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Prof. Phivos Mylonas
Ionian University

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0 Information Retrieval
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Journal article
Published: 04 August 2021 in Electronics
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In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.

ACS Style

Andreas Kanavos; Maria Trigka; Elias Dritsas; Gerasimos Vonitsanos; Phivos Mylonas. A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data. Electronics 2021, 10, 1872 .

AMA Style

Andreas Kanavos, Maria Trigka, Elias Dritsas, Gerasimos Vonitsanos, Phivos Mylonas. A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data. Electronics. 2021; 10 (16):1872.

Chicago/Turabian Style

Andreas Kanavos; Maria Trigka; Elias Dritsas; Gerasimos Vonitsanos; Phivos Mylonas. 2021. "A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data." Electronics 10, no. 16: 1872.

Journal article
Published: 09 July 2021 in Neural Computing and Applications
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Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and supply chains, social media, computational aesthetics, and transportation networks. In this paradigm, signal processing methodologies are applied to the adjacency matrix, seen as a two-dimensional signal. Fundamental operations of this type include graph sampling, the graph Laplace transform, and graph spectrum estimation. In this context, topology similarity metrics allow meaningful and efficient comparisons between pairs of graphs or along evolving graph sequences. In turn, such metrics can be the algorithmic cornerstone of graph clustering schemes. Major advantages of relying on existing signal processing kernels include parallelism, scalability, and numerical stability. This work presents a scheme for training a tensor stack network to estimate the topological correlation coefficient between two graph adjacency matrices compressed with the two-dimensional discrete cosine transform, augmenting thus the indirect decompression with knowledge stored in the network. The results from three benchmark graph sequences are encouraging in terms of mean square error and complexity especially for graph sequences. An additional key point is the independence of the proposed method from the underlying domain semantics. This is primarily achieved by focusing on higher-order structural graph patterns.

ACS Style

Georgios Drakopoulos; Eleanna Kafeza; Phivos Mylonas; Lazaros Iliadis. Transform-based graph topology similarity metrics. Neural Computing and Applications 2021, 1 -13.

AMA Style

Georgios Drakopoulos, Eleanna Kafeza, Phivos Mylonas, Lazaros Iliadis. Transform-based graph topology similarity metrics. Neural Computing and Applications. 2021; ():1-13.

Chicago/Turabian Style

Georgios Drakopoulos; Eleanna Kafeza; Phivos Mylonas; Lazaros Iliadis. 2021. "Transform-based graph topology similarity metrics." Neural Computing and Applications , no. : 1-13.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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Database deployment is a complex task depending on a multitude of operational parameters such as anticipated data scaling trends, expected type and volume of queries, uptime requirements, replication policies, available budget, and personnel training and experience. Thus, enterprise database administrators eventually rely on various performance metrics in conjunction to existing company policies in order to determine the best possible solution under these constraints. The recent advent of NoSQL databases, including graph databases such as Neo4j and document stores like MongoDB, added another degree of freedom in database selection since for a number of years relational databases such as PostgreSQL were the only available technology. In this work the scaling characteristics of a representative set of social queries executed on virtual machine installations of PostgreSQL and MongoDB are evaluated on a large volume of political tweets regarding Brexit. Moreover, Wiener filters for predicting the execution time of social query windows of fixed length over both databases are designed.

ACS Style

Michael Marountas; Georgios Drakopoulos; Phivos Mylonas; Spyros Sioutas. Recommending Database Architectures for Social Queries: A Twitter Case Study. Collaboration in a Hyperconnected World 2021, 715 -728.

AMA Style

Michael Marountas, Georgios Drakopoulos, Phivos Mylonas, Spyros Sioutas. Recommending Database Architectures for Social Queries: A Twitter Case Study. Collaboration in a Hyperconnected World. 2021; ():715-728.

Chicago/Turabian Style

Michael Marountas; Georgios Drakopoulos; Phivos Mylonas; Spyros Sioutas. 2021. "Recommending Database Architectures for Social Queries: A Twitter Case Study." Collaboration in a Hyperconnected World , no. : 715-728.

Journal article
Published: 22 June 2021 in Applied Sciences
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The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.

ACS Style

Konstantinos Michalakis; Yannis Christodoulou; George Caridakis; Yorghos Voutos; Phivos Mylonas. A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces. Applied Sciences 2021, 11, 5770 .

