Building trust-aware P2P systems : from trust and reputation management to decentralized e-commerce applications

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Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

A critical role of online platforms like Facebook, Wikipedia, YouTube, Amazon, Doordash, and Tinder is to moderate content. Interventions like banning users or deleting comments are carried out thousands of times daily and can potentially improve our onlin ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

Peer-to-peer (P2P) sharing may foster sustainability synergistically by saving resources, reducing costs and promoting satisfaction of consumer needs and social connectivity. A survey of Swiss and Swedish consumers (N = 1522) was conducted to analyze drive ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

This thesis investigates the relationship between investors' demand shocks and asset prices through the use of data on portfolio holdings. In three chapters, I study the theory, estimation, and application of demand-based asset pricing models, which incorp ...

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone) ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are obse ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

We use generalized Ray-Knight theorems, introduced by B. Toth in 1996, together with techniques developed for excited random walks as main tools for establishing positive and negative results concerning convergence of some classes of diffusively scaled sel ...

These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are s ...

ObjectivePrematurity is associated with a high risk of long-term behavioral problems. This study aimed to assess the prognostic utility of volumetric brain data at term-equivalent-age (TEA), clinical perinatal factors, and parental social economic risk in ...

Peer-to-peer (p2p) networks are not independent of their peers, and the network efficiency depends on peers contributing resources. Because shared resources are not free, this contribution must be rewarded. Peers across the network may share computation po ...

Internet blackouts are challenging environments for anonymity and censorship resistance. Existing popular anonymity networks (e.g., Freenet, I2P, Tor) rely on Internet connectivity to function, making them impracticable during such blackouts. In such a set ...

Building trust-aware P2P systems : from trust and reputation management to decentralized e-commerce applications

Chattez avec Graph Search

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Extensions of Peer Prediction Incentive Mechanisms

Content Moderation in Online Platforms

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

OASIS: An integrated optimisation framework for activity scheduling

Promoting synergies for sustainability through peer-to-peer sharing: an analysis of drivers and barriers

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Demand-based Asset Pricing: Theory, Estimation and Applications

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

AI-driven Prices for Externalities and Sustainability in Production Markets

Density estimation in RKHS with application to Korobov spaces in high dimensions

Timing and spatial selection bias in rapid extreme event attribution

On double-descent in uncertainty quantification in overparametrized models

The elliptical Ornstein-Uhlenbeck process

Regularization for distributionally robust state estimation and prediction

CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA

Wideband integrated bioaerosol sensor (WIBS) excited, fluorescent, and hyper-fluorescent particle number concentrations and normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020

Behavioral outcome of very preterm children at 5 years of age: Prognostic utility of brain tissue volumes at term-equivalent-age, perinatal, and environmental factors

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

Moby: A Blackout-Resistant Anonymity Network for Mobile Devices