
Georgios AlexandridisNational and Kapodistrian University of Athens | uoa · Digital Industry Technologies
Georgios Alexandridis
Doctor of Engineering
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62
Publications
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588
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Introduction
Additional affiliations
May 2017 - March 2023
September 2009 - December 2015
Publications
Publications (62)
Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. Th...
In this work, a novel approach to earthquake detection by integrating deep learning architectures with decentralized data management is introduced. To this end, variational autoencoders are trained within the OASEES framework, employing the InterPlanetary File System for data and model storage, moving beyond traditional centralized cloud/edge proce...
The maritime industry heavily relies on vessel maintenance to ensure the operational integrity and safety, as it is responsible for transporting more than 80% of global trade. Despite the industry's strong need for efficient maintenance techniques, there has been a noticeable gap in research regarding the use of data-driven methods to enhance vesse...
Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platform...
Purpose - Digital transformation is often hindered for cultural
heritage institutions operating in rural areas, due to factors
including lack of digital literacy and/or digital readiness, limited
capacity, underfunding and remoteness. The typology of these
institutions varies, involving e.g. community-run folklore museums,
cultural sites maintained...
Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) are the neural network of choice, and significant progress has be...
Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents,...
Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) have been the neural network of choice, with significant progress...
Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states and increasing diversity. For this purpose, a diffusion model is used to generate artificial st...
This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work...
This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at...
This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this co...
Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in terms of visi...
Highway operators are in a constant search of techniques and methodologies that can reduce their energy footprint. In this respect, the installation of dimmable light-emitting diode lights on the open road section of highways appears to be a promising solution, due to the reduced energy consumption (compared to high pressure sodium lamps) and the a...
Augmented Reality technologies can be considered to be a fascinating choice for educators seeking resources and methods to stimulate their students about the topic they teach. In recent years, the increasing number of relevant educational applications indicates that this new technology has the potential of becoming a leading educational method in s...
The research field of digital storytelling is cross-disciplinary and extremely wide. In this paper, methods, frameworks, and tools that have been created for authoring and presenting digital narratives, are selected and examined among hundreds of works. The basic criterion for selecting these works has been their ability to create content by comput...
Importance sampling, a variant of online sampling, is often used in neural network training to improve the learning process, and, in particular, the convergence speed of the model. We study, here, the performance of a set of batch selection algorithms, namely, online sampling algorithms that process small parts of the dataset at each iteration. Con...
The use of machine learning in the field of reactor safety and noise diagnostics has recently seen great potential given the advancements made in computational tools, hardware and noise simulations. In this work we demonstrate how deep neural networks, specifically recurrent and convolutional neural networks can be trained in a synthetic setting an...
Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents,...
Machine Learning is used in this paper for noise-diagnostics to detect defined anomalies in nuclear plant reactor cores solely from neutron detector measurements. The proposed approach leverages advanced diffusion-based core simulation tools to generate large amounts of simulated data with different types of driving perturbations originating at all...
The modern cultural industry and the related academic sectors have shown increased interest in Cultural User eXperience (CUX) research, since it constitutes a critical factor to examine and apply when presenting cultural content. Recent CUX studies show that visitors tend to carry their own cultural characteristics and preferences when visiting des...
Monte Carlo Tree Search has proved to be very efficient in the broad domain of Game AI, though it suffers from high dimensionality in cases of large branching factors. Several pruning techniques have been proposed to tackle this problem, most of which require explicit domain knowledge. In this study, an approach using neural networks to determine t...
This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called “neutron-noise” signals: that is, fluctuati...
Social media platforms have become a primary source of information and public influence. This dynamic has given rise to the interest of journalists, companies, scientists and organizations in identifying the most productive and influential agents of a network. Although popular indicators such as Reach, Engagement and Virality can be a good basis fo...
The widespread proliferation of online social networks has resulted in the creation of huge amounts of data related to, among other things, the expression of opinion and sentiment about literally all aspects of everyday life. In this respect, various tools have been developed by interested parties (companies, individuals) that monitor the social me...
This article presents a novel framework for the semantic enrichment of documents, exploiting the hierarchical ontological knowledge of a domain in conjunction with classification techniques. The main contributions of this work are fourfold: (a) a well-defined theoretical model for the semantic representation and enrichment of documents is defined,...
The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on...
As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advan...
Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence-to-sequence neural-based text summari...
The safe operation of nuclear power plants is highly dependent on the ability of quickly and accurately identifying possible anomalies and perturbations in the reactor. Operational defects are primarily diagnosed by detectors that capture changes in the neutron flux, placed at various points inside and outside of the core. Neutron flux signals are...
