Efficient PAC Learning for Episodic Tasks with Acyclic State Spaces

This paper considers the problem of computing an optimal policy for a Markov decision process, under lack of complete a priori knowledge of (1) the branching probability distributions determining the evolution of the process state upon the execution of the different actions, and (2) the probability distributions characterizing the immediate rewards returned by the environment as a result of the execution of these actions at different states of the process. In addition, it is assumed that the underlying process evolves in a repetitive, episodic manner, with each episode starting from a well-defined initial state and evolving over an acyclic state space. A novel efficient algorithm for this problem is proposed, and its convergence properties and computational complexity are rigorously characterized in the formal framework of computational learning theory. Furthermore, in the process of deriving the aforementioned results, the presented work generalizes Bechhofer’s “indifference-zone” approach for the ranking & selection problem, that arises in statistical inference theory, so that it applies to populations with bounded general distributions.

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.     

SIMULATION OF TRANSPORT DYNAMICS IN FLUIDIZED-BED DRYERS

A mathematical model for predicting three-dimensional, two-phase flow, heat and mass transfer inside fluidized-bed dryers has been developed. The model consists of the full set of partial-differential equations that describe the conservation of mass, momentum and energy for both phases inside the dryer, and is coupled with correlations concerning interphase momentum-, heat-, and mass-transfer.

It is shown that the model can predict the most important engineering aspects of a fluidized-bed dryer including pressure drop, particle holdup, temperature distribution in both phases as well as drying efficiency all over the fluidized-bed. Plug-flow conditions are predicted for the gas phase, while back-mixing is predicted for the particles.

The effect of particle mass-flow-rate on fluidized-bed dryer performance is evaluated. It is shown that the lower the particle mass flow-rate, the more intense the horizontal moisture gradients, while the higher the particle rate the more uniform the moisture distribution throughout the bed.     

Making assistive reading tools user friendly: a new platform for Greek dyslexic students empowered by automatic speech recognition

This work presents our effort to incorporate a state of the art speech recognition engine into a new platform for assistive reading for improving reading ability of Greek dyslexic students. This platform was developed in the framework of the Agent-DYSL, IST project, and facilitates dyslexic children in learning to read fluently. Unlike previously presented approaches, the aim of the system is not only to enable access to the reading materials within an inclusive learning system but to promote the development of reading skills by adjusting and adapting in the light of feedback to the system. The idea is to improve speech recognition performance so that gradually increase the reading capabilities of the user, gradually diminish the assistance provided, till he is able to read as a non-dyslexic reader. The evaluation results show that both learners’ reading pace and learners’ reading accuracy were increased.     

SIMULATION OF TRANSPORT DYNAMICS IN FLUIDIZED-BED DRYERS

ABSTRACT

A mathematical model for predicting three-dimensional, two-phase flow, heat and mass transfer inside fluidized-bed dryers has been developed. The model consists of the full set of partial-differential equations that describe the conservation of mass, momentum and energy for both phases inside the dryer, and is coupled with correlations concerning interphase momentum-, heat-, and mass-transfer.

It is shown that the model can predict the most important engineering aspects of a fluidized-bed dryer including pressure drop, particle holdup, temperature distribution in both phases as well as drying efficiency all over the fluidized-bed. Plug-flow conditions are predicted for the gas phase, while back-mixing is predicted for the particles.

The effect of particle mass-flow-rate on fluidized-bed dryer performance is evaluated. It is shown that the lower the particle mass flow-rate, the more intense the horizontal moisture gradients, while the higher the particle rate the more uniform the moisture distribution throughout the bed.     

Optimal flow control in acyclic networks with uncontrollable routings and precedence constraints

This paper introduces a novel optimal flow control problem that seeks to convey a specified amount of fluid to each of the nodes of an acyclic digraph with a single source node, while minimizing the total amount of fluid inducted into the network. Two factors complicating the aforementioned task are (i) the presence of nodes with uncontrollable routing of the traversing flow and (ii) a set of precedence constraints regarding the satisfaction of the nodal fluid requirements. It is shown that the considered problem can be naturally formulated as a continuous-time optimal control problem that can be reduced to a hybrid optimal control problem with controlled switching. This property subsequently enables the solution of the considered problem through a Mixed Integer Programming formulation. Additional results in the paper establish the NP-hardness of the considered problem, highlight its affinity to some well known scheduling problems, and offer guidelines that can alleviate the increased problem complexity.     

Optimal Node Visitation in Acyclic Stochastic Digraphs with Multi-threaded Traversals and Internal Visitation Requirements

The original definition of the problem of optimal node visitation (ONV) in acyclic stochastic digraphs concerns the identification of a routing policy that will enable the visitation of each leaf node a requested number of times, while minimizing the expected number of the graph traversals. The original work of Bountourelis and Reveliotis (2006) formulated this problem as a Stochastic Shortest Path (SSP) problem, and since the state space of this SSP formulation is exponentially sized with respect to the number of the target nodes, it also proposed a suboptimal policy that is computationally tractable and asymptotically optimal. This paper extends the results of Bountourelis and Reveliotis (2006) to the cases where (i) the tokens traversing the graph can “split” during certain transitions to a number of (sub-)tokens, allowing, thus, the satisfaction of many visitation requirements during a single graph traversal, and (ii) there are additional visitation requirements attached to the internal graph nodes, which, however, can be served only when the visitation requirements of their successors have been fully met. In addition, the presented set of results establishes stronger convergence properties for the proposed suboptimal policies, and it provides a formal complexity analysis of the considered ONV formulations. From a practical standpoint, the extension of the original results performed in this paper enables their effective usage in the application domains that motivated the ONV problem, in the first place.