Abstract. To improve on our earlier work on single-view-based ear biometrics, an estimation method is presented for the shooting angle of an ear image based on the summation of similarity scores over a threshold within a database of known shooting angles. Experimental results indicate that the estimation method can improve the robustness of ear recognition in varied poses.
This paper reports a problem solving environments (PSE) to assist researchers who study stochastic simulations such as reinforcement learning algorithms. They have to run their programs many times to compare their algorithms and find better sets of parameters for their programs. In order to reduce the working time, this system has three sub-systems: a distributed computing system, a data management system and a graph generation system. Using this system, we conduct experiments with human subjects. They register their programs, run them on a distributed computing system, obtain results automatically, and compare them graphically. As a result, a user obtained five times speedup for the work time. We present a relationship between development of algorithms and the three sub-systems.
We propose some models to simulate human choice behavior to select facilities. In order to consider models for human behavior, we develop a web-based experimental system to collect the data of human choice behavior in facility selection. Using the developed system, human subjects act their facility selection according to the information such as the congestion level of the facility selected last time. We examine several models to simulate human choice behavior based on the data collected by the experimental system. From our examination, we find that developing models according to experimental results improves simulation results.
Many researchers who study stochastic simulations such as reinforcement learning algorithms have to run their programs many times to compare developing algorithms and find better sets of parameters for their programs. In order to reduce their working time, we build a problem solving environments (PSE) system to assist them. Our system has three subsystems: a distributed computing system, a data management system and a graph generation system. In this paper, we present a relationship between developing algorithms and the three subsystems. Using our system, users register their programs, run them on a distributed computing system, obtain results automatically, and compare them graphically. We conduct experiments with human subjects. As a result, a user obtained five times speedup for his work time through executions, screening simulation data sets and comparing algorithms. As these subsystems for PSE system, it is important not only distributed computing but also supporting both data management and graph generation.
The objective of this paper is to find a new method to estimate real social networks based on observed data collected by questionnaire surveys.Studies on social networks have been increasing in order to analyze social phenomena from a micro viewpoint. Most social phenomena can be explained by micro-level interactions among people. Spread of rumor and pandemics are typical example of micro interaction? However, there has not been much work on an analysis of real social networks based on observed data. This study tries to establish a methodology that exploits a genetic algorithm to rebuild a social network based on the data observed indirectly from real social networks. This paper introduces our proposed method, which allows us to rebuild a social network to some extent from degree distributions of a target real social network.
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