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Dimitrios Tzovaras
Centre for Research & Technology, Information Technologies Institute, 57001 Thessaloniki, Greece

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Short Biography

Dimitrios Tzovaras is the Director of CERTH and a Senior Researcher (Grade A) at CERTH/ITI. He is also leading the Virtual & Augmented Reality Lab of CERTH/ITI. He received the Diploma in Electrical Engineering and the PhD in 2D and 3D Image Compression from the Aristotle University of Thessaloniki, Greece, in 1992 and 1997, respectively. His main research interests include network and visual analytics for network security, computer security, data fusion, biometric security, virtual reality, machine learning and artificial intelligence.

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Journal article
Published: 20 August 2021 in Sustainability
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Building retrofitting is seen as an efficient method for improving a building’s energy performance. On the other hand, when historical buildings are considered for this procedure, retrofitting gets more complicated. As historical buildings typically consist of low-performance building and energy systems, energy retrofits can be highly beneficial. However, not every retrofit technology can be installed in a historical building. In this paper, the study carried out for the implementation of Building-Integrated Photovoltaics (BIPV) solutions in the Historic Centre of Évora is provided, within the framework of the European project POCITYF (Project H2020). The study took into consideration all the observations of the Regional Directorate of Culture of Évora and the administration of the involved schools (including the Association of Parents), the needs of the Municipality of Évora, and the capabilities of technology developers ONYX and Tegola. The proposed solutions aim at fulfilling all the guidelines for preserving the historic centre and achieving the positivity metrics agreed with the European Commission on the challenging and indispensable path to the decarbonisation of European cities.

ACS Style

Georgios Tsoumanis; João Formiga; Nuno Bilo; Panagiotis Tsarchopoulos; Dimosthenis Ioannidis; Dimitrios Tzovaras. The Smart Evolution of Historical Cities: Integrated Innovative Solutions Supporting the Energy Transition while Respecting Cultural Heritage. Sustainability 2021, 13, 9358 .

AMA Style

Georgios Tsoumanis, João Formiga, Nuno Bilo, Panagiotis Tsarchopoulos, Dimosthenis Ioannidis, Dimitrios Tzovaras. The Smart Evolution of Historical Cities: Integrated Innovative Solutions Supporting the Energy Transition while Respecting Cultural Heritage. Sustainability. 2021; 13 (16):9358.

Chicago/Turabian Style

Georgios Tsoumanis; João Formiga; Nuno Bilo; Panagiotis Tsarchopoulos; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "The Smart Evolution of Historical Cities: Integrated Innovative Solutions Supporting the Energy Transition while Respecting Cultural Heritage." Sustainability 13, no. 16: 9358.

Long paper
Published: 20 July 2021 in Universal Access in the Information Society
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Pervasive technologies such as Artificial Intelligence, Virtual Reality and the Internet of Things, despite their great potential for improved workability and well-being of older workers, entail wide ethical concerns. Aligned with these considerations we emphasize the need to present from the viewpoint of ethics the risks of personalized ICT solutions that aim to remedy health and support the well-being of the ageing population at workplaces. The ethical boundaries of digital technologies are opaque. The main motivation is to cope with the uncertainties of workplaces’ digitization and develop an ethics framework, termed SmartFrameWorK, for personalized health support through ICT tools at workplace environments. SmartFrameWorK is built upon a five-dimensional approach of ethics norms: autonomy, privacy, transparency, trustworthiness and accountability to incite trust in digital workplace technologies. A typology underpins these principles and guides the ethical decision-making process with regard to older worker particular needs, context, data type-related risks and digital tools’ use throughout their lifecycle. Risk analysis of pervasive technology use and multimodal data collection, highlighted the imperative for ethically aware practices for older workers' activity and behaviour monitoring. The SmartFrameWorK methodology has been applied in a case study to provide evidence that personalized digital services could elicit trust in users through a well-defined framework. Ethics compliance is a dynamic process from participants’ engagement to data management. Defining ethical determinants is pivotal towards building trust and reinforcing better workability and well-being in older workers.

