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162 results on '"Kehagias, Dionysios"'

1. SDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software

Author

Siavvas, Miltiadis, Tsoukalas, Dimitrios, Marantos, Charalambos, Papadopoulos, Lazaros, Lamprakos, Christos, Matei, Oliviu, Strydis, Christos, Siddiqi, Muhammad Ali, Chrobocinski, Philippe, Filus, Katarzyna, Domańska, Joanna, Avgeriou, Paris, Ampatzoglou, Apostolos, Soudris, Dimitrios, Chatzigeorgiou, Alexander, Gelenbe, Erol, Kehagias, Dionysios, and Tzovaras, Dimitrios

Published
2024
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2. A Reference Architecture for Secure IoT

Author

Jankovic, Marija, Kefalakis, Nikos, Tsekeridou, Sofia, Kehagias, Dionysios, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Trajanovic, Miroslav, editor, Filipovic, Nenad, editor, and Zdravkovic, Milan, editor

Published
2024
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7. An Empirical Evaluation of the Usefulness of Word Embedding Techniques in Deep Learning-Based Vulnerability Prediction

Author

Kalouptsoglou, Ilias, Siavvas, Miltiadis, Kehagias, Dionysios, Chatzigeorgiou, Alexandros, Ampatzoglou, Apostolos, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gelenbe, Erol, editor, Jankovic, Marija, editor, Kehagias, Dionysios, editor, Marton, Anna, editor, and Vilmos, Andras, editor

Published
2022
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10. An Approach to Facilitate Visitors’ Engagement with Contemporary Art in a Virtual Museum

Author

Tsita, Christina, Georgiadis, Charalabos, Zampeti, Maria, Papavergou, Evi, Tsiara, Syrago, Pedefoudas, Alexandros, Kehagias, Dionysios, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Moropoulou, Antonia, editor, Georgopoulos, Andreas, editor, Doulamis, Anastasios, editor, Ioannides, Marinos, editor, and Ronchi, Alfredo, editor

Published
2022
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11. Comparing Univariate and Multivariate Time Series Models for Technical Debt Forecasting

Author

Mathioudaki, Maria, Tsoukalas, Dimitrios, Siavvas, Miltiadis, Kehagias, Dionysios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, and Garau, Chiara, editor

Published
2022
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13. A Self-adaptive Approach for Assessing the Criticality of Security-Related Static Analysis Alerts

Author

Siavvas, Miltiadis, Kalouptsoglou, Ilias, Tsoukalas, Dimitrios, Kehagias, Dionysios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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14. Technical Debt Forecasting Based on Deep Learning Techniques

Author

Mathioudaki, Maria, Tsoukalas, Dimitrios, Siavvas, Miltiadis, Kehagias, Dionysios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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15. The SDK4ED Platform for Embedded Software Quality Improvement - Preliminary Overview

Author

Siavvas, Miltiadis, Tsoukalas, Dimitrios, Marantos, Charalampos, Tsintzira, Angeliki-Agathi, Jankovic, Marija, Soudris, Dimitrios, Chatzigeorgiou, Alexander, Kehagias, Dionysios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, Torre, Carmelo Maria, editor, and Karaca, Yeliz, editor

Published
2020
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16. Cross-Project Vulnerability Prediction Based on Software Metrics and Deep Learning

Author

Kalouptsoglou, Ilias, Siavvas, Miltiadis, Tsoukalas, Dimitrios, Kehagias, Dionysios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, Torre, Carmelo Maria, editor, and Karaca, Yeliz, editor

Published
2020
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20. Static Analysis-Based Approaches for Secure Software Development

Author

Siavvas, Miltiadis, Gelenbe, Erol, Kehagias, Dionysios, Tzovaras, Dimitrios, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Gelenbe, Erol, editor, Campegiani, Paolo, editor, Czachórski, Tadeusz, editor, Katsikas, Sokratis K., editor, Komnios, Ioannis, editor, Romano, Luigi, editor, and Tzovaras, Dimitrios, editor

Published
2018
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21. A Multi-objective Data Mining Approach for Road Traffic Prediction

Author

Kalamaras, Ilias, Drosou, Anastasios, Votis, Konstantinos, Kehagias, Dionysios, Tzovaras, Dimitrios, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, and Plagianakos, Vassilis, editor

Published
2018
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25. A practical approach for technical debt prioritization based on class‐level forecasting.

