Session Program

Abstract - In recent years, Augmented Reality (AR) started transitioning from an experimental technology to a more mature area, with new types of applications in entertainment, marketing, education, retail, transportation, manufacturing, construction, and other industries. One of the main challenges for AR-based field service tools is to help users to correctly locate company's assets and infrastructure in the field. This paper presents an AR system using private maps to find company's assets to support field workforce tasks. The AR system is based on fuzzy logic mechanisms to provide the user with directions for asset location by comparing his/her current position with assets' location in real-time. Auditory and visual feedback is provided via a head mounted display (HMD), enhancing user's perception to achieve human augmentation.

Abstract - In this paper we proposed a writer adaptation system based on an adaptive fuzzy inference system (AFIS)that can be plug-in for any writer-independent handwriting recognition systems. The AFIS starts with an empty rule set. Subsequently, a supervised incremental learning algorithm is operated. When the user reports a misclassification, rule are added or updated. The proposed learning algorithm is evaluated by the adaptation of a writer-independent recognition system (LipiTk). Moreover, the results using a benchmark database named LaViola prove the efficiency of the proposed system. The error rate reduction varies between 66.32\% and 41.05\%.

Abstract - A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots.

Abstract - In this paper, the simple movement (walking dog, crawling human, and walking human) recognition system using the Mamdani fuzzy inference system is introduced. The membership functions of each input feature are generated automatically without experts' prior knowledges. The system produces a very high recognition rate, i.e., 93.97\%, on the validation set of the cross validation. However, there are some misclassifications between walking dog and crawling human classes. The misclassifications are mainly from the incomplete segmentation of the objects of interest.

Abstract - Autonomous robots must operate in complex and changing environments subject to requirements on their behaviour. Verifying absolute satisfaction (true or false) of these requirements is challenging. Instead, we analyse requirements that admit flexible degrees of satisfaction. We analyse vague requirements using fuzzy logic, and probabilistic requirements using model checking. The resulting analysis method provides a partial ordering of system designs, identifying trade-offs between different requirements in terms of the degrees to which they are satisfied. A case study involving a home care robot interacting with a human is used to demonstrate the approach.

Abstract - Analyzing information from microblogs like Twitter is an important issue for the modern society that confronts new challenges. Its Security is the most crucial problem that requires one to anticipate different events, to track specific information or given persons. The detection of suspicious tweets a a main task in this issue, however tweets are generally not well written, contaminated by errors and may use metaphors, slang terms, and colloquialisms. Probabilistic methods inherited from information retrieval are systematically applied to social data analysis. In this paper, we combine probability theory and fuzzy logic in a harmonious approach, where the first one is more related to frequencies, whereas the second concerns qualitative modeling of suspicion and normality. The experimental results shows that the algorithm leveraging probability and fuzziness outperform the on based only on probability.