ACS Style

Sofia Segkouli; Dimitrios Giakoumis; Konstantinos Votis; Andreas Triantafyllidis; Ioannis Paliokas; Dimitrios Tzovaras. Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness. Universal Access in the Information Society 2021, 1 -13.

AMA Style

Sofia Segkouli, Dimitrios Giakoumis, Konstantinos Votis, Andreas Triantafyllidis, Ioannis Paliokas, Dimitrios Tzovaras. Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness. Universal Access in the Information Society. 2021; ():1-13.

Chicago/Turabian Style

Sofia Segkouli; Dimitrios Giakoumis; Konstantinos Votis; Andreas Triantafyllidis; Ioannis Paliokas; Dimitrios Tzovaras. 2021. "Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness." Universal Access in the Information Society , no. : 1-13.

Journal article
Published: 17 July 2021 in Energies
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In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

ACS Style

Angelina Bintoudi; Napoleon Bezas; Lampros Zyglakis; Georgios Isaioglou; Christos Timplalexis; Paschalis Gkaidatzis; Athanasios Tryferidis; Dimosthenis Ioannidis; Dimitrios Tzovaras. Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration. Energies 2021, 14, 4315 .

AMA Style

Angelina Bintoudi, Napoleon Bezas, Lampros Zyglakis, Georgios Isaioglou, Christos Timplalexis, Paschalis Gkaidatzis, Athanasios Tryferidis, Dimosthenis Ioannidis, Dimitrios Tzovaras. Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration. Energies. 2021; 14 (14):4315.

Chicago/Turabian Style

Angelina Bintoudi; Napoleon Bezas; Lampros Zyglakis; Georgios Isaioglou; Christos Timplalexis; Paschalis Gkaidatzis; Athanasios Tryferidis; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration." Energies 14, no. 14: 4315.

Conference paper
Published: 01 July 2021 in Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
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Over the last decade, the volume of videos available on the web has increased exponentially. In order to help users cope with the ever-growing video volume, recommendation systems have emerged that can provide personalized suggestions to users based on their past preferences and relevant online metrics. However, such approaches require user profiling, which raises privacy issues while often providing delayed suggestions as various metrics have to be firstly collected such as ratings and number of views. In this paper, we propose a system specifically targeting video content generated in a conference event, where a series of talks and presentations are held and a separate video for each is recorded. Through audience analysis, our system is able to predict the online views of each video and thus recommend the most popular videos to users. This way, online users don’t have to search through all the videos of a conference event thus saving time while not missing the most impactful videos. The proposed system employs several complementary techniques for audience analysis based on video and audio streams. Experimental evaluation of real data demonstrates the potential of the proposed approach.

ACS Style

Alexandros Vrochidis; Nikolaos Dimitriou; Stelios Krinidis; Savvas Panagiotidis; Stathis Parcharidis; Dimitrios Tzovaras. A Multi-modal Audience Analysis System for Predicting Popularity of Online Videos. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference 2021, 465 -476.

AMA Style

Alexandros Vrochidis, Nikolaos Dimitriou, Stelios Krinidis, Savvas Panagiotidis, Stathis Parcharidis, Dimitrios Tzovaras. A Multi-modal Audience Analysis System for Predicting Popularity of Online Videos. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. 2021; ():465-476.

Chicago/Turabian Style

Alexandros Vrochidis; Nikolaos Dimitriou; Stelios Krinidis; Savvas Panagiotidis; Stathis Parcharidis; Dimitrios Tzovaras. 2021. "A Multi-modal Audience Analysis System for Predicting Popularity of Online Videos." Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference , no. : 465-476.

Research article
Published: 27 June 2021 in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
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No energy-saving actions are implied without maintaining comfortable levels for the residents, as lack of comfort results in stress while threatening the occupants health and well-being. In this paper, a novel algorithm for the estimation of individual metabolic rate and comfort level is introduced. A Genetic Algorithm is utilized for the metabolic rate computation of thermal comfort by eradicating all speculative factors, while creating a personal thermal comfort evaluator. Based on the occupants feedback, the subjective personal factors of thermal comfort (clothing insulation, metabolic rate) are estimated, generating a personal thermal comfort profile. Therefore, the proposed approach can be adapted to create the resident’s personal preferences to achieve accurate comfort level estimation. Ultimately, the proposed algorithm is evaluated against real-life indoor sensor data and users’ feedback, while the experimental results illustrate the efficiency of the proposed system. The Genetic algorithm succeeds 100% in finding the optimal metabolic rate solution while improving the thermal comfort estimation error. The thermal comfort profile is 98% accurate compared to a solution based on ASHRAE tables that has 73% of accuracy.