Author

Tsoukalas, Dimitrios, Siavvas, Miltiadis, Kehagias, Dionysios, Ampatzoglou, Apostolos, and Chatzigeorgiou, Alexander

Subjects
FORECASTING, COMPUTER software industry, DATA visualization, EMPIRICAL research, REPAYMENTS
Abstract

Monitoring technical debt (TD) is considered highly important for software companies, as it provides valuable information on the effort required to repay TD and in turn maintain the system. When it comes to TD repayment, however, developers are often overwhelmed with a large volume of TD liabilities that they need to fix, rendering the procedure effort demanding. Hence, prioritizing TD liabilities is of utmost importance for effective TD repayment. Existing approaches rely on the current TD state of the system; however, prioritization would be more efficient by also considering its future evolution. To this end, the present work proposes a practical approach for prioritization of TD liabilities by incorporating information retrieved from TD forecasting techniques, emphasizing on the class‐level granularity to provide highly actionable results. Specifically, the proposed approach considers the change proneness and forecasted TD evolution of software artifacts and combines it with proper visualization techniques, to enable the early identification of classes that are more likely to become unmaintainable. To demonstrate and evaluate the approach, an empirical study is conducted on six real‐world applications. The proposed approach is expected to facilitate developers better plan refactoring activities, in order to manage TD promptly and avoid unforeseen situations long term. [ABSTRACT FROM AUTHOR]

Published
2024
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32. A Virtual Reality Museum to Reinforce the Interpretation of Contemporary Art and Increase the Educational Value of User Experience

Author

Tsita, Christina, primary, Satratzemi, Maya, additional, Pedefoudas, Alexandros, additional, Georgiadis, Charalabos, additional, Zampeti, Maria, additional, Papavergou, Evi, additional, Tsiara, Syrago, additional, Sismanidou, Eleni, additional, Kyriakidis, Petros, additional, Kehagias, Dionysios, additional, and Tzovaras, Dimitrios, additional

Published
2023
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40. Security Monitoring during Software Development: An Industrial Case Study.

Author

Siavvas, Miltiadis, Tsoukalas, Dimitrios, Kalouptsoglou, Ilias, Manganopoulou, Evdoxia, Manolis, Georgios, Kehagias, Dionysios, and Tzovaras, Dimitrios

Subjects
COMPUTER software development, INDUSTRIALIZATION, COMPUTER software security, APPLICATION software, COMPUTER software industry
Abstract

The devastating consequences of successful security breaches that have been observed recently have forced more and more software development enterprises to shift their focus towards building software products that are highly secure (i.e., vulnerability-free) from the ground up. In order to produce secure software applications, appropriate mechanisms are required for enabling project managers and developers to monitor the security level of their products during their development and identify and eliminate vulnerabilities prior to their release. A large number of such mechanisms have been proposed in the literature over the years, but limited attempts with respect to their industrial applicability, relevance, and practicality can be found. To this end, in the present paper, we demonstrate an integrated security platform, the VM4SEC platform, which exhibits cutting-edge solutions for software security monitoring and optimization, based on static and textual source code analysis. The platform was built in a way to satisfy the actual security needs of a real software development company. For this purpose, an industrial case study was conducted in order to identify the current security state of the company and its security needs in order for the employed security mechanisms to be adapted to the specific needs of the company. Based on this analysis, the overall architecture of the platform and the parameters of the selected models and mechanisms were properly defined and demonstrated in the present paper. The purpose of this paper is to showcase how cutting-edge security monitoring and optimization mechanisms can be adapted to the needs of a dedicated company and to be used as a blueprint for constructing similar security monitoring platforms and pipelines. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Security in Computer and Information Sciences

Author

Gelenbe, Erol, Jankovic, Marija, Kehagias, Dionysios, Marton, Anna, and Vilmos, Andras

Subjects
architecture types, artificial intelligence, communication systems, computer crime, computer hardware, computer networks, computer security, computer systems, cryptography, data security, Internet of Things (IoT), network protocols, network security, signal processing, software architecture, software design, software engineering, telecommunication networks, telecommunication systems, bic Book Industry Communication::U Computing & information technology::UR Computer security, bic Book Industry Communication::U Computing & information technology::UN Databases::UNH Information retrieval, bic Book Industry Communication::U Computing & information technology::UK Computer hardware::UKN Network hardware, bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMZ Software Engineering, bic Book Industry Communication::U Computing & information technology::UB Information technology: general issues::UBL Legal aspects of IT, bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general::GPJ Coding theory & cryptology
Abstract

This open access book constitutes the thoroughly refereed proceedings of the Second International Symposium on Computer and Information Sciences, EuroCybersec 2021, held in Nice, France, in October 2021. The 9 papers presented together with 1 invited paper were carefully reviewed and selected from 21 submissions. The papers focus on topics of security of distributed interconnected systems, software systems, Internet of Things, health informatics systems, energy systems, digital cities, digital economy, mobile networks, and the underlying physical and network infrastructures. This is an open access book.

