Session Program

Abstract - Sequences classification problems in recorded videos are often very complex and have too much uncertainty. In many application domains, such as video event activity detection, sequences of events occurring over time need to be studied in order to summarize the key events from the video clips. In most existing adaptive sequences classification systems, Dynamic Time Warping (DTW) and Gaussian Mixture Mode (GMM) are used as the core techniques in measuring similarity between two temporal sequences, which may vary in speed. Hence, there is a need to develop video event detection systems capable of classifying important events within long video sequences. This paper presents a novel system based on DTW and Interval Type-2 Fuzzy Logic Systems employing the Big Bang Big Crunch (BB-BC) algorithm for video activity detection and classification of critical events from the large-scale data of soccer videos.

Abstract - In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function, -- "Elliptic membership function". After briefly explaining the motivation behind the suggestion of the elliptic membership function, we analyse the uncertainty distribution along its support, and we compare its uncertainty modeling capability with the existing membership functions. We also show how the elliptic membership functions perform in fuzzy arithmetic. In addition to its extra advantages over the existing type-2 fuzzy membership functions such as having decoupled parameters for its support and width, this novel membership function has some similar features to the Gaussian and triangular membership functions in addition and multiplication operations. Finally, we have tested the prediction capability of elliptic membership functions using interval type-2 fuzzy logic systems on US Dollar/Euro exchange rate prediction problem. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic membership functions have comparable prediction results when compared to Gaussian and triangular membership functions.

Abstract - Recently, there has been much research into effective representation and analysis of uncertainty in human responses, with applications in cyber-security, forest and wildlife management, and product development, to name a few. Most of this research has focused on representing the response uncertainty as intervals, e.g., "I give the movie between 2 and 4 stars." In this paper, we extend upon the model-based interval agreement approach (IAA) for combining interval data into fuzzy sets and propose the efficient IAA (eIAA) algorithm, which enables efficient representation of and operation on the fuzzy sets produced by IAA (and other interval-based approaches, for that matter). We develop methods for efficiently modeling, representing, and aggregating both crisp and uncertain interval data (where the interval endpoints are intervals themselves). These intervals are assumed to be collected from individual or multiple survey respondents over single or repeated surveys; although, without loss of generality, the approaches put forth in this paper could be used for any interval-based data where representation and analysis is desired. The proposed method is designed to minimize loss of information when transferring the interval-based data into fuzzy set models and then when projecting onto a compressed set of basis functions. We provide full details of eIAA and demonstrate it on real-world and synthetic data.

Abstract - Supplier companies aim to pursue an efficient resource allocation to different jobs over specific times and other constraints. Dynamic and unstructured environments and real-world situations incorporate a large amount of uncertainties which are difficult to model. This paper proposes a type-2 Fuzzy Logic System (FLS) for estimating the extra number of engineers required to allocate a certain number of jobs. The type-2 FLS was trained from the knowledge extracted dynamically from input data in order to estimate corresponding outputs for unseen data. The proposed methodology has been applied to real-world service provider industry in the workforce allocation domain. The system generated sensible results which outperformed the type-1 fuzzy logic based counterpart over unseen data.

Abstract - This paper presents a type-2 Fuzzy Logic System (FLS) to support technical employees in finding company's assets in outdoor settings. The system provides the user with directions for asset location by comparing his/her current position with assets' location in real-time, giving auditory and visual feedback via a Head Mounted Display (HMD). We carried out 35 path explorations in a predefined area to test the system. The results indicated that the proposed type-2 fuzzy logic produces better performance than the type-1 based fuzzy system, giving more precise indications to reach asset's position.

Abstract - P300 is one of the most widely studied event-related potentials. Unfortunately, most of the existing automatic P300 detection schemes require computations over repetitive trials in both training and recognition phases. Several attempts have recently been endeavored towards the single trial detection of the P300 signals. However, no acceptable solution to the problem is found till date. In the present work, we have attempted to address this problem in the light of latency and (amplitude) deflection of the signal. The intra- and inter-personal variations inherent in these features are managed by the uncertainty management characteristics of General Type-2 Fuzzy Sets. First, these sets are constructed by exploiting the knowledge obtained from different trials of a large number of subjects. The secondary membership functions of the Type-2 Fuzzy Sets are computed based on a novel density dependent measure of the primary membership functions in the footprint of uncertainty. Second, recognition of P300 in an unknown EEG trial is performed based on the agreement of measured feature values with the General Type-2 Fuzzy knowledge-base. Majority voting of the concerned electrodes makes the scheme more robust. The experimental results show that the proposed algorithm is capable of achieving 88.60\% accuracy in single trial detection of P300 instances, which is significantly higher than those obtained in state of the art algorithms.