Συνέδρια

Pateli, A. G., & Mikalef, P. (2017). Configurations explaining the impact of social media on innovation performance. In .

Wetering, R. V. D., Mikalef, P., & Pateli, A. (2017). A strategic alignment model for it flexibility and dynamic capabilities: Toward an assessment tool. In (pp. 1468-1485).

Mikalef, P., & Pateli, A. G. (2017). Social media for open innovation: A study of adoption determinants. In .

Wetering, R. V. D., Mikalef, P., & Pateli, A. (2017). Managing firms’ innovation capabilities through strategically aligning combinative IT and dynamic capabilities. In .

Rill, S., Adolph, S., Drescher, J., Reinel, D., Scheidt, J., Schütz, O., Wogenstein, F., Zicari, R. V., & Korfiatis, N. (2012). A phrase-based opinion list for the German language. In (pp. 305-313).

Mikalef, P., Giannakos, M., & Pateli, A. (2012). Exploring the business potential of social media: An utilitarian and hedonic motivation approach. In (pp. 1-14).

Wu, P. F., Heijden, H. V. D., & Korfiatis, N. T. (2011). The influences of negativity and review quality on the helpfulness of online reviews. In (pp. 3710-3719).

Avlonitis, M. I., Pateli, A. G., & Morfis, I. A. (2009). A stochastic model using self-organization to explore the ICT Industry evolution. In (pp. 80-84). https://doi.org/10.1109/PCI.2009.37

Pateli, A. (2009). Building a decision tree to opt for the structural mode of service innovation alliances in high-tech sectors. In .

Pateli, A., Floros, A., Oikonomou, K., & Magkos, E. (2008). Corfunet: A mesh network providing wireless services at metropolitan level. In (pp. 124-128).

Pateli, A. G., & Giaglis, G. M. (2007). The impact of value on governance decisions for IT-based alliances: Evidence from a joint venture in the wireless networks industry. In . https://doi.org/10.1109/HICSS.2007.539

Korfiatis, N., & Sicilia, M. A. (2007). Social measures and flexible navigation on online contact networks. In . https://doi.org/10.1109/FUZZY.2007.4295565

Poulos, M., Korfiatis, N., & Bokos, G. (2007). Towards the construction of a global bibliometric indicator. In (pp. 920-921).

Pateli, A. G., & Giaglis, G. M. (2006). Governance options for strategic technology alliances in value Webs. In . https://doi.org/10.1109/HICSS.2006.198

Korfiatis, N., & Constantiou, I. (2006). Socially targeted mobile services: Towards an upper level ontology of social roles for mobile environments. In .

Dafoulas, G. A., Pateli, A. G., & Turega, M. (2002). Business-to-employee cooperation support through online job interviews. In (pp. 286-292). https://doi.org/10.1109/DEXA.2002.1045912

Άρθρα σε Πρακτικά Συνεδρίων

Dimitriou, P., Karyotis, V., & Kourouthanassis, P. E. (2026). A Genetic Algorithm Approach to the Generalized Assignment Problem with Non-Linear Costs for Humanitarian Supply Chains. In Proceedings of the 15th International Conference on Operations Research and Enterprise Systems (pp. 359–370). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0014339900004055

Kakkavas, G., Maratos, P., Karyotis, V., Zafeiropoulos, A., & Papavassiliou, S. (2026). Context-Aware Enhancements for Dimension-Preserving Invertible Neural Models in Traffic Matrix Estimation. In 2026 IEEE 50th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1031–1039). IEEE. https://doi.org/10.1109/compsac69091.2026.00141

Kallitsis, G., Stai, E., Karyotis, V., & Papavassiliou, S. (2026). Deep Reinforcement Learning-Based Joint Trajectory-Resource Allocation Optimization for Swipt Mobile Networks. In 2026 32nd International Conference on Telecommunications (ICT) (pp. 465–471). IEEE. https://doi.org/10.1109/ict70370.2026.11594736

Maratos, P., Kakkavas, G., Karyotis, V., Zafeiropoulos, A., Tsiropoulou, E. E., & Papavassiliou, S. (2026). Network Tomography for O-RAN: Inferring Per-UE Metrics from Aggregate Telemetry. In 2026 IEEE 32nd International Symposium on Local and Metropolitan Area Networks (LANMAN) (pp. 1–6). IEEE. https://doi.org/10.1109/lanman69841.2026.11623533

Dimitrakopoulos, G. N., Vrahatis, A. G., Choleva, V., Korfiatis, N., Karyotis, V., & Kourouthanassis, P. E. (2025). AutoGluon and TabPFN for Supply Chain Optimization. In 2025 IEEE International Conference on Big Data (BigData). Macau, China: IEEE.

Kleitsikas, C., Korfiatis, N., Leonardos, S., & Ventre, C. (2025). Bitcoin’s Edge: Embedded Sentiment in Blockchain Transactional Data. In 2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 1–5). IEEE. https://doi.org/10.1109/icbc64466.2025.11114477

Dimitrakopoulos, G. N., Vrahatis, A. G., Panagiotopoulos, A., Korfiatis, N., Karyotis, V., & Kourouthanassis, P. E. (2025). Decision Support and Predictive Intelligence in Humanitarian Logistics: An Updated Overview. In 2025 IEEE International Conference on Big Data (BigData). Macau, China: IEEE.

Kakkavas, G., Maratos, P., Karyotis, V., & Papavassiliou, S. (2025). Conditional Invertible Neural Networks for Traffic Matrix Estimation from Link Loads. In 2025 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) (pp. 1–6). IEEE. https://doi.org/10.1109/meditcom64437.2025.11104479

Papoulia, E., Karyotis, V., & Papavassiliou, S. (2025). Efficient Dynamic Hyperbolic Network Embeddings for Software-Defined Topologies. In 2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking (NFV-SDN) (pp. 1–7). IEEE. https://doi.org/10.1109/nfv-sdn66355.2025.11349480

Stai, E., Kallitsis, G., Karyotis, V., & Papavassiliou, S. (2025). On Meeting Age of Information Constraints via Energy Harvesting and Wireless Power Transfer. In 2025 8th International Balkan Conference on Communications and Networking (Balkancom) (pp. 1–6). IEEE. https://doi.org/10.1109/balkancom65827.2025.11185987

