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This work represents any distribution of data by an Intervals’ Number (IN), hence it represents all-order data statistics, using a “small” number of L intervals. The INs considered are induced from images of grapes that ripen. The objective is the accurate prediction of grape maturity. Based on an established algebra of INs, an optimizable IN-regressor is proposed, implementable on a neural architecture, toward predicting future INs from past INs. A recursive scheme tests the capacity of the IN-regressor to learn the physical “law” that generates the non-stationary time-series of INs. Computational experiments demonstrate comparatively the effectiveness of the proposed techniques.
Christos Bazinas; Eleni Vrochidou; Chris Lytridis; Vassilis Kaburlasos. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Engineering Proceedings 2021, 5, 12 .
AMA StyleChristos Bazinas, Eleni Vrochidou, Chris Lytridis, Vassilis Kaburlasos. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Engineering Proceedings. 2021; 5 (1):12.
Chicago/Turabian StyleChristos Bazinas; Eleni Vrochidou; Chris Lytridis; Vassilis Kaburlasos. 2021. "Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach." Engineering Proceedings 5, no. 1: 12.
The task of child engagement estimation when interacting with a social robot during a special educational procedure is studied. A multimodal machine learning-based methodology for estimating the engagement of the children with learning difficulties, participating in appropriate designed educational scenarios, is proposed. For this purpose, visual and audio data are gathered during the child-robot interaction and processed towards deciding an engaged state of the child or not. Six single and three ensemble machine learning models are examined for their accuracy in providing confident decisions on in-house developed data. The conducted experiments revealed that, using multimodal data and the AdaBoost Decision Tree ensemble model, the children’s engagement can be estimated with 93.33% accuracy. Moreover, an important outcome of this study is the need for explicitly defining the different engagement meanings for each scenario. The results are very promising and put ahead of the research for closed-loop human centric special education activities using social robots.
George A. Papakostas; George K. Sidiropoulos; Chris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Efi Kourampa; Elpida Karageorgiou; Petros Kechayas; Maria T. Papadopoulou. Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning. Mathematical Problems in Engineering 2021, 2021, 1 -10.
AMA StyleGeorge A. Papakostas, George K. Sidiropoulos, Chris Lytridis, Christos Bazinas, Vassilis G. Kaburlasos, Efi Kourampa, Elpida Karageorgiou, Petros Kechayas, Maria T. Papadopoulou. Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning. Mathematical Problems in Engineering. 2021; 2021 ():1-10.
Chicago/Turabian StyleGeorge A. Papakostas; George K. Sidiropoulos; Chris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Efi Kourampa; Elpida Karageorgiou; Petros Kechayas; Maria T. Papadopoulou. 2021. "Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning." Mathematical Problems in Engineering 2021, no. : 1-10.
Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.
Eleni Vrochidou; Chris Lytridis; Christos Bazinas; George Papakostas; Hiroaki Wagatsuma; Vassilis Kaburlasos. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics 2021, 9, 1063 .
AMA StyleEleni Vrochidou, Chris Lytridis, Christos Bazinas, George Papakostas, Hiroaki Wagatsuma, Vassilis Kaburlasos. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics. 2021; 9 (9):1063.
Chicago/Turabian StyleEleni Vrochidou; Chris Lytridis; Christos Bazinas; George Papakostas; Hiroaki Wagatsuma; Vassilis Kaburlasos. 2021. "Brain Signals Classification Based on Fuzzy Lattice Reasoning." Mathematics 9, no. 9: 1063.
The outbreak of the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV2) has resulted in a significant disruption of almost all aspects of everyday life. Several governments around the world have adopted emergency actions to reduce spreading of the virus, which included suspension of non-essential activities and the implementation of social distancing practices. In our case, governmental measures have resulted in the suspension of our experimental protocol for testing the effectiveness of robot-based treatment of children diagnosed with Autism Spectrum Disorder (ASD) compared to conventional human (therapist)-based treatment. These circumstances led to an investigation of the potential of tele-consulting. This paper describes alternatives to implement synchronous and asynchronous therapeutic sessions for children already participating in the protocol, in order to reduce the negative effects of the strict cessation of the in-person sessions. The usefulness of our approach was assessed by recording the children’s and the parent’s satisfaction via questionnaires. In addition, we compare satisfaction between the synchronous and asynchronous sessions. The results show that the approach has been very satisfactory and useful for both children and parents, and that this was especially the case for the robot-based material.
Chris Lytridis; Christos Bazinas; George Sidiropoulos; George A. Papakostas; Vassilis G. Kaburlasos; Vasiliki-Aliki Nikopoulou; Vasiliki Holeva; Athanasios Evangeliou. Distance Special Education Delivery by Social Robots. Electronics 2020, 9, 1034 .
