Cognitive,social and teaching presence in a virtual world and a text chat

Using a framework of cognitive, social, and teaching presence, the nature of learning experiences in a three-dimensional virtual world environment (Second Life) and a text-chat learning environment without visuals (TeachNet) were investigated. A mixed method of code frequencies, coherence graphs, interviews, and a survey was used. The results revealed that the TeachNet debates included more cognitive presence codes that indicate higher levels of cognitive processing than in SL debates. The teams were significantly different from each other in the collaboration style for developing arguments and in the ways to use utterances associated with cognitive, social, and teaching presences, and the groups’ collaboration style became more established with more experience with the tasks. The three critical factors–tool, tasks and group cohesion–that affect cognitive, teaching, and social presence are discussed.     

Repeated auctions for robust task execution by a robot team

We present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.     

An enhanced exact procedure for the absolute robust shortest path problem

The aim of this paper is to investigate the use of heuristic information to efficiently solve to optimality the robust shortest path problem. Starting from the exact algorithm proposed by Murty and Her, we describe how this algorithm can be enhanced by using heuristic rules and evaluation functions to guide the search. The efficiency of the proposed enhanced approach is tested over a range of random generated instances. Our computational results indicate that the use of heuristic criteria is able to speed up considerably the search and that the enhanced exact solution method outperforms the state‐of‐the‐art algorithm proposed by Murty and Her in most of the instances.     

Filling gaps in diameter measurements on standing tree boles in the urban forest of Thessaloniki,Greece

Missing data are omnipresent in forestry research, and this poses problems in the analysis of primary data. Many statistical problems have been viewed as missing data problems. To cope with incomplete data, several methods are currently being used. They are all based on assumptions some of which might not be valid in a particular case. The choice mostly depends on the objective of the study. Considerable mensuration research is motivated by the need for yield projections that can support forest management decisions. This paper is focused on a new approach for filling gaps in diameter measurements on standing tree boles. Dealing with this problem, an attempt was made to examine the applicability of artificial neural network models for missing data estimation and to use the estimated values in the subsequent analysis. The procedure that should be followed in the development of such models is outlined. The results show good performance of the examined ANN models compared to regression treatments for missing data and ANN models demonstrate their adequacy and potential for filling gaps in diameter measurements on standing tree boles. The ANN models applied in this study are sufficiently general and have great potential to be applicable for estimating the missing values of many variables in environmental applications.     

Making it easier for older people to talk to smart homes: the effect of early help prompts

It is well known that help prompts shape how users talk to spoken dialogue systems. This study investigated the effect of help prompt placement on older users’ interaction with a smart home interface. In the dynamic help condition, help was only given in response to system errors; in the inherent help condition, it was also given at the start of each task. Fifteen older and sixteen younger users interacted with a smart home system using two different scenarios. Each scenario consisted of several tasks. The linguistic style users employed to communicate with the system (interaction style) was measured using the ratio of commands to the overall utterance length (keyword ratio) and the percentage of content words in the user’s utterance that could be understood by the system (shared vocabulary). While the timing of help prompts did not affect the interaction style of younger users, it was early task-specific help supported older users in adapting their interaction style to the system’s capabilities. Well-placed help prompts can significantly increase the usability of spoken dialogue systems for older people.     

SVM practical industrial application for mechanical faults diagnostic

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine‘s vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM’s it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.     

A semi-automatic strategy for achieving learning content and practice repurposing

The development of instructional content using Information Technologies is an expensive, time-consuming and complex process that requires new methodologies. It was in this context that the concept of Learning Objects (LOs) was proposed in order to promote reuse. However, this goal is not yet fully attained and new contributions to increase reuse are still welcome. Besides, if content is conveyed in LOs that are easier to reuse, they must be combined and sequenced in order to build more elaborated and complex content. This paper presents a strategy to deal with these problems based on the definition of small LOs here called Component Objects (COs). These COs are structured and combined according to a conceptual metamodel, which is the basis for the definition of conceptual schemas representing the existing material, including not only content but also practice. This strategy for searching, extracting, and sequencing COs, supports a teacher to better control the implementation of complex content, reducing errors in the authoring process. This approach includes a specification language and an algorithm for semi-automatic sequencing learning content and practice. Finally, a case study that shows the proposed approach and some results of using the algorithm are presented.     

A hybrid particle swarm optimization approach for the sequential ordering problem

The sequential ordering problem is a version of the asymmetric travelling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are fulfilled, and the objective is to find a feasible solution with minimum cost.     

Self-stabilizing minimum degree spanning tree within one from the optimal degree

We propose a self-stabilizing algorithm for constructing a Minimum Degree Spanning Tree (MDST) in undirected networks. Starting from an arbitrary state, our algorithm is guaranteed to converge to a legitimate state describing a spanning tree whose maximum node degree is at most Δ^∗+1, where Δ^∗ is the minimum possible maximum degree of a spanning tree of the network.To the best of our knowledge, our algorithm is the first self-stabilizing solution for the construction of a minimum degree spanning tree in undirected graphs. The algorithm uses only local communications (nodes interact only with the neighbors at one hop distance). Moreover, the algorithm is designed to work in any asynchronous message passing network with reliable FIFO channels. Additionally, we use a fine grained atomicity model (i.e., the send/receive atomicity). The time complexity of our solution is O(mn²logn) where m is the number of edges and n is the number of nodes. The memory complexity is O(δlogn) in the send-receive atomicity model (δ is the maximal degree of the network).