Miloulis, S.-T., Kakkos, I., Kaliampakou, C., Zorzos, I., Dimitrakopoulos, G. N., Vezakis, I., Kouris, I., Anastasiou, A., & Matsopoulos, G. K. (2025). Feature-Level Explainability in EEG-Based Fatigue Detection Using LASSO and Functional Connectivity. In 2025 IEEE International Conference on Big Data (BigData) (pp. 6545–6551). IEEE. https://doi.org/10.1109/bigdata66926.2025.11401674

Karakatsoulis, D., Dimitrakopoulos, G. N., Vrahatis, A. G., Matzakos, N., & Doukakis, S. (2025). Learning Management System (LMS) Design Toward Precision Education Using Brain Data. In Advances in Experimental Medicine and Biology (pp. 519–527). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-03394-9_51

Gasparinatou, M. M., Dimitrakopoulos, G. N., Vrahatis, A. G., & Vlamos, P. (2025). Minimum Redundancy Maximum Relevance Feature Selection–Application on Single-Cell RNA Sequencing Dataset. In Advances in Experimental Medicine and Biology (pp. 213–221). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-03398-7_22

Kokotos, S. E., Kalliposis, S., Kouris, P., Bontis, E.-G., & Kermanidis, K. L. (2025). A Comparative Analysis of LLMs for Greek - English Machine Translation: ChatGPT-4 vs. Gemini. In Papaleonidas, A., Pimenidis, E., Papadopoulos, H., & Chochliouros, I. (Ed.), Artificial Intelligence Applications and Innovations. AIAI 2025 IFIP WG 12.5 International Workshops (pp. 3–16). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97313-0_1

Palla, E., Korfiatis, N., Hatzithomas, L., Palla, P., & Lekakos, G. (2024). The Device Effect on Visitor Critique: A Study of Mobile and Desktop Museum Reviews on TripAdvisor. In Lecture Notes in Computer Science (pp. 290–300). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-60487-4_22

Kallitsis, G., Stai, E., Karyotis, V., & Papavassiliou, S. (2024). Enhancing the Cross-layer Operation in Wireless Energy-Harvesting Networks with Age-of-Information Features. In Proceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (pp. 398–403). ACM. https://doi.org/10.1145/3641512.3690162

Kallitsis, G., Stai, E., Karyotis, V., & Papavassiliou, S. (2024). Optimal Power Control for Joint Wireless Information-Energy Transfers in Energy Harvesting Networks. In 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1–6). IEEE. https://doi.org/10.1109/camad62243.2024.10942874

Kakkavas, G., Maratos, P., Karyotis, V., & Papavassiliou, S. (2024). Traffic Matrix Estimation Using Invertible Neural Networks. In 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1–7). IEEE. https://doi.org/10.23919/softcom62040.2024.10721829

Koumadorakis, D. E., Krokidis, M. G., Dimitrakopoulos, G. N., & Vrahatis, A. G. (2023). A Consensus Gene Regulatory Network for Neurodegenerative Diseases Using Single-Cell RNA-Seq Data. In Advances in Experimental Medicine and Biology (pp. 215–224). Springer International Publishing. https://doi.org/10.1007/978-3-031-31978-5_20

Lazaros, K., Dimitrakopoulos, G. N., Vlamos, P., & Vrahatis, A. G. (2023). A Gene Selection Strategy for Enhancing Single-Cell RNA-Seq Data Integration. In Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference (pp. 12). MDPI. https://doi.org/10.3390/engproc2023050012

@inproceedings{lazaros2023gene,

title = {A Gene Selection Strategy for Enhancing Single-Cell RNA-Seq Data Integration},

author = {Konstantinos Lazaros and Georgios N. Dimitrakopoulos and Panagiotis Vlamos and Aristidis G. Vrahatis},

url = {https://doi.org/10.3390/engproc2023050012},

doi = {10.3390/engproc2023050012},

year  = {2023},

date = {2023-01-01},

booktitle = {Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference},

pages = {12},

publisher = {MDPI},

abstract = {Cancer remains a pervasive and formidable disease within modern societies, necessitating the utilization of advanced techniques in both diagnosis and therapy. Molecular biology has emerged as a crucial tool in deciphering the underlying biological mechanisms that contribute to various types of cancer. Notably, single-cell sequencing has garnered significant attention as a state-of-the-art method for profiling gene expression in individual cells, unveiling previously concealed mechanisms and biological phenomena. With the abundance of single-cell datasets available, there is a pressing need to integrate related datasets into larger ones to enhance our understanding of biological processes and augment predictive capabilities. In this study, we investigated the impact of gene selection, achieved through the implementation of feature selection techniques, on the integration of single-cell datasets. By systematically exploring the effects of gene selection, we aim to enhance the integration process, leading to improved biological insights and enhanced predictive power. The proposed method aims to enhance two cutting-edge data integration methodologies for single-cell RNA sequencing (scRNA-seq). The method utilizes a strategy that combines two key components: a statistical approach to isolate the high variability in gene expression across cells or samples and a feature selection strategy based on XgBoost to keep genes that are important for distinguishing among healthy and cancerous cells.},

keywords = {},

pubstate = {published},

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}

Dimitrakopoulos, G. N., Lazaros, K., Krokidis, M. G., Exarchos, T., Vrahatis, A. G., & Vlamos, P. (2023). A Graph-Based Approach to Integrate Large-Scale Drug and Protein Data for Alzheimer’s Disease Drug Repurposing. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4584–4587). IEEE. https://doi.org/10.1109/bigdata59044.2023.10386766

@inproceedings{dimitrakopoulos2023graph,

title = {A Graph-Based Approach to Integrate Large-Scale Drug and Protein Data for Alzheimer’s Disease Drug Repurposing},

author = {Georgios N. Dimitrakopoulos and Konstantinos Lazaros and Marios G. Krokidis and Themis Exarchos and Aristidis G. Vrahatis and Panagiotis Vlamos},

url = {https://doi.org/10.1109/bigdata59044.2023.10386766},

doi = {10.1109/bigdata59044.2023.10386766},

year  = {2023},

date = {2023-01-01},

booktitle = {2023 IEEE International Conference on Big Data (BigData)},

pages = {4584–4587},

publisher = {IEEE},

abstract = {Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.},

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Dimitrakopoulos, G. N., Lazaros, K., Vrahatis, A. G., Krokidis, M. G., Skolariki, K., Vlamos, P., & Exarchos, T. (2023). A Machine Learning approach combining omics data for Alzheimer’s Disease analysis. In Book of Proceedings (pp. 342–345). Institute for Information Technologies, University of Kragujevac. https://doi.org/10.46793/iccbi23.342d

