4,517 research outputs found
Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks
By opportunistically engaging mobile users (workers), mobile crowdsensing
(MCS) networks have emerged as important approach to facilitate sharing of
sensed/gathered data of heterogeneous mobile devices. To assign tasks among
workers and ensure low overheads, a series of stable matching mechanisms is
introduced in this paper, which are integrated into a novel hybrid service
trading paradigm consisting of futures trading mode and spot trading mode to
ensure seamless MCS service provisioning. In the futures trading mode, we
determine a set of long-term workers for each task through an
overbooking-enabled in-advance many-to-many matching (OIA3M) mechanism, while
characterizing the associated risks under statistical analysis. In the spot
trading mode, we investigate the impact of fluctuations in long-term workers'
resources on the violation of service quality requirements of tasks, and
formalize a spot trading mode for tasks with violated service quality
requirements under practical budget constraints, where the task-worker mapping
is carried out via onsite many-to-many matching (O3M) and onsite many-to-one
matching (OMOM). We theoretically show that our proposed matching mechanisms
satisfy stability, individual rationality, fairness and computational
efficiency. Comprehensive evaluations also verify the satisfaction of these
properties under practical network settings, while revealing commendable
performance on running time, participators' interactions, and service quality
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure
Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data
As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers’ payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers’ implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning agents
to build an accurate and robust machine learning models without sharing raw
data. By allowing mobile handsets and devices to collaboratively learn a global
model without explicit sharing of training data, FL exhibits high privacy and
efficient spectrum utilization. While there are a lot of survey papers
exploring FL paradigms and usability in 6G privacy, none of them has clearly
addressed how FL can be used to improve the protocol stack and wireless
operations. The main goal of this survey is to provide a comprehensive overview
on FL usability to enhance mobile services and enable smart ecosystems to
support novel use-cases. This paper examines the added-value of implementing FL
throughout all levels of the protocol stack. Furthermore, it presents important
FL applications, addresses hot topics, provides valuable insights and explicits
guidance for future research and developments. Our concluding remarks aim to
leverage the synergy between FL and future 6G, while highlighting FL's
potential to revolutionize wireless industry and sustain the development of
cutting-edge mobile services.Comment: 32 pages, 7 figures; 9 Table
The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking
Complex systems and realist evaluation offer promising approaches for evaluat-ing social interventions. These approaches take into account the complex interplay among factors to produce outcomes, instead of attempting to isolate single causes of observed effects. This paper explores the use of Bayesian networks (BNs) in real-ist evaluation of interventions to prevent complex social problems. It draws on the example of the theory-based evaluation of the Work in Freedom Programme (WIF), a large UK-funded anti-trafficking intervention by the International Labour Organi-sation in South Asia. We used BN to explore causal pathways to human traffick-ing using data from 519 Nepalese returnee migrants. The findings suggest that risks of trafficking are mostly determined by migrants’ destination country, how they are recruited and in which sector they work. These findings challenge widely held assumptions about individual-level vulnerability and emphasize that future invest-ments will benefit from approaches that recognise the complexity of an interven-tion’s causal mechanisms in social contexts. BNs are a useful approach for the con-ceptualisation, design and evaluation of complex social intervention
Accurate and budget-efficient text, image, and video analysis systems powered by the crowd
Crowdsourcing systems empower individuals and companies to outsource labor-intensive tasks that cannot currently be solved by automated methods and are expensive to tackle by domain experts. Crowdsourcing platforms are traditionally used to provide training labels for supervised machine learning algorithms. Crowdsourced tasks are distributed among internet workers who typically have a range of skills and knowledge, differing previous exposure to the task at hand, and biases that may influence their work. This inhomogeneity of the workforce makes the design of accurate and efficient crowdsourcing systems challenging. This dissertation presents solutions to improve existing crowdsourcing systems in terms of accuracy and efficiency. It explores crowdsourcing tasks in two application areas, political discourse and annotation of biomedical and everyday images. The first part of the dissertation investigates how workers' behavioral factors and their unfamiliarity with data can be leveraged by crowdsourcing systems to control quality. Through studies that involve familiar and unfamiliar image content, the thesis demonstrates the benefit of explicitly accounting for a worker's familiarity with the data when designing annotation systems powered by the crowd. The thesis next presents Crowd-O-Meter, a system that automatically predicts the vulnerability of crowd workers to believe \enquote{fake news} in text and video. The second part of the dissertation explores the reversed relationship between machine learning and crowdsourcing by incorporating machine learning techniques for quality control of crowdsourced end products. In particular, it investigates if machine learning can be used to improve the quality of crowdsourced results and also consider budget constraints. The thesis proposes an image analysis system called ICORD that utilizes behavioral cues of the crowd worker, augmented by automated evaluation of image features, to infer the quality of a worker-drawn outline of a cell in a microscope image dynamically. ICORD determines the need to seek additional annotations from other workers in a budget-efficient manner. Next, the thesis proposes a budget-efficient machine learning system that uses fewer workers to analyze easy-to-label data and more workers for data that require extra scrutiny. The system learns a mapping from data features to number of allocated crowd workers for two case studies, sentiment analysis of twitter messages and segmentation of biomedical images. Finally, the thesis uncovers the potential for design of hybrid crowd-algorithm methods by describing an interactive system for cell tracking in time-lapse microscopy videos, based on a prediction model that determines when automated cell tracking algorithms fail and human interaction is needed to ensure accurate tracking
Ethics in the digital workplace
Aquesta publicació s'elabora a partir de les contribucions de cadascú dels membres nacionals que integren la Network of Eurofound Correspondents. Pel cas d'Espanya la contribució ha estat realitzada per l'Alejandro Godino (veure annex Network of Eurofound Correspondents)Adreça alternativa: https://www.eurofound.europa.eu/sites/default/files/ef_publication/field_ef_document/ef22038en.pdfDigitisation and automation technologies, including artificial intelligence (AI), can affect working conditions in a variety of ways and their use in the workplace raises a host of new ethical concerns. Recently, the policy debate surrounding these concerns has become more prominent and has increasingly focused on AI. This report maps relevant European and national policy and regulatory initiatives. It explores the positions and views of social partners in the policy debate on the implications of technological change for work and employment. It also reviews a growing body of research on the topic showing that ethical implications go well beyond legal and compliance questions, extending to issues relating to quality of work. The report aims to provide a good understanding of the ethical implications of digitisation and automation, which is grounded in evidence-based research
Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data
As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers' payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three real-world datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies
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