ACS Style

Asimina Dimara; Christos-Nikolaos Anagnostopoulos; Stelios Krinidis; Dimitrios Tzovaras. Personalized thermal comfort modeling through genetic algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2021, 1 -22.

AMA Style

Asimina Dimara, Christos-Nikolaos Anagnostopoulos, Stelios Krinidis, Dimitrios Tzovaras. Personalized thermal comfort modeling through genetic algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2021; ():1-22.

Chicago/Turabian Style

Asimina Dimara; Christos-Nikolaos Anagnostopoulos; Stelios Krinidis; Dimitrios Tzovaras. 2021. "Personalized thermal comfort modeling through genetic algorithm." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects , no. : 1-22.

Journal article
Published: 25 June 2021 in Multimedia Tools and Applications
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With the increasing amounts of electronic health data being constantly generated in medical examinations and by sensors and mobile applications, data visualization methods can assist medical professionals and researchers in exploring and making sense of the data. Two important challenges faced by data visualization are large data volume and protection of sensitive data. In this paper, we propose a graph-based method that allows the exploration of a patient dataset, while also naturally allowing the summarization of large amounts of data, making it applicable to large datasets and sensitive data. A graph is constructed from the raw data, encoding local similarities among patients, and is visualized on the screen, producing a visual map of the patient distribution. Multidimensional glyphs are put in place of the nodes, revealing the properties that characterize each graph area. The graph construction method is extended to an incremental scheme, allowing federated graph formation. The proposed method is demonstrated in three use cases, regarding frailty in older adults, Sjögren’s Syndrome patients, and a large-size diabetes dataset.

ACS Style

Ilias Kalamaras; Konstantinos Glykos; Vasilis Megalooikonomou; Konstantinos Votis; Dimitrios Tzovaras. Graph-based visualization of sensitive medical data. Multimedia Tools and Applications 2021, 1 -28.

AMA Style

Ilias Kalamaras, Konstantinos Glykos, Vasilis Megalooikonomou, Konstantinos Votis, Dimitrios Tzovaras. Graph-based visualization of sensitive medical data. Multimedia Tools and Applications. 2021; ():1-28.

Chicago/Turabian Style

Ilias Kalamaras; Konstantinos Glykos; Vasilis Megalooikonomou; Konstantinos Votis; Dimitrios Tzovaras. 2021. "Graph-based visualization of sensitive medical data." Multimedia Tools and Applications , no. : 1-28.

Journal article
Published: 24 June 2021 in Journal of Cleaner Production
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Extracting and exploiting the flexibility of electric demand has been shown to reduce the needs of network upgrades and generation capacity increases. Demand Response (DR) in considered as one of the few available solutions for accessing the untapped energy potential of small and medium customers. Over the past decade, rigorous research has produced significant results in optimally dispatching DR in an attempt to maximize flexibility extraction. However, the vast majority of works assumes a “happy path” scenario in which DR requests are always successfully completed. Hence, there is a large gap in the literature that fails to account for non-deterministic factors that manifest in practical deployments, e.g., the stochasticity of end-user behavior that can drastically influence the DR's outcomes. Investing on that notion, a novel, distributed, multi-agent system (MAS) that aggregates consumers and prosumers and handles automatically OpenADR-compliant DR requests is introduced, following virtual power plant (VPP) principles. Agents of the proposed MAS are able to service DR events originating from a higher level, e.g., Aggregators or Utilities, and optimally dispatch them to their assigned customers. The proposed framework ensures 100% DR success rate, compared to conventional methods, by not only optimally exploiting aggregated flexibility through a combination of clustering and optimisation engines, but also through a dynamic, bi-directional DR matchmaking process that can mitigate observed deviations both internally (intra), as well as, externally (inter) in real-time. Via experimentation, we demonstrate the framework's efficiency in ensuring technical DR fault-tolerance along with its ability to deliver savings of up to 3 orders of magnitude to Aggregators and the customers serving the DR requests.