Krokidis, M. G., Dimitrakopoulos, G. N., Exarchos, T. P., Vrahatis, A. G., & Vlamos, P. (2023). Advanced Big Data Analysis for Deciphering the Role of Protein Misfolding and Interactions in the Pathogenesis of Alzheimer’s Disease. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4614–4617). IEEE. https://doi.org/10.1109/bigdata59044.2023.10386706

Kallitsis, G., Karyotis, V., & Papavassiliou, S. (2023). A Joint Wireless Data-Power Exchange Approach for Distributed Energy Harvesting Networks. In 2023 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 209-215). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCN60443.2023.10453207

Kourouthanassis, P., Bardaki, C., Sourvinos, A., Karyotis, V., & Mylonas, P. (2023). Platform Architecture and Functionality for Efficient and Intelligent Logistics. In 2023 18th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMAP59435.2023.10255183

Stai, E., Karyotis, V., Kourouthanassis, P., & Mylonas, P. (2023). A Backpressure-Based Algorithm for Optimal Scheduling in Freight Management Systems. In Proceedings of the 18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023. Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMAP59435.2023.10255185

Aslanis, I., Krokidis, M. G., Dimitrakopoulos, G. N., & Vrahatis, A. G. (2023). Identifying Network Biomarkers for Alzheimer’s Disease Using Single-Cell RNA Sequencing Data. In Advances in Experimental Medicine and Biology (pp. 207–214). Springer International Publishing. https://doi.org/10.1007/978-3-031-31978-5_19

Mantellos, G., Exarchos, T. P., Dimitrakopoulos, G. N., Vlamos, P., Papastamatiou, N., Karaiskos, K., Minos, P., Alexandridis, T., Axiotopoulos, S., Tsakiridis, D., Avramoudis, V., Vasiliadis, A., & Stagakis, S. (2023). Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers. In Advances in Experimental Medicine and Biology (pp. 213–222). Springer International Publishing. https://doi.org/10.1007/978-3-031-31982-2_23

Krokidis, M. G., Vrahatis, A. G., Dimitrakopoulos, G. N., Skolariki, K., Vlamos, P., & Exarchos, T. P. (2023). Structural simulations predicting protein folding in Alzheimer's disease. In 2nd International Conference on Chemo and Bioinformatics (ICCBIKG 2023). Kragujevac, Serbia.

Giannakopoulou, O., Kakkos, I., Dimitrakopoulos, G. N., Sun, Y., Matsopoulos, G. K., & Koutsouris, D. (2023). Time-Dependent Adaptations of Brain Networks in Driving Fatigue. In ABSET 2023 (pp. 6). MDPI. https://doi.org/10.3390/engproc2023050006

Nikopoulou, M., Chasapi, P., Kourouthanassis, P., & Pateli, A. (2023). Digital Technology Implementation in the Hospitality Industry and Hotel Star Rating: Does It Matter? Evidence from Greece. In 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. https://doi.org/10.1109/IISA59645.2023.10345942

Karyotis, V., Stai, E., Kourouthanassis, P., & Mylonas, P. (2022). A Framework for a Holistic Information System for Small-Medium Logistics Enterprises. In 2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) (pp. 1–6). https://doi.org/10.1109/SMAP56125.2022.9941923

Filiopoulou, E., Bardaki, C., Boukouvalas, D., Nikolaidou, M., & Kourouthanassis, P. (2022). Last-Mile Delivery Options: Exploring Customer Preferences and Challenges. In 2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) (pp. 1–6).

Kakkavas, G., Diamanti, M., Nyarko, K. N., Gabriel, M., Karyotis, V., Mosner, K., & Papavassiliou, S. (2022). Realistic Field Trial Evaluation of a Tele-operated Support Service for Remote Driving over 5G. In 2022 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 58-63). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCN57023.2022.10051034

@inproceedings{Kakkavas202258,

title = {Realistic Field Trial Evaluation of a Tele-operated Support Service for Remote Driving over 5G},

author = {G. Kakkavas and M. Diamanti and K. Nseboah Nyarko and M. Gabriel and V. Karyotis and K. Mosner and S. Papavassiliou},

url = {https://doi.org/10.1109/CSCN57023.2022.10051034},

doi = {10.1109/CSCN57023.2022.10051034},

isbn = {9781665476218},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE Conference on Standards for Communications and Networking (CSCN)},

pages = {58-63},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In this paper, we focus on the development and realistic field trial evaluation of a Tele-operated Support (TeSo) service for remote driving over 5G mobile communications. The presented prototype was created within the context of the transport vertical of the "5G HEalth, AquacultuRe and Transport (5G-HEART)"5G PPP Phase 3 project and was meti culously tested and evaluated in several realistic test cases over a commercial 5G Non-Standalone (NSA) deployment. A vehicle equipped with appropriate sensors and actuators was actively controlled from a distance of 36 km, exchanging sensor data and video streams in the uplink and vehicle-control commands in the downlink with the remote operations center. This work is among the first experimentally-driven quantitative analyses of such an application with a real pilot using an actual vehicle. Our objective was to validate the feasibility of the proposed TeSo service by evaluating relevant performance metrics, such as latency, jitter, throughput, and loss rate. Moreover, our extensive testing aimed to identify salient features and emerging challenges from the prototype, which can aid in a two-fold manner: first, paving the way for the eventual commercialization of the service, and second, guiding standardization effort for a relevant emerging market in the near future. © 2022 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Vitoropoulou, M., Karyotis, V., & Papavassiliou, S. (2022). On the Fair Energy Sharing in Networks with Wireless Charging-capable Devices. In 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 375-380). IEEE Computer Society. https://doi.org/10.1109/WiMob55322.2022.9941704

Kakkavas, G., Nyarko, K. N., Lahoud, C., Kuhnert, D., Kuffner, P., Gabriel, M., Ehsanfar, S., Diamanti, M., Karyotis, V., Mobner, K., & Papavassiliou, S. (2022). Teleoperated Support for Remote Driving over 5G Mobile Communications. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) (pp. 280-285). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MeditCom55741.2022.9928745

