1,721,035 research outputs found
Using the audio of 8-bit video games to monitor web marketing campaigns
No full textMonitoring the performance of a web marketing
campaign is usually a long-lasting, low-effort but
distracting task, where a user repeatedly glances at some
sort of visual analytics tools to check whether the campaign
is going well. In this paper, we explore an alternative
approach for this task, where the performance of a web
marketing campaign is monitored through sonifcation,
using the soundset of popular 8-bit arcade video games. On
one hand, sonifcation would allow a user to be constantly
informed about the current state of the campaign without
being distracted. On the other hand, the sound metaphors
coming from popular 8-bit arcade video games would be
able to convey information about the status of the campaign
in a simple and effective way (i.e., if the sonifcation of a
campaign resembles the audio of a successful game session,
then the campaign is going well). We investigated
this idea by developing a prototype system for the sonifcation
of the behavior of a web server activity through a
confgurable set of sound metaphors. We then analyzed
the effectiveness of our approach by conducting a simple
experimental study. This was done, frst, by sonifying the
progress of a given web marketing campaign using the
soundset of two popular 8-bit video games: Super Mario
Bros and Bubble Bobble. The outcoming soundtrack was then used in a controlled setting to assess the performance
of a group of 20 participants listening to our soundtrack
under different work conditions
Exploiting infomobility to support the planning and the management of evacuation processes
No full text
Improving the experimental analysis of tampered image detection algorithms for biometric systems
No full textAbstract In this paper we deal with the experimental evaluation of tampered image detection algorithms. These algorithms aim at establishing if any manipulation has been carried out on a digital image. In detail, we focus on the evaluation of the CASIA Tampered Image Detection Evaluation (CASIA TIDE) public dataset of images, the de facto standard for evaluating these class of algorithms. Our analysis has been performed using the algorithm of Lin et al. for JPEG tampered image detection as benchmark. The results proved that the images of the dataset contain some statistical artifacts that may help the detection process. To confirm this, we first used this dataset to evaluate the performance of the Lin et al. algorithm. According to our results, the considered algorithm performs very well on this dataset. Some variants of the original algorithm have been developed expressly tuned on these artifacts. These variants performed better than their original counterpart. Then a new unbiased dataset has been assembled and a new set of experiments has been executed with these images. The results showed that the performance of the algorithm and its variants radically decreased, proving that the CASIA TIDE statistical artifacts cause interferences on the detection process. This problem is particularly important in the biometric field, because many image-based biometric systems rely on the assumption that input images have not been manipulated. Indeed, a faithful experimental evaluation must be based on unbiased input dataset to get well founded results. Therefore, the selection of a reliable image tampering detection algorithm is crucial. A preliminary version of this work has been presented in Cattaneo and Roscigno (2014) [6]
Improving the experimental analysis of tampered image detection algorithms for biometric systems
No full textIn this paper we deal with the experimental evaluation of tampered image detection algorithms. These algorithms aim at establishing if any manipulation has been carried out on a digital image. In detail, we focus on the evaluation of the CASIA Tampered Image Detection Evaluation (CASIA TIDE) public dataset of images, the de facto standard for evaluating these class of algorithms. Our analysis has been performed using the algorithm of Lin et al. for JPEG tampered image detection as benchmark. The results proved that the images of the dataset contain some statistical artifacts that may help the detection process. To confirm this, we first used this dataset to evaluate the performance of the Lin et al. algorithm. According to our results, the considered algorithm performs very well on this dataset. Some variants of the original algorithm have been developed expressly tuned on these artifacts. These variants performed better than their original counterpart. Then a new unbiased dataset has been assembled and a new set of experiments has been executed with these images. The results showed that the performance of the algorithm and its variants radically decreased, proving that the CASIA TIDE statistical artifacts cause interferences on the detection process. This problem is particularly important in the biometric field, because many image-based biometric systems rely on the assumption that input images have not been manipulated. Indeed, a faithful experimental evaluation must be based on unbiased input dataset to get well founded results. Therefore, the selection of a reliable image tampering detection algorithm is crucial. A preliminary version of this work has been presented in Cattaneo and Roscigno (2014) [6]
Toward a sustainable mobility: integrating car emission model in traffic management processes
No full text
- …