ACS Style

Christos Patsonakis; Angelina D. Bintoudi; Konstantinos Kostopoulos; Ioannis Koskinas; Apostolos C. Tsolakis; Dimosthenis Ioannidis; Dimitrios Tzovaras. Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation. Journal of Cleaner Production 2021, 314, 127844 .

AMA Style

Christos Patsonakis, Angelina D. Bintoudi, Konstantinos Kostopoulos, Ioannis Koskinas, Apostolos C. Tsolakis, Dimosthenis Ioannidis, Dimitrios Tzovaras. Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation. Journal of Cleaner Production. 2021; 314 ():127844.

Chicago/Turabian Style

Christos Patsonakis; Angelina D. Bintoudi; Konstantinos Kostopoulos; Ioannis Koskinas; Apostolos C. Tsolakis; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation." Journal of Cleaner Production 314, no. : 127844.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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Unmanned Aerial Vehicles (UAVs) have become a major part of everyday life, as well as an emerging research field, by establishing their versatility in a variety of applications. Nevertheless, this rapid spread of UAVs reputation has provoked serious security issues that can probably affect homeland security. Defence communities have started to investigate large field-of-view sensor-based methods to enable various civil protection applications, including the detection and localisation of flying threat objects. Counter-UAV (c-UAV) detection challenges may be granted from a fusion of sensors to enhance the confidence of flying threats identification. The real-time monitoring of the environment is absolutely rigorous and demands accurate methods to detect promptly the occurrence of harmful conditions. Deep learning (DL) based techniques are capable of tackling the challenges that are associated with generic objects detection and explicitly UAV identification. In this paper, we present a novel multimodal DL methodology that combines data from individual unimodal approaches that are associated with UAV detection. Specifically, this work aims to identify and classify potential targets of UAVs based on fusion methods in two different cases of operational environments, i.e. rural and urban scenarios. A dedicated architecture is designed based on the development of deep neural networks (DNNs) frameworks that has been trained and validated employing real UAV flights scenarios. The proposed approach has achieved prominent detection accuracies over different background environments, exhibiting potential employment even in major defence applications.

ACS Style

Eleni Diamantidou; Antonios Lalas; Konstaninos Votis; Dimitrios Tzovaras. A Multimodal AI-Leveraged Counter-UAV Framework for Diverse Environments. Collaboration in a Hyperconnected World 2021, 228 -239.

AMA Style

Eleni Diamantidou, Antonios Lalas, Konstaninos Votis, Dimitrios Tzovaras. A Multimodal AI-Leveraged Counter-UAV Framework for Diverse Environments. Collaboration in a Hyperconnected World. 2021; ():228-239.

Chicago/Turabian Style

Eleni Diamantidou; Antonios Lalas; Konstaninos Votis; Dimitrios Tzovaras. 2021. "A Multimodal AI-Leveraged Counter-UAV Framework for Diverse Environments." Collaboration in a Hyperconnected World , no. : 228-239.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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Autonomous Vehicles (AVs) can potentially reduce the accident risk while a human is driving. They can also improve the public transportation by connecting city centers with main mass transit systems. The development of technologies that can provide a sense of security to the passenger when the driver is missing remains a challenging task. Moreover, such technologies are forced to adopt to the new reality formed by the COVID-19 pandemic, as it has created significant restrictions to passenger mobility through public transportation. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase autonomy of non-fully autonomous people such as kids, grandparents and disabled people. The proposed real-time service, can identify family members via facial characteristics and efficiently ignore face masks, while providing notifications for their condition to their supervisor relatives. The envisioned AI-supported security framework, apart from enhancing the trust to autonomous mobility, could be advantageous in other applications also related to domestic security and defense.