Antonopoulos, K., Ringas, D., Christopoulou, E., Keramidas, G., Antonopoulos, C., & Voros, N. (2022). A Network Simulator as a Service for Demanding Performance Evaluation of Wireless and IoT Scenarios. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) (pp. 221–226). IEEE. https://doi.org/10.1109/meditcom55741.2022.9928767

Antonopoulos, K., Ringas, D., Christopoulou, E., Keramidas, G., Antonopoulos, C., & Voros, N. (2022). NS3SaaS: Cloud-based “Network Simulator as a Service” with customisable resource scheduling. In 2022 IEEE 11th International Conference on Cloud Networking (CloudNet) (pp. 93–100). IEEE. https://doi.org/10.1109/cloudnet55617.2022.9978899

@inproceedings{antonopoulos2022ns3saas,

title = {NS3SaaS: Cloud-based “Network Simulator as a Service” with customisable resource scheduling},

author = {Konstantinos Antonopoulos and Dimitrios Ringas and Eleni Christopoulou and Georgios Keramidas and Christos Antonopoulos and Nikolaos Voros},

url = {https://doi.org/10.1109/cloudnet55617.2022.9978899},

doi = {10.1109/cloudnet55617.2022.9978899},

year  = {2022},

date = {2022-01-01},

booktitle = {2022 IEEE 11th International Conference on Cloud Networking (CloudNet)},

pages = {93–100},

publisher = {IEEE},

abstract = {This paper presents the design, development and evaluation of a novel cloud-based Network Simulator as a Service. It wraps a discrete-event network simulator and deploys it as an elastic service, which can then be used for interactive and batch simulations execution, enhanced with results post-processing and analysis as well as presentation features. It addresses the requirement to rapidly carry out large numbers of network simulations in order to test and validate appropriate and domain specific optimisations in industry, research and education settings. This cloud based service drastically enhances the capabilities of a researcher by allowing arbitrary definition of simulation scenarios along with sets of parameters and commission of large scale simulation executions. It is based on an ensemble of efficient open-source tools, which, when properly coordinated, deliver to the end user a cloud based service that is novel, robust, extendible and extensible. It has been deployed within ESDA Lab (UoP, Greece) where several performance evaluation research scenarios were carried out testing different simulation scheduling schemes in order to evaluate the usefulness of our system and its efficiency under different prioritisation settings. Results demonstrate that, compared to the default general purpose network simulator, the offered cloud-based service significantly enhances end-users capabilities by tailor fitting to their different prioritisation requirements, e.g. favouring interactive users, minimising turnaround times, offering custom resources allocation, etc. In addition to that the offered service effectively hides the peculiarities of the standalone network simulator enabling its use by non-experts and significantly expanding the potential target group of end users.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Nikoviotis, R., & Ringas, D. (2021). Self-care Diabetes: a diabetes self-management application based on the seven self-management areas (AADE7) of the American Association of Diabetes Educators. In 25th Pan-Hellenic Conference on Informatics (pp. 477–482).

Kakkavas, G., Kalntis, M., Karyotis, V., & Papavassiliou, S. (2021). Future Network Traffic Matrix Synthesis and Estimation Based on Deep Generative Models. In 2021 International Conference on Computer Communications and Networks (ICCCN) (pp. 1–8). https://doi.org/10.1109/ICCCN52240.2021.9522222

Kakkavas, G., Diamanti, M., Stamou, A., Karyotis, V., Papavassiliou, S., Bouali, F., & Moessner, K. (2021). 5G Network Requirement Analysis and Slice Dimensioning for Sustainable Vehicular Services. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 495–502). https://doi.org/10.1109/DCOSS52077.2021.00082

@inproceedings{kakkavas20215g,

title = {5G Network Requirement Analysis and Slice Dimensioning for Sustainable Vehicular Services},

author = {Grigorios Kakkavas and Maria Diamanti and Adamantia Stamou and Vasileios Karyotis and Symeon Papavassiliou and Faouzi Bouali and Klaus Moessner},

url = {https://doi.org/10.1109/DCOSS52077.2021.00082},

doi = {10.1109/DCOSS52077.2021.00082},

isbn = {9781665439299},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS)},

pages = {495--502},

organization = {IEEE},

abstract = {The Fifth Generation (5G) mobile communications together with software defined networking (SDN) and network function virtualization (NFV) are expected to enable a wide range of vertical use-cases. Different vertical industries with diverse service streams and sets of requirements should leverage the advanced capabilities of 5G networks through a single infrastructure to support the desired Quality of Service/Experience (QoS/QoE). In this paper, we focus on the Transport vertical and we study four novel service categories, each one consisting of one or more related scenarios, within the framework of the 5G Health, Aquaculture and Transport (5G-HEART) 5G PPP Phase 3 project. The first pass analysis of the envisioned vehicular services and their underlying operation, combined with the mapping of the mostly high-level functional user requirements to quantitative network Key Performance Indicators (KPIs) via a thorough and concise methodology, is essential for future testing with real pilots. Furthermore, our work paves the way towards efficient network slicing by exploring the interrelations between the identified KPIs and the respective target values that must be simultaneously satisfied over the same physical network infrastructure, in the context of the three 5G generic services. © 2021 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Spatharakis, D., Avgeris, M., Kakkavas, G., Papadakis-Vlachopapadopoulos, K., Dimolitsas, I., Dechouniotis, D., Karyotis, V., & Papavassiliou, S. (2021). Industrial Robotics Experimentation over Federated Next Generation Internet Testbeds. In 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) (pp. 1–2). https://doi.org/10.1109/MeditCom49071.2021.9647524

Kakkavas, G., Diamanti, M., Gabriel, M., Lahoud, C., Akula, P., Karyotis, V., Mößner, K., & Papavassiliou, S. (2021). Demo Proposal: Tele-Operated Support over 4G/5G Mobile Communications. In 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) (pp. 1–2). https://doi.org/10.1109/MeditCom49071.2021.9647643

Vitoropoulou, M., Tsitseklis, K., Karyotis, V., & Papavassiliou, S. (2021). Caching, Recommendations and Opportunistic Offloading at the Network Edge. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (pp. 112–119). https://doi.org/10.1109/MSN53354.2021.00031

Miloulis, S.-T., Kakkos, I., Dimitrakopoulos, G. N., Sun, Y., Karanasiou, I. S., Asvestas, P. A., Ventouras, E.-C., & Matsopoulos, G. K. (2021). Evaluating Memory and Cognition via a Wearable EEG System: A Preliminary Study. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 52–66). Springer International Publishing. https://doi.org/10.1007/978-3-030-70569-5_4