AMA StyleChris Lytridis, Christos Bazinas, George Sidiropoulos, George A. Papakostas, Vassilis G. Kaburlasos, Vasiliki-Aliki Nikopoulou, Vasiliki Holeva, Athanasios Evangeliou. Distance Special Education Delivery by Social Robots. Electronics. 2020; 9 (6):1034.
Chicago/Turabian StyleChris Lytridis; Christos Bazinas; George Sidiropoulos; George A. Papakostas; Vassilis G. Kaburlasos; Vasiliki-Aliki Nikopoulou; Vasiliki Holeva; Athanasios Evangeliou. 2020. "Distance Special Education Delivery by Social Robots." Electronics 9, no. 6: 1034.
Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.
Chris Lytridis; Anna Lekova; Christos Bazinas; Michail Manios; Vassilis G. Kaburlasos. WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications. Mathematics 2020, 8, 413 .
AMA StyleChris Lytridis, Anna Lekova, Christos Bazinas, Michail Manios, Vassilis G. Kaburlasos. WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications. Mathematics. 2020; 8 (3):413.
Chicago/Turabian StyleChris Lytridis; Anna Lekova; Christos Bazinas; Michail Manios; Vassilis G. Kaburlasos. 2020. "WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications." Mathematics 8, no. 3: 413.
Chris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Violina Vassileva-Aleksandrova; Mohamed Youssfi; Mohammed Mestari; Vasileios Ferelis; Alexander Jaki. Social Robots as Cyber-Physical Actors in Entertainment and Education. 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) 2019, 1 .
AMA StyleChris Lytridis, Christos Bazinas, Vassilis G. Kaburlasos, Violina Vassileva-Aleksandrova, Mohamed Youssfi, Mohammed Mestari, Vasileios Ferelis, Alexander Jaki. Social Robots as Cyber-Physical Actors in Entertainment and Education. 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). 2019; ():1.
Chicago/Turabian StyleChris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Violina Vassileva-Aleksandrova; Mohamed Youssfi; Mohammed Mestari; Vasileios Ferelis; Alexander Jaki. 2019. "Social Robots as Cyber-Physical Actors in Entertainment and Education." 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) , no. : 1.
The introduction of social robots in education has been a major theme in robotics research in recent years. Various studies have been conducted that demonstrate the merits of using robots as teachers or teacher assistants. These studies are mainly focused on activities where the child interacts with the robot to achieve a certain educational or therapeutic goal. The principal reason that robots in education are observed to have a positive effect, is that children appear to be more engaged during the educational process when a robot is involved. This paper reviews the current literature on the subject of using social robots in education for the purposes of identifying the most appropriate methodologies in measuring the engagement levels of children during child-robot interactions, specifically focusing on interactions occurring in an educational or therapeutic setting.
Chris Lytridis; Christos Bazinas; George A. Papakostas; Vassilis Kaburlasos. On Measuring Engagement Level During Child-Robot Interaction in Education. Advances in Intelligent Systems and Computing 2019, 3 -13.
AMA StyleChris Lytridis, Christos Bazinas, George A. Papakostas, Vassilis Kaburlasos. On Measuring Engagement Level During Child-Robot Interaction in Education. Advances in Intelligent Systems and Computing. 2019; ():3-13.
Chicago/Turabian StyleChris Lytridis; Christos Bazinas; George A. Papakostas; Vassilis Kaburlasos. 2019. "On Measuring Engagement Level During Child-Robot Interaction in Education." Advances in Intelligent Systems and Computing , no. : 3-13.
Ioannis Kazanidis; George Palaigeorgiou; Christos Bazinas. Dynamic interactive number lines for fraction learning in a mixed reality environment. 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) 2018, 1 .
AMA StyleIoannis Kazanidis, George Palaigeorgiou, Christos Bazinas. Dynamic interactive number lines for fraction learning in a mixed reality environment. 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM). 2018; ():1.
Chicago/Turabian StyleIoannis Kazanidis; George Palaigeorgiou; Christos Bazinas. 2018. "Dynamic interactive number lines for fraction learning in a mixed reality environment." 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) , no. : 1.
Vassilis Kaburlasos; Christos Bazinas; George Siavalas; George Papakostas. Linguistic Social Robot Control by Crowd-Computing Feedback. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018, 2018, 1A1 -B13.
AMA StyleVassilis Kaburlasos, Christos Bazinas, George Siavalas, George Papakostas. Linguistic Social Robot Control by Crowd-Computing Feedback. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2018; 2018 ():1A1-B13.
Chicago/Turabian StyleVassilis Kaburlasos; Christos Bazinas; George Siavalas; George Papakostas. 2018. "Linguistic Social Robot Control by Crowd-Computing Feedback." The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018, no. : 1A1-B13.