AMA Style

Konstantinos Michalakis, Yannis Christodoulou, George Caridakis, Yorghos Voutos, Phivos Mylonas. A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces. Applied Sciences. 2021; 11 (13):5770.

Chicago/Turabian Style

Konstantinos Michalakis; Yannis Christodoulou; George Caridakis; Yorghos Voutos; Phivos Mylonas. 2021. "A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces." Applied Sciences 11, no. 13: 5770.

Review
Published: 17 May 2021 in Data
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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

ACS Style

Maria Nikiforos; Yorghos Voutos; Anthi Drougani; Phivos Mylonas; Katia Kermanidis. The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications. Data 2021, 6, 52 .

AMA Style

Maria Nikiforos, Yorghos Voutos, Anthi Drougani, Phivos Mylonas, Katia Kermanidis. The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications. Data. 2021; 6 (5):52.

Chicago/Turabian Style

Maria Nikiforos; Yorghos Voutos; Anthi Drougani; Phivos Mylonas; Katia Kermanidis. 2021. "The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications." Data 6, no. 5: 52.

Journal article
Published: 12 December 2020 in Big Data and Cognitive Computing
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Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.

ACS Style

Georgios Drakopoulos; Yorghos Voutos; Phivos Mylonas. Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia. Big Data and Cognitive Computing 2020, 4, 39 .

AMA Style

Georgios Drakopoulos, Yorghos Voutos, Phivos Mylonas. Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia. Big Data and Cognitive Computing. 2020; 4 (4):39.

Chicago/Turabian Style

Georgios Drakopoulos; Yorghos Voutos; Phivos Mylonas. 2020. "Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia." Big Data and Cognitive Computing 4, no. 4: 39.

Journal article
Published: 31 October 2020 in Mathematics
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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

ACS Style

Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas Mylonas; Panagiotis Pintelas. Extending Fuzzy Cognitive Maps With Tensor-Based Distance Metrics. Mathematics 2020, 8, 1898 .

AMA Style

Georgios Drakopoulos, Andreas Kanavos, Phivos Mylonas Mylonas, Panagiotis Pintelas. Extending Fuzzy Cognitive Maps With Tensor-Based Distance Metrics. Mathematics. 2020; 8 (11):1898.

Chicago/Turabian Style

Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas Mylonas; Panagiotis Pintelas. 2020. "Extending Fuzzy Cognitive Maps With Tensor-Based Distance Metrics." Mathematics 8, no. 11: 1898.

Journal article
Published: 15 October 2020 in Technologies
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Recent advances in big data systems and databases have made it possible to gather raw unlabeled data at unprecedented rates. However, labeling such data constitutes a costly and timely process. This is especially true for video data, and in particular for human activity recognition (HAR) tasks. For this reason, methods for reducing the need of labeled data for HAR applications have drawn significant attention from the research community. In particular, two popular approaches developed to address the above issue are data augmentation and domain adaptation. The former attempts to leverage problem-specific, hand-crafted data synthesizers to augment the training dataset with artificial labeled data instances. The latter attempts to extract knowledge from distinct but related supervised learning tasks for which labeled data is more abundant than the problem at hand. Both methods have been extensively studied and used successfully on various tasks, but a comprehensive comparison of the two has not been carried out in the context of video data HAR. In this work, we fill this gap by providing ample experimental results comparing data augmentation and domain adaptation techniques on a cross-viewpoint, human activity recognition task from pose information.

ACS Style

Evaggelos Spyrou; Eirini Mathe; Georgios Pikramenos; Konstantinos Kechagias; Phivos Mylonas. Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition. Technologies 2020, 8, 55 .

AMA Style

Evaggelos Spyrou, Eirini Mathe, Georgios Pikramenos, Konstantinos Kechagias, Phivos Mylonas. Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition. Technologies. 2020; 8 (4):55.

Chicago/Turabian Style

Evaggelos Spyrou; Eirini Mathe; Georgios Pikramenos; Konstantinos Kechagias; Phivos Mylonas. 2020. "Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition." Technologies 8, no. 4: 55.

