most citedAutomatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives

4 citations · 11 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV20221 cited

Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications

Andrea Cavagna, Nan Li, Alexandros Iosifidis +1

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…

cs.CV20222 cited

Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing

Zhongtian Dong, Nan Li, Alexandros Iosifidis +1

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…

q-fin.ST2022

Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots

Mostafa Shabani, Martin Magris, George Tzagkarakis +2

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchroniz…

cs.LG2022

Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing

Nan Li, Alexandros Iosifidis, Qi Zhang

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge…

cs.CV20224 cited

Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives

Anssi Männistö, Mert Seker, Alexandros Iosifidis +1

Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences. The ability…

cs.RO20222 cited

OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

N. Passalis, S. Pedrazzi, R. Babuska +15

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…