ACS Style

Dimitris Tsiktsiris; Antonios Lalas; Minas Dasygenis; Konstantinos Votis; Dimitrios Tzovaras. Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19. Collaboration in a Hyperconnected World 2021, 145 -154.

AMA Style

Dimitris Tsiktsiris, Antonios Lalas, Minas Dasygenis, Konstantinos Votis, Dimitrios Tzovaras. Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19. Collaboration in a Hyperconnected World. 2021; ():145-154.

Chicago/Turabian Style

Dimitris Tsiktsiris; Antonios Lalas; Minas Dasygenis; Konstantinos Votis; Dimitrios Tzovaras. 2021. "Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19." Collaboration in a Hyperconnected World , no. : 145-154.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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Human activity recognition is a challenging field that grabbed considerable research attention in the last decade. Two types of models can be used for such predictions, those which use visual data and those which use data from inertial sensors. To improve the classification algorithms in the sensor category, a new dataset has been created, targeting more realistic activities, during which the user may be more prompt to receive and act upon a recommendation. Contrary to previous similar datasets, which were collected with the device in the user’s pockets or strapped to their waist, the introduced dataset presents activities during which the user is looking on the screen, and thus most likely interacts with the device. The dataset from an initial sample of 31 participants was gathered using a mobile application that prompted users to do 10 different activities following specific guidelines. Finally, towards evaluating the resulting data, a brief classification benchmarking was performed with two other datasets (i.e., WISDM and Actitracker datasets) by employing a Convolutional Neural Network model. The results acquired demonstrate a promising performance of the model tested, as well as a high quality of the dataset created, which is available online on Zenodo.

ACS Style

Alexandros Vrochidis; Vasileios G. Vasilopoulos; Konstantinos Peppas; Valia Dimaridou; Iordanis Makaratzis; Apostolos C. Tsolakis; Stelios Krinidis; Dimitrios Tzovaras. A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data. Collaboration in a Hyperconnected World 2021, 327 -339.

AMA Style

Alexandros Vrochidis, Vasileios G. Vasilopoulos, Konstantinos Peppas, Valia Dimaridou, Iordanis Makaratzis, Apostolos C. Tsolakis, Stelios Krinidis, Dimitrios Tzovaras. A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data. Collaboration in a Hyperconnected World. 2021; ():327-339.

Chicago/Turabian Style

Alexandros Vrochidis; Vasileios G. Vasilopoulos; Konstantinos Peppas; Valia Dimaridou; Iordanis Makaratzis; Apostolos C. Tsolakis; Stelios Krinidis; Dimitrios Tzovaras. 2021. "A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data." Collaboration in a Hyperconnected World , no. : 327-339.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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As energy markets become more and more dynamic, the importance of price forecasting has gained a lot of attention over the last few years. Considering also the introduction of new business models and roles, such as Aggregators and energy flexibility traders, in the constantly evolving energy landscape which follows the general opening of the European electricity markets, the need for anticipating energy price trends and flows holds significant business value. On top of that, the exponential renewable energy sources penetration, adds to the challenges introduced to this dynamic scheme of things. Given their volatile and intermittent nature, supply-demand imbalance can reach critical margins, threatening the overall system stability. In the scope of reducing the power imbalances, a forecast for the imbalance volume will be beneficial either from the perspective of the system operator that could minimise mitigation costs, or the market participants that could target extreme prices for maximising their profit, while effectively managing their risks. The development of a deep learning algorithm for the prediction of the net imbalance volume in the UK market is proposed in this paper in comparison with a common but widely used machine learning approach, namely a gradient boosting trees regression model. The variables which contributed the most on those models were mainly the historical values of net imbalance volume. The deep neural network returns a Root mean squared error (RMSE) and Mean Absolute Error (MAE) equal to 200 and 152 MWh in a range of values between [−1.5, 2.0] GWh, respectively, the gradient boosting trees model has an RMSE and MAE equal to 203 and 154 MWh, in contrast to an ARIMA model that has RMSE and MAE equal to 226 and 173 MWh.