Conference paper
Published: 13 September 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Self organizing maps (SOMs) are neural networks designed to be in an unsupervised way to create connections, learned through a modified Hebbian rule, between a high- (the input vector space) and a low-dimensional space (the cognitive map) based solely on distances in the input vector space. Moreover, the cognitive map is segmentwise continuous and preserves many of the major topological features of the latter. Therefore, neurons, trained using a Hebbian learning rule, can approximate the shape of any arbitrary manifold provided there are enough neurons to accomplish this. Moreover, the cognitive map can be readily used for clustering and visualization. Because of the above properties, SOMs are often used in big data pipelines. This conference paper focuses on a multilinear distance metric for the input vector space which adds flexibility in two ways. First, clustering can be extended to higher order data such as images, graphs, matrices, and time series. Second, the resulting clusters are unions of arbitrary shapes instead of fixed ones such as rectangles in case of \(\ell _1\) norm or circles in case of \(\ell _2\) norm. As a concrete example, the proposed distance metric is applied to an anonymized and open under the Creative Commons license cognitive multimodal dataset of fMRI images taken during three distinct cognitive tasks. Keeping the latter as ground truth, a subset of these images is clustered with SOMs of various configurations. The results are evaluated using the corresponding confusion matrices, topological error rates, activation set change rates, and intra-cluster distance variations.

ACS Style

Georgios Drakopoulos; Ioanna Giannoukou; Phivos Mylonas; Spyros Sioutas. On Tensor Distances for Self Organizing Maps: Clustering Cognitive Tasks. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 195 -210.

AMA Style

Georgios Drakopoulos, Ioanna Giannoukou, Phivos Mylonas, Spyros Sioutas. On Tensor Distances for Self Organizing Maps: Clustering Cognitive Tasks. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():195-210.

Chicago/Turabian Style

Georgios Drakopoulos; Ioanna Giannoukou; Phivos Mylonas; Spyros Sioutas. 2020. "On Tensor Distances for Self Organizing Maps: Clustering Cognitive Tasks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 195-210.

Journal article
Published: 08 July 2020 in Neural Computing and Applications
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The collection of video data for action recognition is very susceptible to measurement bias; the equipment used, camera angle and environmental conditions are all factors that majorly affect the distribution of the collected dataset. Inevitably, training a classifier that can successfully generalize to new data becomes a very hard problem, since it is impossible to gather general enough training sets. Recent approaches in the literature attempt to solve this problem by augmenting a given training set, with synthetic data, so as to better represent the global distribution of the covariates. However, these approaches are limited because they essentially involve hand-crafted data synthesizers, which are typically hard to implement and problem specific. In this work, we propose a different approach to tackling the above issues, which relies on the combination of two techniques: pose extraction, and domain adaptation as a means to improve the generalization capabilities of classifiers. We show that adapted skeletal representations can be retrieved automatically in a semi-supervised setting and these help to generalize classifiers to new forms of measurement bias. We empirically validate our approach for generalizing across different camera angles.

ACS Style

George Pikramenos; Eirini Mathe; Eleanna Vali; Ioannis Vernikos; Antonios Papadakis; Evaggelos Spyrou; Phivos Mylonas. An adversarial semi-supervised approach for action recognition from pose information. Neural Computing and Applications 2020, 32, 17181 -17195.

AMA Style

George Pikramenos, Eirini Mathe, Eleanna Vali, Ioannis Vernikos, Antonios Papadakis, Evaggelos Spyrou, Phivos Mylonas. An adversarial semi-supervised approach for action recognition from pose information. Neural Computing and Applications. 2020; 32 (23):17181-17195.

Chicago/Turabian Style

George Pikramenos; Eirini Mathe; Eleanna Vali; Ioannis Vernikos; Antonios Papadakis; Evaggelos Spyrou; Phivos Mylonas. 2020. "An adversarial semi-supervised approach for action recognition from pose information." Neural Computing and Applications 32, no. 23: 17181-17195.

Original paper
Published: 07 July 2020 in Evolving Systems
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Does a tweet with specific emotional content posted by an influential account have the capability to shape or even completely alter the opinions of its readers? Moreover, can other influential accounts further enhance its original emotional potential by retweeting it and, thus, letting their followers read it? Real Twitter conversations seem to imply an affirmative answer to both questions. If this is indeed the case, then what is the key for not only successfully reaching to a large number of accounts but also for convincingly offering an alternative perspective via affective means, therefore triggering a large scale opinion change in an ongoing Twitter conversation? This work primarily focuses on determining which tweets cause multiple sentiment polarity alternations to occur based on a window segmentation approach. Moreover, an offline framework for discovering affective pivot points in a conversation based on its Hilbert–Huang spectrum, which has close ties to the Fourier transform, is introduced. Finally, given that it is highly desirable to track the sentiment shifts of a Twitter conversation while it unfolds, an adaptive scheme is presented for approximating the window sizes obtained by the offline methodology. As a concrete example, the abovementioned methodologies are applied to three recent long Twitter discussions and the results are analyzed.

