Amalia F. Foka

• Bachelor of Engineering
• Professor (Assistant) at University of Ioannina

Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recent...

Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media art-works, but they have been focused on affect detection rather than affect generation. For affect generation, computational-ly creative methods need to be explored that late...

One of the contributing factors to the continuing debate among art historians over the use of computational methods in art history research is that they do not consider the core of today's art history research questions. The lack of close collaboration between the two involved research communities makes the definition of contemporary art-historical...

The author shows a methodology for constructing a network map of the artworld. The tweets posted by a manually comprised set of exemplar artworld actors and Natural Language Processing (NLP) methods are used to identify significant artworld actors, including those that do not own a Twitter account. Identified artworld actors form communities detect...

In this paper, we present the classification of electroencephalograph (EEG) signals produced by the first-octave musical notes of the piano as stimuli. The EEG classification of musical notes is attempted for the first time, to the best of our knowledge. This type of classification could be applied towards the development of Brain-Computer Interfac...

Digital technology since the early 1990s has influenced advanced economies not only the way and the speed of disseminating information about historical, economic and factual information about past artworks, but also how modern art is produced. It has also sparked widespread and interdisciplinary discussions about how it can help research for the hu...

The Invisible Structures of the Artworld draws inspiration from the contemporary artworld processes through which artists and artworks are appreciated and gain recognition as well as the fact that the artworld structure at any given time is not entirely known. Another point from which this work draws inspiration is the approaches taken for social n...

The continuous increase of social media users and content has brought new opportunities to understand individuals, groups and society by mining social media. Numerous methodologies have emerged that have been proven a valuable tool for a wide range of industries and markets. However, the same does not hold for the art world. In this paper, it is pr...

Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our...

The job description of an engineer, of any specialty - civil, electrical, mechanical, chemical or computer engineer, is common: to solve problems, to face challenges and problems of the real world with creative and innovative solutions aiming to make the world work better and contribute to progress. In the context of this paper we will consider as...

A primary goal in robotics research is to provide means for mobile platforms to perform autonomously within their environment. Depending on the task at hand, autonomous performance can be defined as the execution by the robot, without human intervention, of certain navigational tasks. Commonly addressed navigation tasks include the localization, ma...

This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where probabilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Predictive navigation is performed in a global manner with the...

Time series prediction involves the determination of an appropriate model, which can encapsulate the dynamics of the system, described by the sample data. Previous work has demonstrated the potential of neural networks in predicting the behaviour of complex, non-linear systems. In particular, the class of polynomial neural networks has been shown t...

This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory efficient. It will be referred to in this paper as the Robot Navigation–Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments....

This paper proposes a novel hierarchical representation of POMDPs that for the first time is amenable to real-time solution. It will be referred to in this paper as the Robot Navigation - Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments. As such, it is used for...

A primary goal in robotics research is to provide means for mobile platforms to perform autonomously within their environment. Depending on the task at hand, autonomous performance can be defined as the execution by the robot, without human intervention, of certain navigational tasks. In mobile robotics literature, commonly addressed navigation tas...

This paper introduces a methodology for avoiding obstacles by controlling the robot's velocity. Contemporary approaches to obstacle avoidance usually dictate a detour from the originally planned trajectory to its goal position. In our previous work, we presented a method for predicting the motion of obstacles, and how to make use of this prediction...

This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-...

This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-...

Real world problems are described by non-linear and chaotic processes which makes them hard to model and predict. The aim of this dissertation is to determine the structure and weights of a polynomial neural network, using evolutionary computing methods, and apply it to the non-linear time series prediction problem. This dissertation first develop...