Aspect-based sentiment prediction is a specific area of sentiment analysis that models the sentiment of a text excerpt as a multi-dimensional quantity pertaining to various interpretations, rather than a scalar one, that admits a single explanation. Extending earlier work, the said task is examined as a part of a unified architecture that collects,...
Package recommendation systems have gained in popularity especially in the tourism domain, where they propose combinations of different types of attractions that can be visited by someone during a city tour. These systems can also be applied in suggesting home entertainment, proper nutrition or academic courses. Such systems must optimize multiple...
The safe operation of nuclear power plants is highly dependent on the ability of quickly and accurately identifying possible anomalies and perturbations in the reactor. Operational defects are primarily diagnosed by detectors that capture changes in the neutron flux, placed at various points inside and outside of the core. Neutron flux signals are...
Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmen...
Sentiment analysis is a vigorous research area, with many application domains. In this work, aspect-based sentiment prediction is examined as a component of a larger architecture that crawls, indexes and stores documents from a wide variety of online sources, including the most popular social networks. The textual part of the collected information...
Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inf...
A critical issue for the safe operation of nuclear power plants is to quickly and accurately detect possible anomalies and perturbations in the reactor. Defects in operation are principally identified through changes in the neutron flux, as captured by detectors placed at various points inside and outside of the core. While wavelet-based analysis o...
This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a su...
This work constitutes a theoretically-informed empirical analysis of the spatial characteristics of the short-term rentals' market and explores their linkage with shifts in the wider housing market within the context of a southeastern EU metropolis. The same research objective has been pursued for a variety of international paradigms; however , to...
Recent theoretical and practical advances have led to the emergence of review-based recommender systems, where user preference data is encoded in at least two dimensions; the traditional rating scores in a predefined discrete scale and the user-generated reviews in the form of free-text. The main contribution of this work is the presentation of a n...
Home Sharing Breathes Life into Underdeveloped Athens Neighborhoods
https://news.gtp.gr/2019/04/17/home-sharing-breathes-life-underdeveloped-athens-neighborhoods/
Genetic Algorithms (GAs) have been predominantly used in video games for finding the best possible sequence of actions that leads to a win condition. This work sets out to investigate an alternative application of GAs on action-adventure type video games. The main intuition is to encode actions depending on the state of the world of the game instea...
Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content...
Even though path recommendation is a subject that has been vigorously studied, the majority of related work has been predominantly focused on travel and routing topics, with relatively minimal incorporation of cultural context. The latter issue is addressed in the current contribution through the proposition of a personalized, context-aware cultura...
Integrated spiral inductors are a fundamental part of Radio-Frequency (RF) circuits. In certain scenarios, a solution to the inverse spiral inductor design problem is required; given the desired properties of an inductor, locate the most suitable geometric characteristics. This problem does not have a unique solution and current approaches approxim...
The popularity of recommendation systems has made them a substantial component of many applications and projects. This work proposes a framework for package recommendations that try to meet users’ preferences as much as possible through the satisfaction of several criteria. This is achieved by modeling the relation between the items and the categor...
Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social...
Review-based recommender systems have gained noticeable ground in recent years. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data,...
In this paper a new algorithm for session identification in web logs is outlined, based on the fuzzy c-means clustering of the available data. The novelty of the proposed methodology lies in the initialization of the partition matrix using subtractive clustering, the examination of the effect a variety of distance metrics have on the clustering pro...
In this chapter, we focus on recommender systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the social graph, where every step in the walk is chosen almost uniformly at random from the available choices. Alth...
In this paper, we focus on Recommender Systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the Social Graph, where every step in the walk is chosen almost uniformly at random from the available choices. Even t...
In online Recommender Systems, people tend to consume and rate items that are not necessarily similar to one another. This phenomenon is a direct consequence of the fact that human taste is influenced by many factors that cannot be captured by pure Content-based or Collaborative Filtering approaches. For this reason, a desirable property of Recomme...
Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying...
Most recommender systems usually have too many items to recommend to too many users using limited information. This problem
is formally known as the sparsity of the ratings’ matrix, because this is the structure that holds user preferences. This article outlines a collaborative
recommender system, that tries to amend this situation. The system is b...
PDAs and other handheld devices are commonly used for processing private or otherwise secret information. Their increased usage along with their networking capabilities raises security considerations for the protection of the sensitive information they contain and their communications.
We present CryptoPalm, an extensible cryptographic library for...