ACS Style

Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas; Spyros Sioutas. Discovering sentiment potential in Twitter conversations with Hilbert–Huang spectrum. Evolving Systems 2020, 12, 3 -17.

AMA Style

Georgios Drakopoulos, Andreas Kanavos, Phivos Mylonas, Spyros Sioutas. Discovering sentiment potential in Twitter conversations with Hilbert–Huang spectrum. Evolving Systems. 2020; 12 (1):3-17.

Chicago/Turabian Style

Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas; Spyros Sioutas. 2020. "Discovering sentiment potential in Twitter conversations with Hilbert–Huang spectrum." Evolving Systems 12, no. 1: 3-17.

Conference paper
Published: 29 May 2020 in Advances in Experimental Medicine and Biology
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Mobile health applications are steadily gaining momentum in the modern world given the omnipresence of various mobile or Wi-Fi connections. Given that the bandwidth of these connections increases over time, especially in conjunction with advanced modulation and error-correction codes, whereas the latency drops, the cooperation between mobile applications becomes gradually easier. This translates to reduced computational burden and heat dissipation for each isolated device but at the expense of increased privacy risks. This chapter presents a configurable and scalable edge computing architecture for cooperative digital health mobile applications.

ACS Style

Georgios Drakopoulos; Phivos Mylonas; Spyros Sioutas. An Architecture for Cooperative Mobile Health Applications. Advances in Experimental Medicine and Biology 2020, 1194, 23 -29.

AMA Style

Georgios Drakopoulos, Phivos Mylonas, Spyros Sioutas. An Architecture for Cooperative Mobile Health Applications. Advances in Experimental Medicine and Biology. 2020; 1194 ():23-29.

Chicago/Turabian Style

Georgios Drakopoulos; Phivos Mylonas; Spyros Sioutas. 2020. "An Architecture for Cooperative Mobile Health Applications." Advances in Experimental Medicine and Biology 1194, no. : 23-29.

Conference paper
Published: 29 May 2020 in Collaboration in a Hyperconnected World
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How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from well-established principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.

ACS Style

Georgios Drakopoulos; Ioanna Giannoukou; Phivos Mylonas; Spyros Sioutas. The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics. Collaboration in a Hyperconnected World 2020, 200 -212.

AMA Style

Georgios Drakopoulos, Ioanna Giannoukou, Phivos Mylonas, Spyros Sioutas. The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics. Collaboration in a Hyperconnected World. 2020; ():200-212.

Chicago/Turabian Style

Georgios Drakopoulos; Ioanna Giannoukou; Phivos Mylonas; Spyros Sioutas. 2020. "The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics." Collaboration in a Hyperconnected World , no. : 200-212.

Conference paper
Published: 29 May 2020 in Advances in Experimental Medicine and Biology
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In this paper we present an approach toward human action detection for activities of daily living (ADLs) that uses a convolutional neural network (CNN). The network is trained on discrete Fourier transform (DFT) images that result from raw sensor readings, i.e., each human action is ultimately described by an image. More specifically, we work using 3D skeletal positions of human joints, which originate from processing of raw RGB sequences enhanced by depth information. The motion of each joint may be described by a combination of three 1D signals, representing its coefficients into a 3D Euclidean space. All such signals from a set of human joints are concatenated to form an image, which is then transformed by DFT and is used for training and evaluation of a CNN. We evaluate our approach using a publicly available challenging dataset of human actions that may involve one or more body parts simultaneously and for two sets of actions which resemble common ADLs.

ACS Style

Eirini Mathe; Apostolos Maniatis; Evaggelos Spyrou; Phivos Mylonas. A Deep Learning Approach for Human Action Recognition Using Skeletal Information. Advances in Experimental Medicine and Biology 2020, 1194, 105 -114.

AMA Style

Eirini Mathe, Apostolos Maniatis, Evaggelos Spyrou, Phivos Mylonas. A Deep Learning Approach for Human Action Recognition Using Skeletal Information. Advances in Experimental Medicine and Biology. 2020; 1194 ():105-114.

