this background, we propose a two-phase framework for urban village boundary identification and population estimation. Specifically, based on heterogeneous open government data, the proposed framework can not only accurately identify the boundaries of urban villages from large-scale satellite imagery by fusing road networks guided patches with bike-sharing drop-off patterns, but also accurately estimate the resident and floating populations of urban villages with a proposed multi-view neural network model. We evaluate our method leveraging real-world datasets collected from Xiamen Island. Results show that our framework can accurately identify the urban village boundaries with an IoU of 0.827, and estimate the resident population and floating population with R2 of 0.92 and 0.94 respectively, outperforming the baseline methods. We also deploy our system on the Xiamen Open Government Data Platform to provide services to both urban authorities and citizens.

scholarly journals Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146220 ◽  
Author(s):  
Aleksandra do Socorro da Silva ◽  
Silvana Rossy de Brito ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
Cláudio Alex Jorge da Rocha ◽  
Maurílio de Abreu Monteiro ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Large Scale ◽  
Information And Communication

scholarly journals The information complexity of learning tasks, their structure and their distance

2021 ◽  
Author(s):  
Alessandro Achille ◽  
Giovanni Paolini ◽  
Glen Mbeng ◽  
Stefano Soatto
Keyword(s):  
Kolmogorov Complexity ◽  
Large Scale ◽  
Parametric Model ◽  
Training Dataset ◽  
Optimization Scheme ◽  
Learning Tasks ◽  
Asymmetric Distance ◽  
Special Cases ◽  
Scale Models ◽  
Real World Datasets

Abstract We introduce an asymmetric distance in the space of learning tasks and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity and Shannon and Fisher information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in deep learning.

MetaTP

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Weida Zhong ◽  
Qiuling Suo ◽  
Abhishek Gupta ◽  
Xiaowei Jia ◽  
Chunming Qiao ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Modern Society ◽  
Training Data ◽  
Traffic Prediction ◽  
Temporal Prediction ◽  
Reference Space ◽  
Meta Learning ◽  
Real World Datasets

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

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