@inproceedings{miloulis2021evaluating,

title = {Evaluating Memory and Cognition via a Wearable EEG System: A Preliminary Study},

author = {Stavros-Theofanis Miloulis and Ioannis Kakkos and Georgios N. Dimitrakopoulos and Yu Sun and Irene S. Karanasiou and Pantelis A. Asvestas and Errikos-Chaim Ventouras and George K. Matsopoulos},

url = {https://doi.org/10.1007/978-3-030-70569-5_4},

doi = {10.1007/978-3-030-70569-5_4},

isbn = {9783030705688},

year  = {2021},

date = {2021-01-01},

booktitle = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},

pages = {52–66},

publisher = {Springer International Publishing},

abstract = {Human memory comprises one of the most complex brain functions, attracting researchers to unveil the neural mechanisms governing its effective operation. In this respect, the current study examines the application of a wearable single-channel EEG to the interpretation of cognitive operations reflecting memory processes. For this purpose, we implemented a set of tasks for evaluating the participants’ processing skills and memory efficiency, in order to examine potential outcomes derived from a specialized cognitive training routine. The employed training method targeted the distinction of automatic and controlled processing and its effects on memory, while we also investigated transfer effects to untrained tasks. Based on the electrophysiological data recorded during the cognitive tasks, we computed measures of induced EEG activity for each frequency band to examine the influence of cognitive training on both task performance and brain activity, as well as whether the EEG metrics could provide insight into the underlying brain processes and augment the interpretation of behavioral outcomes. Ultimately, statistical analysis showed an apparent contribution of EEG in understanding the observed behavioral differences, while our training program had a clear impact on the participants’ performance and brain activity. Moreover, we observed the reported distinction between automatic and controlled memory processes which play an integral part in both ageing and cognitive impairments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Krokidis, M. G., Dimitrakopoulos, G. N., Vrahatis, A. G., Exarchos, T. P., & Vlamos, P. (2021). Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson’s Disease Omics Data. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4460–4463). IEEE. https://doi.org/10.1109/bigdata52589.2021.9671736

Krommyda, M., Tsitseklis, K., Kantere, V., Karyotis, V., & Papavassiliou, S. (2021). Visualizing and exploring big datasets based on semantic community detection. In Proceedings of the 24th International Conference on Extending Database Technology (EDBT 2021) (pp. 678–681). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2021.82

Nikiforos, M. N., Malakopoulou, M., & Exarchos, T. (2021). Development of a Diagnostic Tool for Balance Disorders Based on Machine Learning Techniques. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 47–54). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_7

Xipolitopoulos, C., Nikiforos, M. N., & Exarchos, T. (2021). Machine Learning for Autistic Spectrum Disorder Risk Screening. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 81–87). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_10

Angelis, I., Alvanou, A. G., Avgoustis, N., Vergis, S., Zervopoulos, A., Malakopoulou, M., Bezas, K., Nikiforos, M. N., Papamichail, A., Stylidou, A., Exarchos, T. P., & Vlamos, P. (2021). Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 89–96). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_11

Vassilopoulou, S., Sakkas, V., Batsos, J., Sakkopoulos, E., & Ringas, D. (2020). Geo-Environmental Information Management Software for Automated Terrain and Ground Deformation Analysis. In 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1–8).

Chloros, D., & Ringas, D. (2020). Fluspot: Seasonal flu tracking app exploiting wearable IoT device for symptoms monitoring. In 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1–7).

Kakkavas, G., Karyotis, V., & Papavassiliou, S. (2020). A Distance-based Agglomerative Clustering Algorithm for Multicast Network Tomography. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1–7). https://doi.org/10.1109/ICC40277.2020.9149412

Nikiforos, M. N., Malakopoulou, M., Stylidou, A., Alvanou, A.-G., Karyotis, V., & Kourouthanassis, P. (2020). Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA (pp. 1–6). https://doi.org/10.1109/SMAP49528.2020.9248472

Vitoropoulou, M., Tsitseklis, K., Karakoulias, A., Karyotis, V., & Papavassiliou, S. (2020). CAUSE: Caching Aided by USer Equipment. In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) (pp. 713–721). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00123

Karampasi, A., Kakkos, I., Miloulis, S.-T., Zorzos, I., Dimitrakopoulos, G. N., Gkiatis, K., Asvestas, P., & Matsopoulos, G. K. (2020). A Machine Learning fMRI Approach in the Diagnosis of Autism. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 3628–3631). IEEE. https://doi.org/10.1109/bigdata50022.2020.9378453

Dimitrakopoulos, G. N., Klapa, M. I., & Moschonas, N. K. (2020). Enriching PICKLE meta-database with the mouse protein interactome. In ELIXIR-GR All Hands Meeting 2020. Virtual.

Kastampolidou, K., Nikiforos, M. N., & Andronikos, T. (2020). A Brief Survey of the Prisoners’ Dilemma Game and Its Potential Use in Biology. In Vlamos, P. (Ed.), GeNeDis 2018: Computational Biology and Bioinformatics (pp. 315–322). Springer International Publishing. https://doi.org/10.1007/978-3-030-32622-7_29

Nikiforos, M. N., & Kermanidis, K. L. (2020). A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web. In Calzolari, N., Béchet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., & Piperidis, S. (Ed.), Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020) (pp. 3861–3867). Marseille, France: European Language Resources Association.

Nikiforos, M. N., Vergis, S., Stylidou, A., Augoustis, N., Kermanidis, K. L., & Maragoudakis, M. (2020). Fake News Detection Regarding the Hong Kong Events from Tweets. In Maglogiannis, I., Iliadis, L., & Pimenidis, E. (Ed.), Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops (pp. 177–186). Springer International Publishing. https://doi.org/10.1007/978-3-030-49190-1_16

Xipolitopoulos, C., Nikiforos, M. N., Malakopoulou, M., & Pateli, A. G. (2020). Success Factors for Crowd-funding Campaigns with Machine Learning Techniques. In ICTO 2020: Smart Technologies for an Inclusive World. Paris, France.

Magioladitis, M., Ringas, D., & Christopoulou, E. (2019). Promoting cultural heritage via gamification and augmented reality. In International Conference, Digital Culture & AudioVisual Challenges.