Published
2022
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42. EXA2PRO : A Framework for High Development Productivity on Heterogeneous Computing Systems

Author

Papadopoulos, Lazaros, Soudris, Dimitrios, Kessler, Christoph, Ernstsson, August, Ahlqvist, Johan, Vasilas, Nikos, Papadopoulos, Athanasios I, Seferlis, Panos, Prouveur, Charles, Haefele, Matthieu, Thibault, Samuel, Salamanis, Athanasios, Ioakimidis, Theodoros, Kehagias, Dionysios, Papadopoulos, Lazaros, Soudris, Dimitrios, Kessler, Christoph, Ernstsson, August, Ahlqvist, Johan, Vasilas, Nikos, Papadopoulos, Athanasios I, Seferlis, Panos, Prouveur, Charles, Haefele, Matthieu, Thibault, Samuel, Salamanis, Athanasios, Ioakimidis, Theodoros, and Kehagias, Dionysios

Abstract

Programming upcoming exascale computing systems is expected to be a major challenge. New programming models are required to improve programmability, by hiding the complexity of these systems from application developers. The EXA2PRO programming framework aims at improving developers productivity for applications that target heterogeneous computing systems. It is based on advanced programming models and abstractions that encapsulate low-level platform-specific optimizations and it is supported by a runtime that handles application deployment on heterogeneous nodes. It supports a wide variety of platforms and accelerators (CPU, GPU, FPGA-based Data-Flow Engines), allowing developers to efficiently exploit heterogeneous computing systems, thus enabling more HPC applications to reach exascale computing. The EXA2PRO framework was evaluated using four HPC applications from different domains. By applying the EXA2PRO framework, the applications were automatically deployed and evaluated on a variety of computing architectures, enabling developers to obtain performance results on accelerators, test scalability on MPI clusters and productively investigate the degree by which each application can efficiently use different types of hardware resources., Funding Agencies|European Unions Horizon 2020 research and innovation programme [801015]; National Infrastructures for Research and Technology S.A. (GRNET) [SNIC 2020/13-113, SNIC 2016/5-6]; PRACE (Piz-Daint) [pr114]

Published
2022
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44. TD classifier

Author

Tsoukalas, Dimitrios, primary, Chatzigeorgiou, Alexander, additional, Ampatzoglou, Apostolos, additional, Mittas, Nikolaos, additional, and Kehagias, Dionysios, additional

Published
2022
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46. EXA2PRO: A Framework for High Development Productivity on Heterogeneous Computing Systems

Author

Papadopoulos, Lazaros, primary, Soudris, Dimitrios, additional, Kessler, Christoph, additional, Ernstsson, August, additional, Ahlqvist, Johan, additional, Vasilas, Nikos, additional, Papadopoulos, Athanasios I., additional, Seferlis, Panos, additional, Prouveur, Charles, additional, Haefele, Matthieu, additional, Thibault, Samuel, additional, Salamanis, Athanasios, additional, Ioakimidis, Theodoros, additional, and Kehagias, Dionysios, additional

Published
2022
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48. SDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications

Author

Marantos, Charalampos, primary, Siavvas, Miltiadis, additional, Tsoukalas, Dimitrios, additional, Lamprakos, Christos P., additional, Papadopoulos, Lazaros, additional, Boryszko, Pawel, additional, Filus, Katarzyna, additional, Domanska, Joanna, additional, Ampatzoglou, Apostolos, additional, Chatzigeorgiou, Alexander, additional, Gelenbe, Erol, additional, Kehagias, Dionysios, additional, and Soudris, Dimitrios, additional

Published
2022
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50. An Empirical Evaluation of the Usefulness of Word Embedding Techniques in Deep Learning-based Vulnerability Prediction

Author

Kalouptsoglou, Ilias, Siavvas, Miltiadis, Kehagias, Dionysios, Chatzigeorgiou, Alexandros, and Ampatzoglou, Apostolos

Subjects
Software Security, Vulnerability Prediction, Deep Learning, Natural Language Processing, Word Embedding Vectors
Abstract

Software security is a critical consideration for software developmentcompanies that want to provide their customers with high quality and dependable software. The automated detection of softwarevulnerabilities is a critical aspect in software security. Vulnerability prediction is a mechanism that enables the detection and mitigation of software vulnerabilities early enough in the development cycle. Recently the scientific community has dedicated a lot of eort on the design of Deep learning models based on text mining techniques. Initially, Bag-of-Words was the most promising method but recently more complex models have beenproposed focusing on the sequences of instructions in the sourcecode. Recent research endeavors have started utilizing word embedding vectors, which are widely used in text classification tasks like semantic analysis, for representing the words (i.e., code instructions) in vector format. These vectors could be trained either jointly with the other layers of the neural network, or they can be pre-trained using popular algorithmslike word2vec and fast-text. In this paper, we empirically examinewhether the utilization of word embedding vectors that are pre-trained separately from the vulnerability predictor could lead to more accurate vulnerability prediction models. For the purposes of the present study, a popular vulnerability dataset maintained by NIST was utilized. Theresults of the analysis suggest that pre-training the embedding vectors separately from the neural network leads to better vulnerability predictors with respect to their effectiveness and performance.

Published
2021
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