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 knowledge, this will be the first attempt to mine social media to extract quantitative and qualitative data for the art market. Although there are previous works on analyzing and predicting other markets, these methodologies cannot be applied directly to the art market. In our proposed methodology, artists will be treated as brands. That is, we will mine Twitter posts that mention specific artists' names and attempt to rank artists in a similar manner as brand equity and awareness would be measured. The particularities of the art market are considered mainly in the construction of a topic-specific user network where user expertise and influence is evaluated and later used to rank artists. The proposed ranking system is evaluated against two other available systems to identify the advantages it can offer.
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 "problem" the forming of a curriculum for a 21st century School of Fine Arts. The logic and process of problem-solving will be followed, in the same manner as it would be followed in a School of Engineering. This approach will allow us to identify and study the different perceptions and practices in art and engineering education. As we would do for solving any other problem we will first examine the situation from which we start, the input, and the situation in which we want to conclude, the output. In our case, initially the reasons for which someone enters a school, their expectations from it and eventually the knowledge, skills, and abilities that they are expected to have as graduates of this school to accomplish a successful career path. The study of the defined problem of forming a curriculum will be tackled with a systems design approach that allows us to move from an initial to a final desired state. This will lead us to identify the key differences and/or similarities in the two kinds of Schools that are examined. More importantly though it will allow us to explore the different ways in which a system like that can be designed while at the same time it will be juxtaposed with the existing curricula and teaching methods in Schools of Fine Arts.
Ο καλλιτεχνικός θεσμός έχει αποδεχθεί ότι τα πάντα δύνανται να καταστούν έργα τέχνης εφόσον είναι μοναδικά αντικείμενα, φτιαγμένα από έναν καλλιτέχνη. Τι γίνεται όμως όταν ο δημιουργός τους δεν έχει αυτήν την πρόθεση ή ούτε καν συνείδηση ότι φτιάχνει ένα έργο; Όταν το έργο είναι ανώνυμο; Όταν η ελευθερία της έκφρασης δεν τον ενδιαφέρει καθόλου; Όταν το έργο δεν είναι αντικείμενο αισθητικής ανιδιοτέλειας αλλά σεξουαλικού αισθησιασμού; Όταν το λογισμικό και όχι ο άνθρωπος έχει τον πρώτο λόγο στη δημιουργία; Όταν το έργο τέχνης εκδίδεται ως καθημερινή εφημερίδα; Και τι συμβαίνει όταν τα έργα τέχνης στην εποχή τους και μέχρι σήμερα παραμένουν αφανή; Αποτελούν εξίσου τέχνη με αυτά των μουσείων και των βιβλίων ιστορίας της τέχνης ή μία τέχνη δεύτερης κατηγορίας; Αυτά τα ερωτήματα πραγματεύεται η παρούσα έκδοση με αφορμή τα έργα που εκτέθηκαν στο φεστιβάλ RE-culture 4 που πραγματοποιήθηκε το 2016 στην Πάτρα με τις ακόλουθες θεματικές: τέχνη των ψυχικά ασθενών, ανώνυμη τέχνη, τέχνη από τη Βόρεια Κορέα, το γκράφιτι, το σεξ στην τέχνη, ψυχεδελική τέχνη και το ελλαδικό καλλιτεχνικό αντεργκράουντ. Παράλληλα, φιλοξενούνται αφιερώματα στα νέα μέσα και στους καλλιτέχνες: Τζούλιο Καΐμη, Τάκη Γερμένη και Αντώνη Βάθη μαζί με έργα που επελέγησαν μέσω ανοικτής πρόσκλησης για τις θεματικές του φεστιβάλ.
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, mapping, path planning and obstacle avoidance tasks. A probabilistic framework for the navigation tasks of localization, path planning and obstacle avoidance in dynamic environments is presented based on the Partially Observable Markov Decision Process (POMDP) model. POMDPs have the major shortcoming of their extreme computational complexity and hence they have been mainly used in robotics as high level path planners only. An hierarchical representation of POMDPs is introduced specifically designed for the autonomous robot navigation problem and termed as the Robot Navigation-Hierarchical POMDP (RNHPOMDP). Integration of human motion prediction into the navigation model is utilized with two kinds of prediction: short-term and long-term prediction. The book should be useful to students in Robotics utilizing POMDPs.
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 use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved on-line at each time step and provides the actual actions the robot performs. Obstacle avoidance is performed within the predictive navigation model with a novel approach by deciding paths to the goal position that are not obstructed by other moving objects movement with the use of future motion prediction and by enabling the robot to increase or decrease its speed of movement or by performing detours. The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed.
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 to possess universal approximation properties, while ensuring robustness to noise and missing data, good generalisation and rapid learning. In this work, a polynomial neural network is proposed, whose structure and weight values are determined with the use of evolutionary computing. The resulting networks allow an insight into the relationships underlying the input data, hence allowing a qualitative analysis of the models' performance. The approach is tested on a variety of non-linear time series data.