ACS Style

Elpiniki Makri; Ioannis Koskinas; Apostolos C. Tsolakis; Dimosthenis Ioannidis; Dimitrios Tzovaras. Short Term Net Imbalance Volume Forecasting Through Machine and Deep Learning: A UK Case Study. Collaboration in a Hyperconnected World 2021, 377 -389.

AMA Style

Elpiniki Makri, Ioannis Koskinas, Apostolos C. Tsolakis, Dimosthenis Ioannidis, Dimitrios Tzovaras. Short Term Net Imbalance Volume Forecasting Through Machine and Deep Learning: A UK Case Study. Collaboration in a Hyperconnected World. 2021; ():377-389.

Chicago/Turabian Style

Elpiniki Makri; Ioannis Koskinas; Apostolos C. Tsolakis; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "Short Term Net Imbalance Volume Forecasting Through Machine and Deep Learning: A UK Case Study." Collaboration in a Hyperconnected World , no. : 377-389.

Conference paper
Published: 22 June 2021 in Collaboration in a Hyperconnected World
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Sustainable energy is hands down one of the biggest challenges of our times. As the EU sets its focus to reach its 2030 and 2050 goals, the importance of energy efficiency for energy consumers/prosumers becomes prevalent. Over the years, a lot of different approaches have been followed to engage end-users and affect energy-related occupant behaviour towards improving energy efficiency results and long term behaviour changes. This work presents the SIT4Energy user-centered approach for tertiary buildings that delivers an end-to-end solution that takes into consideration a set of tools and models for successfully engaging and affecting the end-user’s energy-related behaviour. Starting from appropriate user profiling models for energy-related behaviour models and an explainable recommendation engine, to on the fly human activity tracking and micro-moments detection on mobile devices, a set of recommendations are delivered to the end-users through a mobile device, presenting valuable information with user-tailored context and on the optimal timing. The overall solution is clearly documented, whereas real-life results are presented from the deployment in offices in a university building. From the evaluation performed it is clearly depicted that a positive impact has been achieved both in terms of energy efficiency as well as energy-related behaviour.

ACS Style

Apostolos C. Tsolakis; George Tsakirakis; Vasileios G. Vasilopoulos; Konstantinos Peppas; Charisios Zafeiris; Iordanis Makaratzis; Ana Grimaldo; Stelios Krinidis; Jasminko Novak; George Bravos; Dimitrios Tzovaras. Improving Energy Efficiency in Tertiary Buildings Through User-Driven Recommendations Delivered on Optimal Micro-moments. Collaboration in a Hyperconnected World 2021, 352 -363.

AMA Style

Apostolos C. Tsolakis, George Tsakirakis, Vasileios G. Vasilopoulos, Konstantinos Peppas, Charisios Zafeiris, Iordanis Makaratzis, Ana Grimaldo, Stelios Krinidis, Jasminko Novak, George Bravos, Dimitrios Tzovaras. Improving Energy Efficiency in Tertiary Buildings Through User-Driven Recommendations Delivered on Optimal Micro-moments. Collaboration in a Hyperconnected World. 2021; ():352-363.

Chicago/Turabian Style

Apostolos C. Tsolakis; George Tsakirakis; Vasileios G. Vasilopoulos; Konstantinos Peppas; Charisios Zafeiris; Iordanis Makaratzis; Ana Grimaldo; Stelios Krinidis; Jasminko Novak; George Bravos; Dimitrios Tzovaras. 2021. "Improving Energy Efficiency in Tertiary Buildings Through User-Driven Recommendations Delivered on Optimal Micro-moments." Collaboration in a Hyperconnected World , no. : 352-363.

Journal article
Published: 21 June 2021 in Multimedia Tools and Applications
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Software vulnerabilities constitute a critical threat for cybersecurity analysts in the contemporary society, since the successfully exploited vulnerabilities could harm any system in terms of Confidentiality, Integrity and Availability. Similarly, the characterization of vulnerabilities and the assessment of vulnerability risk is a crucial task for cybersecurity managers regarding the resource management. However, the proliferation of software vulnerabilities causes problems related to the response time of the security experts. For this reason, a methodology based on RAndom k-labELsets (RAkEL) is proposed in this paper in order to estimate software vulnerability characteristics and score from the vulnerability technical description. The proposed methodology aims to a) improve an existing multi-target methodology and b) be integrated in a Cyber Threat Intelligence (CTI) information sharing system. The results, in a dataset containing more than 130000 vulnerabilities, clearly proved that the proposed methodology could improve the existing methodology regarding the estimation of vulnerability characteristics and score.