Tsitseklis, K., Kakkavas, G., Karyotis, V., & Papavassiliou, S. (2019). A Realistic Evaluation of MRF-based Resource Allocation for SDR Cognitive Radio Networks. In 2019 IEEE 44th Conference on Local Computer Networks (LCN) (pp. 185–192). https://doi.org/10.1109/LCN44214.2019.8990725

Vrahatis, A. G., Dimitrakopoulos, G. N., Tasoulis, S. K., & Plagianakos, V. P. (2019). A single-cell Systems Biology approach for disease-specific subpathway extraction. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1–7). IEEE. https://doi.org/10.1109/cibcb.2019.8791460

@inproceedings{vrahatis2019single,

title = {A single-cell Systems Biology approach for disease-specific subpathway extraction},

author = {Aristidis G. Vrahatis and Georgios N. Dimitrakopoulos and Sotiris K. Tasoulis and Vassilis P. Plagianakos},

url = {https://doi.org/10.1109/cibcb.2019.8791460},

doi = {10.1109/cibcb.2019.8791460},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)},

pages = {1–7},

publisher = {IEEE},

abstract = {Single-cell sequencing technologies offer a platform to explore deeper the complex diseases and biological processes. On parallel, Systems Biology approaches can tackle part of this complexity, since they are based on biological networks elucidating genotype to phenotype relationships in a more comprehensive framework. Hence, research community is shifting towards singlecell systems biology approaches to identify and validate disease-specific biomarkers. Under this perspective, we propose a methodological framework aimed to infer disease-specific subpathway activities, using cell signaling pathway topology and transcriptomics data derived from single-cell RNA-seq technologies. More specifically, we concatenated and integrated pathway network refining with single-cell expression profiles. Linear subpathways were extracted and a subpathway profile score was calculated taking into account the pathway information flow. The core of our method is XGBoost supervised classification based on the aforementioned score in order to identify important subpathways that control the rest of the network and cause major changes from a control to a disease state under study. The proposed methodology was applied to a real experimental study with single-cell RNA-seq expression profiles revealing changes in type 2 diabetes. Exported subpathways showed promising results, suggesting that the proposed approach can reliably identify subpathways, acting as potential disease-specific biomarkers.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Vrahatis, A. G., Dimitrakopoulos, G. N., Tasoulis, S. K., & Plagianakos, V. P. (2019). Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 846–849). IEEE. https://doi.org/10.1109/bibe.2019.00157

@inproceedings{vrahatis2019enhancing,

title = {Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces},

author = {Aristidis G. Vrahatis and Georgios N. Dimitrakopoulos and Sotiris K. Tasoulis and Vassilis P. Plagianakos},

url = {https://doi.org/10.1109/bibe.2019.00157},

doi = {10.1109/bibe.2019.00157},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)},

volume = {3},

pages = {846–849},

publisher = {IEEE},

abstract = {We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex human diseases. However, such studies create extremely high dimensional data isolating expression profiles for thousands or even millions of cells. Consequently, dealing with single-cell RNA-seq (scRNA-seq) data is considered the main challenge for unsupervised clustering, which can be used in order to identify grouped cell types. Towards this direction, we present a framework that enhances hierarchical clustering utilizing Proximity Learning on Random Projected spaces (PLRP). The proposed method's efficiency lies in the fact that we exploit the distances from multiple significantly lower dimension spaces defined by Random Projections using ensembles of k-nearest neighbor searches. In the transformed data we applied hierarchical agglomerative clustering (HAC) improving significantly its performance when compared against using the original space. The performance of the proposed PLRP was evaluated in a publicly available experimental dataset with scRNA-seq expression profiles, against three well-established clustering tools. The results showed that our approach greatly enhances clustering performance exposing its applicability in ultra-high dimensions and imposing further development towards this direction.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Dimitrakopoulos, G. N., Gioutlakis, A., Klapa, M. I., & Moschonas, N. K. (2019). Evaluating the expansion of the experimentally determined human protein interactome using the PICKLE meta-database. In 12th Panhellenic Scientific Conference in Chemical Engineering. Athens, Greece.

Vrahatis, A. G., Dimitrakopoulos, G. N., Tasoulis, S. K., Georgakopoulos, S. V., & Plagianakos, V. P. (2019). Single-cell regulatory network inference and clustering from high-dimensional sequencing data. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 2782–2789). IEEE. https://doi.org/10.1109/bigdata47090.2019.9006016

@inproceedings{vrahatis2019singlea,

title = {Single-cell regulatory network inference and clustering from high-dimensional sequencing data},

author = {Aristidis G. Vrahatis and Georgios N. Dimitrakopoulos and Sotiris K. Tasoulis and Spiros V. Georgakopoulos and Vassilis P. Plagianakos},

url = {https://doi.org/10.1109/bigdata47090.2019.9006016},

doi = {10.1109/bigdata47090.2019.9006016},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE International Conference on Big Data (Big Data)},

pages = {2782–2789},

publisher = {IEEE},

abstract = {We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Dimitrakopoulos, G. N., Gioutlakis, A., Klapa, M. I., & Moschonas, N. K. (2019). Unraveling the way of expansion of the experimentally supported human protein interactome using the PICKLE meta-database. In Conference of the Hellenic Society for Computational Biology and Bioinformatics (HSCBB19). Patras, Greece.

Vrahatis, A. G., Tasoulis, S. K., Dimitrakopoulos, G. N., & Plagianakos, V. P. (2019). Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1–6). IEEE. https://doi.org/10.1109/cibcb.2019.8791482

