Symeon Papadopoulos

Symeon Papadopoulos
The Centre for Research and Technology, Hellas · Information Technologies Institute (ITI)

PhD Informatics, Aristotle University of Thessaloniki

About

232
Publications
75,077
Reads
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4,088
Citations
Citations since 2016
133 Research Items
3230 Citations
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Introduction
Symeon Papadopoulos currently works at the Information Technologies Institute (ITI), The Centre for Research and Technology, Hellas. Symeon does research in Artificial Intelligence, Data Mining, Social Media and Databases.
Additional affiliations
September 2006 - present
The Centre for Research and Technology, Hellas
Position
  • Research Associate
Description
  • Conducting research on the topics of multimedia analysis and indexing, social media mining, and web data mining and management.
Education
January 2008 - February 2012
Aristotle University of Thessaloniki
Field of study
  • Informatics
September 2007 - July 2009
Blekinge Institute of Technology
Field of study
  • Management
September 2004 - August 2006
Eindhoven University of Technology
Field of study
  • Information and Communication Technologies

Publications

Publications (232)
Article
Full-text available
We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithmic components, such as graph filters, post-processors, measures, benchmarks, and online tuning. Computations can be delegated to numpy, tensorflow, or pytorch backends and fit in ba...
Article
Full-text available
The overwhelming amount of information and misinformation on social media platforms has created a new role that these platforms are inclined to take on, that of the Internet custodian. Mainstream platforms, such as Facebook, Twitter and YouTube, are under tremendous public and political pressure to combat disinformation and remove harmful content....
Article
Full-text available
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided...
Article
Full-text available
Estimating the preferences of consumers is of utmost importance for the fashion industry as appropriately leveraging this information can be beneficial in terms of profit. Trend detection in fashion is a challenging task due to the fast pace of change in the fashion industry. Moreover, forecasting the visual popularity of new garment designs is eve...
Preprint
Full-text available
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided...
Conference Paper
In this paper, we discuss how we can re-imagine the interaction between users (i.e. fashion designers and consumers) and fashion items, by researching and developing technologies that allow virtual try-on of garments. In this direction we a) generate personal 3D avatars of the user, b) automatically simulate the interaction between 3D user avatars...
Article
Computer Vision (CV) has achieved remarkable results, outperforming humans in several tasks. Nonetheless, it may result in significant discrimination if not handled properly as CV systems highly depend on training datasets and can learn and amplify biases that such datasets may carry. Thus, the problem of understanding and discovering bias in visua...
Preprint
Full-text available
In order to consider fashion outfits as aesthetically pleasing, the garments that constitute them need to be compatible in terms of visual aspects, such as style, category and color. With the advent and omnipresence of computer vision deep learning models, increased interest has also emerged for the task of visual compatibility detection with the a...
Preprint
Full-text available
Image memes and specifically their widely-known variation image macros, is a special new media type that combines text with images and is used in social media to playfully or subtly express humour, irony, sarcasm and even hate. It is important to accurately retrieve image memes from social media to better capture the cultural and social aspects of...
Article
Full-text available
The proliferation of online news, especially during the “infodemic” that emerged along with the COVID-19 pandemic, has rapidly increased the risk of and, more importantly, the volume of online misinformation. Online Social Networks (OSNs), such as Facebook, Twitter, and YouTube, serve as fertile ground for disseminating misinformation, making the n...
Preprint
Full-text available
Decentralization is emerging as a key feature of the future Internet. However, effective algorithms for search are missing from state-of-the-art decentralized technologies, such as distributed hash tables and blockchain. This is surprising, since decentralized search has been studied extensively in earlier peer-to-peer (P2P) literature. In this wor...
Preprint
Full-text available
Enabled by recent improvements in generation methodologies, DeepFakes have become mainstream due to their increasingly better visual quality, the increase in easy-to-use generation tools and the rapid dissemination through social media. This fact poses a severe threat to our societies with the potential to erode social cohesion and influence our de...
Preprint
Full-text available
Estimating the preferences of consumers is of utmost importance for the fashion industry as appropriately leveraging this information can be beneficial in terms of profit. Trend detection in fashion is a challenging task due to the fast pace of change in the fashion industry. Moreover, forecasting the visual popularity of new garment designs is eve...