ACS Style

Georgios Aivatoglou; Mike Anastasiadis; Georgios Spanos; Antonis Voulgaridis; Konstantinos Votis; Dimitrios Tzovaras; Lefteris Angelis. A RAkEL-based methodology to estimate software vulnerability characteristics & score - an application to EU project ECHO. Multimedia Tools and Applications 2021, 1 -21.

AMA Style

Georgios Aivatoglou, Mike Anastasiadis, Georgios Spanos, Antonis Voulgaridis, Konstantinos Votis, Dimitrios Tzovaras, Lefteris Angelis. A RAkEL-based methodology to estimate software vulnerability characteristics & score - an application to EU project ECHO. Multimedia Tools and Applications. 2021; ():1-21.

Chicago/Turabian Style

Georgios Aivatoglou; Mike Anastasiadis; Georgios Spanos; Antonis Voulgaridis; Konstantinos Votis; Dimitrios Tzovaras; Lefteris Angelis. 2021. "A RAkEL-based methodology to estimate software vulnerability characteristics & score - an application to EU project ECHO." Multimedia Tools and Applications , no. : 1-21.

Journal article
Published: 19 June 2021 in Future Internet
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In the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles.

ACS Style

Maria Tsourma; Alexandros Zamichos; Efthymios Efthymiadis; Anastasios Drosou; Dimitrios Tzovaras. An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting. Future Internet 2021, 13, 161 .

AMA Style

Maria Tsourma, Alexandros Zamichos, Efthymios Efthymiadis, Anastasios Drosou, Dimitrios Tzovaras. An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting. Future Internet. 2021; 13 (6):161.

Chicago/Turabian Style

Maria Tsourma; Alexandros Zamichos; Efthymios Efthymiadis; Anastasios Drosou; Dimitrios Tzovaras. 2021. "An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting." Future Internet 13, no. 6: 161.

Journal article
Published: 17 June 2021 in Energies
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Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

ACS Style

Paraskevas Koukaras; Paschalis Gkaidatzis; Napoleon Bezas; Tommaso Bragatto; Federico Carere; Francesca Santori; Marcel Antal; Dimosthenis Ioannidis; Christos Tjortjis; Dimitrios Tzovaras. A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization. Energies 2021, 14, 3599 .

AMA Style

Paraskevas Koukaras, Paschalis Gkaidatzis, Napoleon Bezas, Tommaso Bragatto, Federico Carere, Francesca Santori, Marcel Antal, Dimosthenis Ioannidis, Christos Tjortjis, Dimitrios Tzovaras. A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization. Energies. 2021; 14 (12):3599.

Chicago/Turabian Style

Paraskevas Koukaras; Paschalis Gkaidatzis; Napoleon Bezas; Tommaso Bragatto; Federico Carere; Francesca Santori; Marcel Antal; Dimosthenis Ioannidis; Christos Tjortjis; Dimitrios Tzovaras. 2021. "A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization." Energies 14, no. 12: 3599.

Journal article
Published: 28 May 2021 in Energies
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Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

ACS Style

Nikolaos Kolokas; Dimosthenis Ioannidis; Dimitrios Tzovaras. Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization. Energies 2021, 14, 3162 .

AMA Style

Nikolaos Kolokas, Dimosthenis Ioannidis, Dimitrios Tzovaras. Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization. Energies. 2021; 14 (11):3162.

Chicago/Turabian Style

Nikolaos Kolokas; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization." Energies 14, no. 11: 3162.

Journal article
Published: 27 May 2021 in Sustainability
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The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts.