@inproceedings{vrahatis2019visualizing,

title = {Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances},

author = {Aristidis G. Vrahatis and Sotiris K. Tasoulis and Georgios N. Dimitrakopoulos and Vassilis P. Plagianakos},

url = {https://doi.org/10.1109/cibcb.2019.8791482},

doi = {10.1109/cibcb.2019.8791482},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)},

pages = {1–6},

publisher = {IEEE},

abstract = {The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA sequencing, which allows transcriptomics measurements in individual cells, having promising results. However, such studies measure the entire genome for thousands of cells, creating datasets with extremely high dimensionality and complexity. Following this perspective, we propose a dimensionality reduction approach, called RGt-SNE, which visualizes single-cell RNA-seq data in two dimensions. Initially, RGt-SNE defines a cell-cell distance matrix based on Random Projections and Geodesic Distances. The first is used to define the pairwise cells distances in a low dimensional projected space avoiding the difficulties that exist in data with ultra-high dimensionality. The latter is used to better define the large pairwise cells distances. Subsequently, the t-SNE method is applied in the customized distance matrix for two dimensional visualization. RGt-SNE was evaluated in two real experimental single-cell RNA-seq data against three well-known methods, such as t-SNE, Multidimensional scaling, and ISOMAP. Outcomes provide the superiority of RGt-SNE suggesting it as a reliable tool for single-cell RNA-seq data analysis and visualization.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Varthis, E., Poulos, M., Yarenis, I., & Papavlasopoulos, S. (2019). Implementation of a Databaseless Web REST API for the Unstructured Texts of Migne's Patrologia Graeca with Searching capabilities and additional Semantic and Syntactic expandability. In Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage (pp. 123–129). Brussels, Belgium: ACM. https://doi.org/10.1145/3322905.3322913

Varthis, E., Poulos, M., Giarenis, I., & Papavlasopoulos, S. (2019). Semantic enrichment on Large Corpora: a case study for Patrologia Graeca. In IFLA WLIC 2019 Satellite Meeting: Artificial Intelligence (AI) and its impact on libraries and librarianship. Corfu, Greece.

Varsanis, K., Belias, D., Kakkos, N., Chondrogiannis, M., Rossidis, I., & Mantas, C. (2019). The Relationship Between Service Quality and Customer Satisfaction on Luxurious Hotels So to Produce Error-Free Service. In Kavoura, A., Kefallonitis, E., & Giovanis, A. (Ed.), Strategic Innovative Marketing and Tourism: 7th ICSIMAT, Athenian Riviera, Greece, 2018 (pp. 67–76). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-12453-3_8

Varsanis, K., Belias, D., Papailias, S., Chondrogiannis, M., Rossidis, I., Mantas, C., & Koustelios, A. (2019). The Sharing Economy and How It Affects the Conditions of Consumption and Competition in the Tourism Industry: The Case of Airbnb in Greece. In Kavoura, A., Kefallonitis, E., & Giovanis, A. (Ed.), Strategic Innovative Marketing and Tourism: 7th ICSIMAT, Athenian Riviera, Greece, 2018 (pp. 85–92). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-12453-3_10

Leonidou, L., Aykol, B., & Korfiatis, N. (2018). A meta-analysis of the link between power, conflict, opportunism, and satisfaction in business-to-business relationships. In European Marketing Academic Annual Conference (EMAC 2018). Glasgow, United Kingdom.

Karatzas, A., Stamolampros, P., Daskalakis, G., & Korfiatis, N. (2018). The effect of manufacturers' service transition on employee job satisfaction. In Proceedings of the 25th International EurOMA Conference 2018. Budapest, Hungary.

Karyotis, V., Tsitseklis, K., Sotiropoulos, K., & Papavassiliou, S. (2018). Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 266–271). https://doi.org/10.1109/PERCOMW.2018.8480134

Tsanikidis, C., Vitoropoulou, M., Karyotis, V., & Papavassiliou, S. (2018). On the Energy-Efficient Coverage of Network Regions with Convex Opaque Obstacles. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 135–141). https://doi.org/10.1109/PIMRC.2018.8580878

Karyotis, V., Avgeris, M., Michaloliakos, M., Tsagkaris, K., & Papavassiliou, S. (2018). Utility decisions for QoE-QoS driven applications in practical mobile broadband networks. In 2018 Global Information Infrastructure and Networking Symposium (GIIS) (pp. 1–5).

Dimitrakopoulos, G. N., Vrahatis, A. G., Plagianakos, V. P., & Sgarbas, K. (2018). Pathway analysis using XGBoost classification in Biomedical Data. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (pp. 1–6). ACM. https://doi.org/10.1145/3200947.3201029

Huang, G. H., & Korfiatis, N. (2017). To Abandon or Not to Abandon a Mobile Shopping Cart: The Role of Ambivalence. In Proceedings of the American Marketing Association Winter Educators Conference 2017. Orlando, Florida.

Pappas, I. O., Papavlasopoulou, S., Kourouthanassis, P. E., Mikalef, P., & Giannakos, M. N. (2017). Motivations and emotions in social media: explaining users’ satisfaction with FsQCA. In Digital Nations--Smart Cities, Innovation, and Sustainability: 16th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2017, Delhi, India, November 21--23, 2017, Proceedings 16 (pp. 375–387).

Russo, F. (2017). Performance evaluation of non-local means (NLM) algorithms for grayscale image denoising. In 2017 European Conference on Electrical Engineering and Computer Science (EECS) (pp. 204–210).

Papadis, N., Stai, E., & Karyotis, V. (2017). A path-based recommendations approach for online systems via hyperbolic network embedding. In 2017 IEEE Symposium on Computers and Communications (ISCC) (pp. 973–980). https://doi.org/10.1109/ISCC.2017.8024652

@inproceedings{papadis2017path,

title = {A path-based recommendations approach for online systems via hyperbolic network embedding},

author = {Nikolaos Papadis and Eleni Stai and Vasileios Karyotis},

url = {https://doi.org/10.1109/ISCC.2017.8024652},

doi = {10.1109/ISCC.2017.8024652},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE Symposium on Computers and Communications (ISCC)},

pages = {973--980},

organization = {IEEE},

abstract = {In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in hyperbolic space with greedy routing, exploiting properties of hyperbolic metric spaces. Contrary to the existing recommender systems that rank products in order to propose the highest ranked ones to the users, our proposed recommender system creates a progressive path of recommendations towards a final (known or inferred) target product using greedy routing over networks embedded in hyperbolic space. Thus, it prepares the user by intermediate recommendations for maximizing the chances that he/she accepts the recommendation of the target product(s). This casts the problem of locating a suitable recommendation as a path problem, where leveraging on the efficiency of greedy routing in graphs embedded in hyperbolic spaces and exploiting special network structure, if any, pays dividends. Two variants of our recommendation approach are provided, namely Hyperbolic Recommendation-Known Destination (HRKD), Hyperbolic Recommendation-Unknown Destination (HRUD), when the target product is known or unknown, respectively. We demonstrate how the proposed approach can be used for producing efficient recommendations in online systems, along with studying the impact of the several parameters involved in its performance via proper emulation of user activity over suitably defined graphs. © 2017 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Wetering, R., Mikalef, P., & Pateli, A. G. (2017). How Strategic Alignment of IT Flexibility, a Firm's Networking Capability, and Absorptive Capacity Influences Firm Innovation. In Mediterranean Conference on Information Systems (MCIS) 2017 (pp. 14). AIS Electronic Library (AISeL).