Preprint
Full-text available
In this work, we aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. Graph Neural Networks (GNNs) are known to improve the accuracy of simple classifiers in centralized settings by leveraging naturally occurring network links, but graph convolutional layers are...
Article
Communication via digital means, such as mobile messaging applications (apps), plays an increasingly important role in everyday life. However, most messaging apps employ centralized computing principles that relinquish control of their users' personal data to social network platform providers. Decentralization has been proposed as an alternative th...
Chapter
Fine-grained information extraction from fashion imagery is a challenging task due to the inherent diversity and complexity of fashion categories and attributes. Additionally, fashion imagery often depict multiple items while fashion items tend to follow hierarchical relations among various object types, categories, and attributes. In this study, w...
Chapter
Full-text available
Reliable image geolocation is crucial for several applications, ranging from social media geo-tagging to media verification. State-of-the-art geolocation methods surpass human performance on the task of geolocation estimation from images. However, no method assesses the suitability of an image for this task, which results in unreliable and erroneou...
Article
Full-text available
Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be res...
Preprint
Full-text available
Reliable image geolocation is crucial for several applications, ranging from social media geo-tagging to fake news detection. State-of-the-art geolocation methods surpass human performance on the task of geolocation estimation from images. However, no method assesses the suitability of an image for this task, which results in unreliable and erroneo...
Preprint
Full-text available
We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning. Computations can be delegated to numpy, tensorflow or pytorch backends and fit in back-p...
Article
The rapid spread of misinformation online has been deemed as a growing problem in the current digital media environment with significant impact both on journalism and on society at large. As news practitioners are increasingly challenged by information overload and the need to process huge volumes of unstructured and unfiltered data within a very s...
Article
User-generated content -commonly referred to as "eyewitness media"- has become an essential component in journalism and news reporting. Increasingly more news providers, such as news agencies, broadcasters and Web-only players have set up teams of dedicated investigators or are in the process of training parts of their journalistic workforce to gat...
Preprint
Full-text available
Computer Vision (CV) has achieved remarkable results, outperforming humans in several tasks. Nonetheless, it may result in major discrimination if not dealt with proper care. CV systems highly depend on the data they are fed with and can learn and amplify biases within such data. Thus, both the problems of understanding and discovering biases are o...
Preprint
Full-text available
In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose either: (i) fine-grained approaches employing spatio-temporal representations and similarity calculations, achieving high performance at a high computational cost or (ii) co...
Chapter
Full-text available
Artificial Intelligence brings exciting innovations in all aspects of life and creates new opportunities across industry sectors. At the same time, it raises significant questions in terms of trust, ethics, and accountability. This paper offers an introduction to the AI4Media project, which aims to build on recent advances of AI in order to offer i...
Preprint
Full-text available
In this paper, we address the problem of global-scale image geolocation, proposing a mixed classification-retrieval scheme. Unlike other methods that strictly tackle the problem as a classification or retrieval task, we combine the two practices in a unified solution leveraging the advantages of each approach with two different modules. The first l...
Preprint
Full-text available
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of...
Preprint
Full-text available
Graph filters are an emerging paradigm that systematizes information propagation in graphs as transformation of prior node values, called graph signals, to posterior scores. In this work, we study the problem of mitigating disparate impact, i.e. posterior score differences between a protected set of sensitive nodes and the rest, while minimally edi...
Chapter
In this work we address algorithmic fairness concerns that arise when graph nodes are ranked based on their structural relatedness to a personalized set of query nodes. In particular, we aim to mitigate disparate impact, i.e. the difference in average rank between nodes of a sensitive attribute compared to the rest, while also preserving node rank...
Book