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. As such, it is used for localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP is introduced. The latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been experimentally validated in real 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 localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP, that holds all the information regarding motion and sensor uncertainty is introduced, which makes our hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been tested extensively in a real-world environment.
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 when planning the robot trajectory to its goal position. This is extended in the current paper by also using this prediction to decide if the robot should change its speed to avoid an obstacle more effectively. The robot can choose to move at three different speeds: slow, normal and fast. A hierarchical partially observable Markov decision process (POMDP) controls the robot movement. The POMDP formulation is not altered to accommodate for the three different speeds, to avoid the increase of the size of the state space. Instead, a modified solution of POMDPs is used.
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-term and long-term. The short-term prediction refers to the one-step ahead prediction and the long-term to the prediction of the final destination point of the obstacle's movement. The robot movement is controlled by a partially observable Markov decision process (POMDP). POMDPs are utilized because of their ability to model information about the robot's location and sensory information in a probabilistic manner. The solution of a POMDP is computationally expensive and thus a hierarchical representation of POMDPs is used.
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 tasks include the localization, mapping, path planning and obstacle avoidance tasks. Solving any of these tasks is a hard problem by itself. The reason stems from the inherent complexity associated with both the robot and its environment, each of them being an extremely complex dynamical system with uncertainty involved.
In this thesis, we propose a probabilistic framework for mobile robot navigation in dynamic environments based on the Partially Observable Markov Decision Process (POMDP) model. The proposed model is able to perform the navigation tasks of localization, path planning and obstacle avoidance. POMDPs are models for sequential decision making where the world in which the robot operates is partially observable, i.e. the true underlying robot's state is not known, and the outcome of actions it executes is modelled probabilistically. As such POMDPs perform localization and path planning in a probabilistic manner.
POMDPs have the major shortcoming of their extreme computational complexity and hence they have been mainly used in robotics as high level path planners only. In this thesis, we propose a novel hierarchical representation of POMDPs, specifically designed for the autonomous robot navigation problem and termed as the Robot Navigation-Hierarchical POMDP (RN-HPOMDP). The proposed hierarchical POMDP can efficiently model large real-world environments and is amenable to real time solution. This is achieved mainly due to the design choice of modelling the state transition and observation functions dependent only on the robot motion model and not on the environment as it is commonly used in the POMDP literature. Furthermore, the notion of the reference POMDP (rPOMDP) is introduced that infers the robot motion model in a very small POMDP and it transfers this information to the hierarchical structure while being solved. The environment specific information is modelled within the reward function of the RN-HPOMDP. The employed model is utilized as a unified probabilistic navigation framework that accommodates for localization, path planning and obstacle avoidance. Hence, real-time solution of the RN-HPOMDP is essential since no other external modules are utilized and paths have to be replanned at each time step.
The RN-HPOMDP has been developed for the application of robot navigation in dynamic real-world environments that are highly populated. Thus, it is desirable for the robot to perform obstacle avoidance in a manner that resembles the human motion for obstacle avoidance. That is, the robot should be able to decide the most suitable obstacle avoidance behavior based on the state of the environment. Therefore, the robot can decide to either perform a detour or follow a completely new path to the goal and also modify its speed of movement (increase it or decrease it) to bypass an obstacle or let it move away respectively. Any of the above four distinct behaviors for obstacle avoidance should be decided well before the robot comes too close to the obstacle. For that reason, future motion prediction of obstacles is employed. Two kinds of prediction are utilized: short-term and long-term prediction. Short term prediction refers to the one-step ahead prediction whereas long-term prediction refers to the prediction of the final destination point of the obstacle's movement. Both kinds of prediction are integrated into the reward function of the RN-HPOMDP and the speed decision is performed through a modified solution of the RN-HPOMDP. As a result, the RN-HPOMDP can decide the optimal obstacle avoidance behavior based on the current and the predicted state of the environment without the intervention of any other external module.
Experimental results have shown the applicability and effectiveness of the proposed framework for the navigation task. The robustness and the probabilistic nature of the RN-HPOMDP as well as the future motion prediction are required to be able to perform efficiently and effectively in dynamic real-world environments that are highly populated.
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 develops a general framework of evolutionary computing methods. Genetic Algorithms, Niched Genetic Algorithms and Evolutionary Algorithms are introduced and their applicability to neural networks optimization is examined.
Following, the problem of time series prediction is formulated. The time series prediction problem is formulated as a system identification problem, where the input to the system is the past values of a time series, and its desired output is the future values of a time series. Then, the Group Method of Data Handling (GMDH) algorithms are examined in detail. These algorithms use simple partial descriptions, usually polynomials, to gradually build complex models. The hybrid method of GMDH and GAs, Genetics-Based Self-Organizing Network (GBSON), is also examined.
The method implemented for the time series prediction problem is based on the GBSON method. It uses a niched generic algorithm to determine the partial descriptions of the final model, as well as the structure of the neural network used to model the time series to be predicted. Finally, the results obtained with this method are compared with the results obtained by the GMDH algorithm.