Chicago/Turabian Style

Eirini Mathe; Apostolos Maniatis; Evaggelos Spyrou; Phivos Mylonas. 2020. "A Deep Learning Approach for Human Action Recognition Using Skeletal Information." Advances in Experimental Medicine and Biology 1194, no. : 105-114.

Journal article
Published: 24 March 2020 in Algorithms
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At the dawn of the 10V or big data data era, there are a considerable number of sources such as smart phones, IoT devices, social media, smart city sensors, as well as the health care system, all of which constitute but a small portion of the data lakes feeding the entire big data ecosystem. This 10V data growth poses two primary challenges, namely storing and processing. Concerning the latter, new frameworks have been developed including distributed platforms such as the Hadoop ecosystem. Classification is a major machine learning task typically executed on distributed platforms and as a consequence many algorithmic techniques have been developed tailored for these platforms. This article extensively relies in two ways on classifiers implemented in MLlib, the main machine learning library for the Hadoop ecosystem. First, a vast number of classifiers is applied to two datasets, namely Higgs and PAMAP. Second, a two-step classification is ab ovo performed to the same datasets. Specifically, the singular value decomposition of the data matrix determines first a set of transformed attributes which in turn drive the classifiers of MLlib. The twofold purpose of the proposed architecture is to reduce complexity while maintaining a similar if not better level of the metrics of accuracy, recall, and F 1 . The intuition behind this approach stems from the engineering principle of breaking down complex problems to simpler and more manageable tasks. The experiments based on the same Spark cluster indicate that the proposed architecture outperforms the individual classifiers with respect to both complexity and the abovementioned metrics.

ACS Style

Athanasios Alexopoulos; Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas; Gerasimos Vonitsanos. Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark. Algorithms 2020, 13, 71 .

AMA Style

Athanasios Alexopoulos, Georgios Drakopoulos, Andreas Kanavos, Phivos Mylonas, Gerasimos Vonitsanos. Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark. Algorithms. 2020; 13 (3):71.

Chicago/Turabian Style

Athanasios Alexopoulos; Georgios Drakopoulos; Andreas Kanavos; Phivos Mylonas; Gerasimos Vonitsanos. 2020. "Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark." Algorithms 13, no. 3: 71.

Journal article
Published: 10 March 2020 in Neural Computing and Applications
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In communication networks resilience or structural coherency, namely the ability to maintain total connectivity even after some data links are lost for an indefinite time, is a major design consideration. Evaluating resilience is a computationally challenging task since it often requires examining a prohibitively high number of connections or of node combinations, depending on the structural coherency definition. In order to study resilience, communication systems are treated in an abstract level as graphs where the existence of an edge depends heavily on the local connectivity properties between the two nodes. Once the graph is derived, its resilience is evaluated by a tensor stack network (TSN). TSN is an emerging deep learning classification methodology for big data which can be expressed either as stacked vectors or as matrices, such as images or oversampled data from multiple-input and multiple-output digital communication systems. As their collective name suggests, the architecture of TSNs is based on tensors, namely higher-dimensional vectors, which simulate the simultaneous training of a cluster of ordinary multilayer feedforward neural networks (FFNNs). In the TSN structure the FFNNs are also interconnected and, thus, at certain steps of the training process they learn from the errors of each other. An additional advantage of the TSN training process is that it is regularized, resulting in parsimonious classifiers. The TSNs are trained to evaluate how resilient a graph is, where the real structural strength is assessed through three established resiliency metrics, namely the Estrada index, the odd Estrada index, and the clustering coefficient. Although the approach of modelling the communication system exclusively in structural terms is function oblivious, it can be applied to virtually any type of communication network independently of the underlying technology. The classification achieved by four configurations of TSNs is evaluated through six metrics, including the F1 metric as well as the type I and type II errors, derived from the corresponding contingency tables. Moreover, the effects of sparsifying the synaptic weights resulting from the training process are explored for various thresholds. Results indicate that the proposed method achieves a very high accuracy, while it is considerably faster than the computation of each of the three resilience metrics. Concerning sparsification, after a threshold the accuracy drops, meaning that the TSNs cannot be further sparsified. Thus, their training is very efficient in that respect.

ACS Style

Georgios Drakopoulos; Phivos Mylonas. Evaluating graph resilience with tensor stack networks: a Keras implementation. Neural Computing and Applications 2020, 32, 4161 -4176.

