Reading Your Mind: EEG during Reading Task

This paper demonstrates the ability to study the human reading behaviors with the use of Electroencephalography (EEG). This is a relatively new research direction because, obviously, gaze-tracking technologies are used specifically for those types of studies. We suspect, EEG, with the capability of recording brain-wave activities from the human scalp, in theory, could exhibit potential attributes to replace gaze-tracking in such research. To prove the concept, in this paper, we organized a BCI experiment and propose a model for effective classifying EEG data in comparison to the accuracy of gaze-tracking. The results show that by using EEG, we could achieve comparable results against the more established methods while demonstrating a potential live EEG applications. This paper also discusses certain points of consideration for using EEG in this work

Keyboard before Head Tracking Depresses User Success in Remote Camera Control

In remote mining, operators of complex machinery have more tasks or devices to control than they have hands. For example, operating a rock breaker requires two handed joystick control to position and fire the jackhammer, leaving the camera control to either automatic control or require the operator to switch between controls. We modelled such a teleoperated setting by performing experiments using a simple physical game analogue, being a half size table soccer game with two handles. The complex camera angles of the mining application were modelled by obscuring the direct view of the play area and the use of a Pan-Tilt-Zoom (PTZ) camera. The camera control was via either a keyboard or via head tracking using two different sets of head gestures called "head motion" and "head flicking" for turning camera motion on/off. Our results show that the head motion control was able to provide a comparable performance to using a keyboard, while head flicking was significantly worse. In addition, the sequence of use of the three control methods is highly significant. It appears that use of the keyboard first depresses successful use of the head tracking methods, with significantly better results when one of the head tracking methods was used first. Analysis of the qualitative survey data collected supports that the worst (by performance) method was disliked by participants. Surprisingly, use of that worst method as the first control method significantly enhanced performance using the other two control methods

Use of Noise to Augment Training Data: A Neural Network Method of Mineral-Potential Mapping in Regions of Limited Known Deposit Examples.

One of the main factors that affects the performance ofMLPneural networks trained using the backpropagation algorithm in mineral-potential mapping is the paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increase significantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using≥40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D≥(D/A), where Dis the percentage of deposits and Ais the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D≥(D/A), and area under the ROC curve, respectively

Enhancement of Subjective Logic for Semantic Document Analysis Using Hierarchical Document Signature

In this paper, an extension of Subjective Logic (SL) is presented which uses semantic information from a document to find 'opinions' about a sentence. This method computes semantic overlap of events (words or sentences) using Hierarchical Document Signature (HDS) and uses it as evidence to formulate SL belief measures to order sentences according to their importance. Stronger the opinion, more is the significance. These significant sentences then form extractive summaries of the document. The experimental results show that summaries generated by this method are more similar to human generated ones have outperformed the baseline summaries on average over all the data sets considered

Robot Cooperation without Explicit Communication by Fuzzy Signatures and Decision Trees

This paper presents a novel action selection method for multi robot task sharing problem. Two autonomous mobile robots try to cooperate for push a box to a goal position. Both robots equipped with object and goal sensing, but do not have explicit communication ability. We explore the use of fuzzy signatures and decision making system to intention guessing and efficient action selection. Virtual reality simulation is used to build and test our proposed algorithm

Construction of Fuzzy Signature from Data: An Example of SARS Pre-clinical Diagnosis System

There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. Fuzzy signatures are introduced to handle complex structured data and interdependent feature problems. Fuzzy signatures can also used in cases where data is missing. This paper presents the concept of a fuzzy signature and how its flexibility can be used to quickly construct a medical pre-clinical diagnosis system. A Severe Acute Respiratory Syndrome (SARS) pre-clinical diagnosis system using fuzzy signatures is constructed as an example to show many advantages of the fuzzy signature. With the use of this fuzzy signature structure, complex decision models in the medical field should be able to be constructed more effectively

Separated antecedent and consequent learning for Takagi-Sugeno fuzzy systems

In this paper a new algorithm for the learning of Takagi-Sugeno fuzzy systems is introduced. In the algorithm different learning techniques are applied for the antecedent and the consequent parameters of the fuzzy system. We propose a hybrid method for the antecedent parameters learning based on the combination of the Bacterial Evolutionary Algorithm (BEA) and the Levenberg-Marquardt (LM) method. For the linear parameters in fuzzy systems appearing in the rule consequents the Least Squares (LS) and the Recursive Least Squares (RLS) techniques are applied, which will lead to a global optimal solution of linear parameter vectors in the least squares sense. Therefore a better performance can be guaranteed than with a complete learning by BEA and LM. The paper is concluded by evaluation results based on high-dimensional test data. These evaluation results compare the new method with some conventional fuzzy training methods with respect to approximation accuracy and model complexity

Neural Networks for Modeling Esthetic Selection

Some real world problems require significant human interaction for labeling the data, which is very expensive. Worse, in some cases, the exercise of human judgement is inherently subjective and contextual, and so the entire labeling must be done in one session, which may be too long. Our domain is the automatic generation of Mondrian-like images with an interactive interface for the user to select images. We use back-propagation neural networks to learn an approximation of a viewer's aesthetic using 2 category labelled data (images liked/disliked). We construct a data set for training in a sequential fashion related to the interactive art appreciation task, and produce an output profile which well approximates a regression task, even trained on classification data. Analysis of the learned network produces some surprises, with the discovery of some input contributions which are unexpected to the user

Conservation of Relative Fuzziness: Retrospective and Triangular Extension

Fuzzy rule interpolation is one of the tools for reducing computational complexity of fuzzy systems, and can be used when there are gaps in the knowledge base. These gaps can be natural, due to cost, or due to rule base reduction. The fuzzy interpolation methods are all descendent techniques of Kóczy and Hirota's linear interpolation. In this paper we provide a retrospective on the development of these techniques, and then focus on an early technique of conservation of fuzziness which has advantages in interpolation in hierarchical fuzzy systems as only near flank information is meant to be used and this allows the interpolation between different levels in the fuzzy rule base hierarchy. We point out an error and rectify it using a triangular extension which restores the intuitive, philosophical and practical nature of the approach