Dimitrakopoulos, G. N., Kakkos, I., Thakor, N. V., Bezerianos, A., & Sun, Y. (2017). A mental fatigue index based on regression using mulitband EEG features with application in simulated driving. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3220–3223). IEEE. https://doi.org/10.1109/embc.2017.8037542

Dimitrakopoulos, G. N., Kakkos, I., Vrahatis, A. G., Sgarbas, K., Li, J., Sun, Y., & Bezerianos, A. (2017). Driving Mental Fatigue Classification Based on Brain Functional Connectivity. In Communications in Computer and Information Science (pp. 465–474). Springer International Publishing. https://doi.org/10.1007/978-3-319-65172-9_39

Chai, J., Gong, C., Thangavel, P., Dimitrakopoulos, G. N., Kakkos, I., Sun, Y., Dai, Z., Yu, H., Thakor, N. V., Bezerianos, A., & Li, J. (2017). Identification of gait-related brain activity using electroencephalographic signals. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 548–551). IEEE. https://doi.org/10.1109/ner.2017.8008410

Karyotis, V., Stai, E., & Papavassiliou, S. (2016). On the Evolution of Complex Network Topology Under Network Churn. In International Conference on Wired/Wireless Internet Communication (pp. 227–240). https://doi.org/10.1007/978-3-319-33936-8_18

Stai, E., Sotiropoulos, K., Karyotis, V., & Papavassiliou, S. (2016). Hyperbolic traffic load centrality for large-scale complex communications networks. In 2016 23rd International Conference on Telecommunications (ICT) (pp. 1–5). https://doi.org/10.1109/ICT.2016.7500371

Gkika, S., Skiada, M., Lekakos, G., & Kourouthanassis, P. E. (2016). Investigating the Role of Personality Traits and Influence Strategies on the Persuasive Effect of Personalized Recommendations. In Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE 2016), co-located with ACM RecSys 2016 (pp. 9–17). CEUR-WS.org.

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Mikalef, P., & Pateli, A. G. (2016). Developing and Validating a Measurement Instrument of IT-Enabled Dynamic Capabilities. In European Conference on Information Systems.

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Arnautovic, E., Vallée, M., Mulvenna, M., Baumgarten, M., Hadjiantonis, A. M., Rehm, S.-V., Müthel, M., Karyotis, V., Papavassiliou, S., & Stathis, K. (2010). Towards self-managing systems inspired by economic organizations. In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 888–895). https://doi.org/10.1109/ICSMC.2010.5641875

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Karyotis, V., Pittal`a, F., Fazio, M., Papavassiliou, S., & Puliafito, A. (2010). Topology-Aware Hybrid Random Walk Protocols for Wireless Multihop Networks. In International Conference on Mobile Networks and Management (pp. 107–118). https://doi.org/10.1007/978-3-642-11817-3_10

@inproceedings{karyotis2009topology,

title = {Topology-Aware Hybrid Random Walk Protocols for Wireless Multihop Networks},

author = {Vasileios Karyotis and Fabio Pittal`a and Maria Fazio and Symeon Papavassiliou and Antonio Puliafito},

url = {https://doi.org/10.1007/978-3-642-11817-3_10},

doi = {10.1007/978-3-642-11817-3_10},

year  = {2010},

date = {2010-01-01},

urldate = {2009-01-01},

booktitle = {International Conference on Mobile Networks and Management},

pages = {107--118},

organization = {Springer, Berlin, Heidelberg},

abstract = {The proliferation of wireless multihop networks has made various operations, such as search and retrieval of distributed data a significant concern. Various methods have been proposed for performing such tasks efficiently, especially when all network nodes need to be visited at least once. Random walks are probabilistic approaches for performing the aforementioned operations effectively and with relatively small overhead compared to other typically-employed schemes, such as flooding. Recently, a hybrid random walk scheme has been proposed for increasing the desired performance, at the cost of additional consumed resources. In this work, we adopt the paradigm of hybrid random walk protocols and propose two novel hybrid schemes that exploit local topological information, aiming at further increasing the performance of random walk protocols in multihop networks. We consider different jump configurations of the hybrid random walk protocols and various degrees of mobility. Through analysis and simulation, the simple random walk model appears more appropriate for energy-constrained networks such as sensor networks, while the hybrid ones are more appealing for less energy-stringent, performance-oriented multihop networks, such as vehicular and mesh networks. The simple hybrid protocol occupies the middle ground, being appealing for ad hoc networks with medium to low node densities and average energy requirements. © 2010 ICST Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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@inproceedings{pateli2004wireless,

title = {Wireless Info-Communication and Navigation Services in Exhibition Shows},

author = {Adamantia G. Pateli and Diomidis Spinellis and George M. Giaglis},

url = {http://dmst.aueb.gr/dds/pubs/conf/2004-mbusiness/html/asg04.pdf},

year  = {2004},

date = {2004-01-01},

booktitle = {Proceedings of the 3rd International Conference on Mobile Business (m>Business 2004)},

address = {New York, USA},

abstract = {Exhibition shows accumulate vast quantities of information and their success relies in the quantity and quality of interaction of the involved parties: exhibitors, visitors and organizers. The more effective the interaction between end parties, the more successful the exhibition. The application of advanced ICT applications in the exhibition industry has always been a major opportunity for leveraging the value proposition of exhibition-related services. More specifically, as the telecommunication technology evolves, unexpected possibilities arise for such information and communication rich environments and quite interesting applications, from both the technology and the business side, appear. This case study concerns extended discussion about an innovative wireless system, named as Wireless Exhibition Guide. The wireless system, which has been the primary research and development result of a European project funded under the IST programme, employs a set of integrated software components, mobile terminals, wireless networks and indoor location positioning technologies to introduce advanced information, communication and navigation services for a typical European exhibition environment. The discussion concerns several perspectives on the development and exploitation of the Wireless Exhibition Guide, such as applied technology (sub-systems and infrastructure), provided services, value chain analysis, candidate business models, and regulatory framework with which it complies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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