AMA Style

Georgios Drakopoulos, Phivos Mylonas. Evaluating graph resilience with tensor stack networks: a Keras implementation. Neural Computing and Applications. 2020; 32 (9):4161-4176.

Chicago/Turabian Style

Georgios Drakopoulos; Phivos Mylonas. 2020. "Evaluating graph resilience with tensor stack networks: a Keras implementation." Neural Computing and Applications 32, no. 9: 4161-4176.

Journal article
Published: 04 March 2020 in Algorithms
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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 inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

ACS Style

Georgios Alexandridis; Yorghos Voutos; Phivos Mylonas; George Caridakis. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms 2020, 13, 59 .

AMA Style

Georgios Alexandridis, Yorghos Voutos, Phivos Mylonas, George Caridakis. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms. 2020; 13 (3):59.

Chicago/Turabian Style

Georgios Alexandridis; Yorghos Voutos; Phivos Mylonas; George Caridakis. 2020. "A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends." Algorithms 13, no. 3: 59.

Editorial
Published: 30 June 2019 in Journal of Imaging
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In recent years, following the tremendous growth of the Web, extremely large amounts of digital multimedia content are being produced every day and are shared online mainly through several newly emerged channels, such as social networks

ACS Style

Phivos Mylonas; Evaggelos Spyrou. Introduction to the Special Issue on Image-Based Information Retrieval from the Web. Journal of Imaging 2019, 5, 62 .

AMA Style

Phivos Mylonas, Evaggelos Spyrou. Introduction to the Special Issue on Image-Based Information Retrieval from the Web. Journal of Imaging. 2019; 5 (7):62.

Chicago/Turabian Style

Phivos Mylonas; Evaggelos Spyrou. 2019. "Introduction to the Special Issue on Image-Based Information Retrieval from the Web." Journal of Imaging 5, no. 7: 62.

Journal article
Published: 13 June 2019 in Sustainability
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The term intelligent agriculture, or smart farming, typically involves the incorporation of computer science and information technologies into the traditional notion of farming. The latter utilizes plain machinery and equipment used for many decades and the only significant improvement made over the years has been the introduction of automation in the process. Still, at the beginning of the new century, there are ways and room for further vast improvements. More specifically, the low cost of rather advanced sensors and small-scale devices, now even connected to the Internet of Things (IoT), allowed them to be introduced in the process and used within agricultural production systems. New and emerging technologies and methodologies, like the utilization of cheap network storage, are expected to advance this development. In this sense, the main goals of this paper may be summarized as follows: (a) To identify, group, and acknowledge the current state-of-the-art research knowledge about intelligent agriculture approaches, (b) to categorize them according to meaningful data sources categories, and (c) to describe current efficient data processing and utilization aspects from the perspective of the main trends in the field.

ACS Style

Yorghos Voutos; Phivos Mylonas; John Katheniotis; Anastasia Sofou. A Survey on Intelligent Agricultural Information Handling Methodologies. Sustainability 2019, 11, 3278 .

AMA Style

Yorghos Voutos, Phivos Mylonas, John Katheniotis, Anastasia Sofou. A Survey on Intelligent Agricultural Information Handling Methodologies. Sustainability. 2019; 11 (12):3278.

Chicago/Turabian Style

Yorghos Voutos; Phivos Mylonas; John Katheniotis; Anastasia Sofou. 2019. "A Survey on Intelligent Agricultural Information Handling Methodologies." Sustainability 11, no. 12: 3278.

Journal article
Published: 20 May 2019 in Algorithms
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In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works.

ACS Style

Georgios Paraskevopoulos; Evaggelos Spyrou; Dimitrios Sgouropoulos; Theodoros Giannakopoulos; Phivos Mylonas. Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data. Algorithms 2019, 12, 108 .

AMA Style

Georgios Paraskevopoulos, Evaggelos Spyrou, Dimitrios Sgouropoulos, Theodoros Giannakopoulos, Phivos Mylonas. Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data. Algorithms. 2019; 12 (5):108.

Chicago/Turabian Style

Georgios Paraskevopoulos; Evaggelos Spyrou; Dimitrios Sgouropoulos; Theodoros Giannakopoulos; Phivos Mylonas. 2019. "Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data." Algorithms 12, no. 5: 108.