This book constitutes the proceedings of the 4th International Workshop on Chatbot Research and Design, CONVERSATIONS 2020, which was held during November 23-24, 2020, hosted by the University of Amsterdam. The conference was planned to take place in Amsterdam, The Netherlands, but changed to an online format due to the COVID-19 pandemic. The 14 pa...
Conference Paper
Personalized PageRank (PPR) is a popular scheme for scoring the relevance of network nodes to a set of seed ones through a random walk with restart process. Calculating the scores of all network nodes often involves the power method, which iterates the PPR formula until convergence to an empirically selected numerical tolerance. However, finding a...
Preprint
Full-text available
In this work, we address the problem of audio-based near-duplicate video retrieval. We propose the Audio Similarity Learning (AuSiL) approach that effectively captures temporal patterns of audio similarity between video pairs. For the robust similarity calculation between two videos, we first extract representative audio-based video descriptors by...
Conference Paper
In this work we address algorithmic fairness concerns that arise when graph nodes are ranked based on their structural relatedness to a personalized set of query nodes. In particular, we aim to mitigate disparate impact, i.e. the difference in average rank between nodes of a sensitive attribute compared to the rest, while also preserving node rank...
Chapter
The new generation of bike-sharing services without docking stations is spreading around large cities of the world. The paper provides a technical specification of a platform, for managing a dockless bike sharing system. The bicycles of the platform are equipped with GPS devices and GPRS cards that can transmit, over the Internet, their exact locat...
Preprint
Full-text available
Recent advancements in content generation technologies (widely known as DeepFakes) along with the online proliferation of manipulated media and disinformation campaigns render the detection of such manipulations a task of increasing importance. Even though there are many DeepFake detection methods, only few focus on the impact of dataset preprocess...
Preprint
Full-text available
Multivariate time series forecasting is of great importance to many scientific disciplines and industrial sectors. The evolution of a multivariate time series depends on the dynamics of its variables and the connectivity network of causal interrelationships among them. Most of the existing time series models do not account for the causal effects am...
Article
Full-text available
A problem that frequently occurs when mining complex networks is selecting algorithms with which to rank the relevance of nodes to metadata groups characterized by a small number of examples. The best algorithms are often found through experiments on labeled networks or unsupervised structural community quality measures. However, new networks could...
Article
Full-text available
Multivariate time series forecasting is of great importance to many scientific disciplines and industrial sectors. The evolution of a multivariate time series depends on the dynamics of its variables and the connectivity network of causal interrelationships among them. Most of the existing time series models do not account for the causal effects am...
Article
Full-text available
Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are v...
Chapter
In this paper we introduce a fusion framework for image tampering localization, that moves towards overcoming the limitation of available tools by allowing a synergistic analysis and multiperspective refinement of the final forensic report. The framework is designed to combine multiple state-of-the-art techniques by exploiting their complementariti...
Article
Full-text available
Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for...
Preprint
Full-text available
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and...
Book
This book constitutes the refereed proceedings of the Third International Workshop on Chatbot Research and Design, CONVERSATIONS 2019, held in Amsterdam, The Netherlands, in November 2019. The 18 revised full papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers are grouped in the following topical sec...
Conference Paper
Full-text available
An emerging problem in network analysis is ranking network nodes based on their relevance to metadata groups that share attributes of interest, for example in the context of recommender systems or node discovery services. For this task, it is important to evaluate ranking algorithms and parameters and select the ones most suited to each network. Un...
Conference Paper
Full-text available
Cultural products such as music tracks intend to be appreciated and recognized by a portion of the audience. However , no matter how highly recognized a song might be at the beginning of its life, its recognition will inevitably and progressively decay. The mechanism that governs this decreasing trajectory could be modelled as a forgetting curve or...
Conference Paper
In recent years, social media have become a powerful medium of public discourse, and in particular microblogging services (e.g., Twitter) play a crucial role in information dissemination. Being able to efficiently monitor and glean insights from streams of social media interactions could be valuable for better mapping and engaging with a community...