ACS Style

Theo Lynn; Pierangelo Rosati; Antonia Egli; Stelios Krinidis; Komninos Angelakoglou; Vasileios Sougkakis; Dimitrios Tzovaras; Mohamad Kassem; David Greenwood; Omar Doukari. RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects. Sustainability 2021, 13, 6018 .

AMA Style

Theo Lynn, Pierangelo Rosati, Antonia Egli, Stelios Krinidis, Komninos Angelakoglou, Vasileios Sougkakis, Dimitrios Tzovaras, Mohamad Kassem, David Greenwood, Omar Doukari. RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects. Sustainability. 2021; 13 (11):6018.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Antonia Egli; Stelios Krinidis; Komninos Angelakoglou; Vasileios Sougkakis; Dimitrios Tzovaras; Mohamad Kassem; David Greenwood; Omar Doukari. 2021. "RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects." Sustainability 13, no. 11: 6018.

Journal article
Published: 21 May 2021 in Future Internet
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In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors.

ACS Style

Traianos-Ioannis Theodorou; Alexandros Zamichos; Michalis Skoumperdis; Anna Kougioumtzidou; Kalliopi Tsolaki; Dimitris Papadopoulos; Thanasis Patsios; George Papanikolaou; Athanasios Konstantinidis; Anastasios Drosou; Dimitrios Tzovaras. An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements. Future Internet 2021, 13, 138 .

AMA Style

Traianos-Ioannis Theodorou, Alexandros Zamichos, Michalis Skoumperdis, Anna Kougioumtzidou, Kalliopi Tsolaki, Dimitris Papadopoulos, Thanasis Patsios, George Papanikolaou, Athanasios Konstantinidis, Anastasios Drosou, Dimitrios Tzovaras. An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements. Future Internet. 2021; 13 (6):138.

Chicago/Turabian Style

Traianos-Ioannis Theodorou; Alexandros Zamichos; Michalis Skoumperdis; Anna Kougioumtzidou; Kalliopi Tsolaki; Dimitris Papadopoulos; Thanasis Patsios; George Papanikolaou; Athanasios Konstantinidis; Anastasios Drosou; Dimitrios Tzovaras. 2021. "An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements." Future Internet 13, no. 6: 138.

Article
Published: 15 May 2021 in Software Quality Journal
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Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efficient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that reflects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefly reflect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufficient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the first fully automated, operationalized and sufficiently evaluated security assessment model in the modern literature.

ACS Style

Miltiadis Siavvas; Dionysios Kehagias; Dimitrios Tzovaras; Erol Gelenbe. A hierarchical model for quantifying software security based on static analysis alerts and software metrics. Software Quality Journal 2021, 29, 431 -507.

AMA Style

Miltiadis Siavvas, Dionysios Kehagias, Dimitrios Tzovaras, Erol Gelenbe. A hierarchical model for quantifying software security based on static analysis alerts and software metrics. Software Quality Journal. 2021; 29 (2):431-507.

Chicago/Turabian Style

Miltiadis Siavvas; Dionysios Kehagias; Dimitrios Tzovaras; Erol Gelenbe. 2021. "A hierarchical model for quantifying software security based on static analysis alerts and software metrics." Software Quality Journal 29, no. 2: 431-507.

Journal article
Published: 11 May 2021 in Energies
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As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.

ACS Style

Angelina Bintoudi; Lampros Zyglakis; Apostolos Tsolakis; Paschalis Gkaidatzis; Athanasios Tryferidis; Dimosthenis Ioannidis; Dimitrios Tzovaras. OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration. Energies 2021, 14, 2752 .

AMA Style

Angelina Bintoudi, Lampros Zyglakis, Apostolos Tsolakis, Paschalis Gkaidatzis, Athanasios Tryferidis, Dimosthenis Ioannidis, Dimitrios Tzovaras. OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration. Energies. 2021; 14 (10):2752.

Chicago/Turabian Style

Angelina Bintoudi; Lampros Zyglakis; Apostolos Tsolakis; Paschalis Gkaidatzis; Athanasios Tryferidis; Dimosthenis Ioannidis; Dimitrios Tzovaras. 2021. "OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration." Energies 14, no. 10: 2752.