@article{Lytos2020,

title = {Towards smart farming: Systems, frameworks and exploitation of multiple sources},

author = { A. Lytos and T. Lagkas and P. Sarigiannidis and M. Zervakis and G. Livanos},

url = {https://www.researchgate.net/publication/339221382_Towards_Smart_Farming_Systems_Frameworks_and_Exploitation_of_Multiple_Sources},

doi = {10.1016/j.comnet.2020.107147},

year  = {2020},

date = {2020-01-01},

journal = {Computer Networks},

volume = {172},

abstract = {Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status. This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture. © 2020 The Authors},

keywords = {Agriculture, Big Data, Internet of things, machine learning, smart farming},

pubstate = {published},

tppubtype = {article}

}

@article{RadoglouGrammatikis2020,

title = {ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid},

author = { P. Radoglou Grammatikis and P. Sarigiannidis and G. Efstathopoulos and E. Panaousis},

url = {https://www.researchgate.net/publication/344176314_ARIES_A_Novel_Multivariate_Intrusion_Detection_System_for_Smart_Grid},

doi = {10.3390/s20185305},

year  = {2020},

date = {2020-01-01},

journal = {Sensors (Basel, Switzerland)},

volume = {20},

number = {18},

abstract = {The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.},

keywords = {Cybersecurity, intrusion detection system, machine learning, Modbus, SCADA, Smart Grid},

pubstate = {published},

tppubtype = {article}

}

@article{Lytos2019,

title = {The evolution of argumentation mining: From models to social media and emerging tools},

author = { A. Lytos and T. Lagkas and P. Sarigiannidis and K. Bontcheva},

url = {https://www.researchgate.net/publication/334195420_The_evolution_of_argumentation_mining_From_models_to_social_media_and_emerging_tools},

doi = {10.1016/j.ipm.2019.102055},

year  = {2019},

date = {2019-01-01},

journal = {Information Processing and Management},

volume = {56},

number = {6},

abstract = {Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks. © 2019 Elsevier Ltd},

keywords = {Argumentation mining, Argumentation models, Argumentation tools, Computational linguistics, machine learning, Social media},

pubstate = {published},

tppubtype = {article}

}

@article{Kakali2011474,

title = {A novel HMM-based learning framework for improving dynamic wireless push system performance},

author = { V.L. Kakali and P.G. Sarigiannidis and G.I. Papadimitriou and A.S. Pomportsis},

url = {https://www.researchgate.net/publication/220511276_A_novel_HMM-based_learning_framework_for_improving_dynamic_wireless_push_system_performance},

doi = {10.1016/j.camwa.2011.05.028},

year  = {2011},

date = {2011-01-01},

journal = {Computers and Mathematics with Applications},

volume = {62},

number = {1},

pages = {474-485},

abstract = {A new machine learning framework is introduced in this paper, based on the hidden Markov model (HMM), designed to provide scheduling in dynamic wireless push systems. In realistic wireless systems, the clients' intentions change dynamically, hence a cognitive scheduling scheme is needed to estimate the desirability of the connected clients. The proposed scheduling scheme is enhanced with self-organized HMMs, supporting the network with an estimated expectation of the clients' intentions, since the system's environment characteristics alter dynamically and the base station (server side) has no a priori knowledge of such changes. Compared to the original pure scheme, the proposed machine learning framework succeeds in predicting the clients' information desires and overcomes the limitation of the original static scheme, in terms of mean delay and system efficiency. © 2011 Elsevier Ltd. All rights reserved.},

keywords = {Hidden Markov models, machine learning, Scheduling, wireless networks},

pubstate = {published},

tppubtype = {article}

}