Modern information systems make it increasingly easy to gain more insight into the public interest, which is becoming more and more important in diverse public and corporate activities and processes. The disadvantage of existing research that focuses on mining the information from social networks and online communities is that it doesn’t uniformly represent all population groups and that the content can be subjected to self-censoring or curation. In this paper we propose and describe a framework and a method for estimating public interest from the implicit negative feedback collected from the IPTV audience. Our research focuses primarily on the channel change events and their match with the content information obtained from closed captions. The presented framework is based on concept modeling, viewership profiling, and combines the implicit viewer reactions (channel changes) into an interest score. The proposed framework addresses both above mentioned disadvantages or concerns. It is able to cover a much broader population, and it can detect even minor variations in user behavior. We demonstrate our approach on a large pseudonymized real-world IPTV dataset provided by an ISP, and show how the results correlate with different trending topics and with parallel classical long-term population surveys.
COBISS.SI-ID: 11734356
Genetic programming was used to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In the training process, several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices were used. The genetically obtained optimization algorithm showed overall better performance than the original Nelder–Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder–Mead method while still performing better than the original method.
COBISS.SI-ID: 11276628
The aim of the study was to examine, whether by the human observer observed attention of the learner could be computationally modelled in real time. An experimental study on the real-time estimation of observed learners' attention given the task of touch-typing was performed. The study has shown, it is possible to model learner's attention in real time from psycho-physiological parameters with relatively high sampling frequency which is impossible to achieve with traditional assessment methods (e.g. between session self-reports). Human ratings obtained by human raters watching session video were used as ground truth. The results show, that computational models were relatively successful discriminating low versus high levels of attention within learners. The influence of individual psychophysiological and affective signals (eye gaze, pupil dilation, and valence and arousal) on the estimation of the observed attention was also examined. The results show that both affective dimensions (valence and arousal), as well as eye gaze offset significantly and most frequently influence the performance of the within-learner model.
COBISS.SI-ID: 11814484
Smart sport equipment employs various sensors for detecting its state and actions. The correct choice of the most appropriate sensor(s) is of paramount importance for efficient and successful operation of sport equipment. The article focuses on experiments for identification and selection of sensors that are suitable for the integration into a golf club with the final goal of their use in real time biofeedback applications. We test two orthogonally affixed strain gage sensors, 3-axis accelerometer, and 3-axis gyroscope. Field test results show that different types of golf swing and improper movement in early phases of golf swing can be detected with strain gage sensors.
COBISS.SI-ID: 11726676
With the ever-growing number of mobile devices, there is an explosive expansion in mobile data services. This represents a challenge for the traditional cellular network architecture to cope with the massive wireless traffic generated by mobile media applications. To meet this challenge, research is currently focused on the introduction of a small cell base station (BS) due to its low transmit power consumption and flexibility of deployment. However, due to a complex deployment environment and low transmit power of small cell BSs, the coverage boundary of small cell BSs will not have a traditional regular shape. Therefore, in this paper, we discuss the coverage boundary of an ultra-dense small cell network and give its main features: aeolotropy of path loss fading and fractal coverage boundary. Simple performance analysis is given, including coverage probability and transmission rate, etc., based on stochastic geometry theory and fractal theory. Finally, we present an application scene and discuss challenges in the ultra-dense small cell network.
COBISS.SI-ID: 11929172