Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.     

Automatic cluster detection in Kohonen's SOM.

Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM. In this paper, this problem is addressed by introducing an enhanced version of the Clusot algorithm. This algorithm consists of two main steps: 1) the computation of the Clusot surface utilizing the information contained in a trained SOM and 2) the automatic detection of clusters in this surface. In the Clusot surface, clusters present in the underlying SOM are indicated by the local maxima of the surface. For SOMs with 2-D topology, the Clusot surface can, therefore, be considered as a convenient visualization technique. Yet, the presented approach is not restricted to a certain type of 2-D SOM topology and it is also applicable for SOMs having an n-dimensional grid topology.     

Visual methods for analyzing time-oriented data

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.     

Network effects as drivers of individual technology adoption: Analyzing adoption and diffusion of mobile communication services

Adoption research has largely ignored the dynamic impact of network effects on technology adoption and diffusion. For example, some technologies become more attractive the more social peers use them as well. But adoption at the same time increases the value for the peers and thereby their adoption decisions as well. Unfortunately, interdependencies like these make adoption and diffusion patterns very complex. Drawing on network effect theory, we develop an adoption and diffusion model that explicitly considers the role of direct and indirect network effects for individual technology adoption, using mobile commerce adoption as application example. By applying a simulation approach we can exemplify and analyze the fundamental adoption dynamics given rise to by network effects. We thereby propose a way of how to incorporate network effects into adoption research and disclose the role of the technology diffusion lifecycle for individual adoption.     

The impact of process standardization on business process outsourcing success

What is the impact of business process standardization on business process outsourcing (BPO) success? This paper argues that there is a direct impact of process standardization on BPO success, due to production cost economies, and also an indirect effect via improved contractual and relational governance resulting from better monitoring opportunities and facilitated communication and coordination. This threefold impact of standardization on BPO success is empirically confirmed using data from 335 BPO ventures in 215 German banks.     

Human eye-head co-ordination in natural exploration

During natural behavior humans continuously adjust their gaze by moving head and eyes, yielding rich dynamics of the retinal input. Sensory coding models, however, typically assume visual input as smooth or a sequence of static images interleaved by volitional gaze shifts. Are these assumptions valid during free exploration behavior in natural environments? We used an innovative technique to simultaneously record gaze and head movements in humans, who freely explored various environments (forest, train station, apartment). Most movements occur along the cardinal axes, and the predominance of vertical or horizontal movements depends on the environment. Eye and head movements co-occur more frequently than their individual statistics predicts under an independence assumption. The majority of co-occurring movements point in opposite directions, consistent with a gaze-stabilizing role of eye movements. Nevertheless, a substantial fraction of eye movements point in the same direction as co-occurring head movements. Even under the very most conservative assumptions, saccadic eye movements alone cannot account for these synergistic movements. Hence nonsaccadic eye movements that interact synergistically with head movements to adjust gaze cannot be neglected in natural visual input. Natural retinal input is continuously dynamic, and cannot be faithfully modeled as a mere sequence of static frames with interleaved large saccades.     

Evaluation of a low-cost 3D sound system for immersive virtual reality training systems

Since head mounted displays (HMD), datagloves, tracking systems, and powerful computer graphics resources are nowadays in an affordable price range, the usage of PC-based "virtual training systems" becomes very attractive. However, due to the limited field of view of HMD devices, additional modalities have to be provided to benefit from 3D environments. A 3D sound simulation can improve the capabilities of VR systems dramatically. Unfortunately, realistic 3D sound simulations are expensive and demand a tremendous amount of computational power to calculate reverberation, occlusion, and obstruction effects. To use 3D sound in a PC-based training system as a way to direct and guide trainees to observe specific events in 3D space, a cheaper alternative has to be provided, so that a broader range of applications can take advantage of this modality. To address this issue, we focus in this paper on the evaluation of a low-cost 3D sound simulation that is capable of providing traceable 3D sound events. We describe our experimental system setup using conventional stereo headsets in combination with a tracked HMD device and present our results with regard to precision, speed, and used signal types for localizing simulated sound events in a virtual training environment     

Distributed Symbolic Bounded Property Checking

In this paper we describe an algorithm for distributed, BDD-based bounded property checking and its implementation in the verification tool SymC. The distributed algorithm verifies larger models and returns results faster than the sequential version.The core algorithm distributes partitions of the state set to computation nodes after reaching a threshold size. The nodes proceed with image computation on the nodes asynchronously. The main scalability problem of this scheme is the overlap of state set partitions. We present static and dynamic overlap reduction techniques.