Dimitris Chatzopoulos | DeepAI

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∙ 06/28/2023

Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

The multitude of data generated by sensors available on users' mobile de...

0 Mattia Giovanni Campana, et al. ∙

∙ 02/08/2023

AVeCQ: Anonymous Verifiable Crowdsourcing with Worker Qualities

In crowdsourcing systems, requesters publish tasks, and interested worke...

0 Sankarshan Damle, et al. ∙

∙ 01/13/2021

Towards Mobile Distributed Ledgers

Advances in mobile computing have paved the way for new types of distrib...

0 Dimitris Chatzopoulos, et al. ∙

∙ 01/06/2021

IPLS : A Framework for Decentralized Federated Learning

The proliferation of resourceful mobile devices that store rich, multidi...

0 Christodoulos Pappas, et al. ∙

∙ 11/09/2020

SplitEasy: A Practical Approach for Training ML models on Mobile Devices in a split second

Modern mobile devices, although resourceful, cannot train state-of-the-a...

35 Kamalesh Palanisamy, et al. ∙

∙ 11/12/2019

Emerging Natural User Interfaces in Mobile Computing: A Bottoms-Up Survey

Mobile and wearable interfaces and interaction paradigms are highly cons...

0 Kirill A. Shatilov, et al. ∙

∙ 06/10/2019

FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)

Although peer prediction markets are widely used in crowdsourcing to agg...

0 Moin Hussain Moti, et al. ∙

∙ 08/13/2018

Privacy Preserving and Cost Optimal Mobile Crowdsensing using Smart Contracts on Blockchain

The popularity and applicability of mobile crowdsensing applications are...

0 Dimitris Chatzopoulos, et al. ∙

∙ 03/01/2018

Boosting the Performance of Content Centric Networking using Delay Tolerant Networking Mechanisms

Content-centric networking (CCN) introduces a paradigm shift from a host...

0 Hasan M A Islam, et al. ∙

∙ 09/17/2013

Mobile augmented reality survey: a bottom-up approach

Augmented Reality (AR) is becoming mobile. Mobile devices have many cons...

0 Zhanpeng Huang, et al. ∙

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