The latest smartphones with GPS, electronic compasses, directional audio, touch screens, and so forth, hold a potential for location-based services that are easier to use and that let users focus on their activities and the environment around them. Rather than interpreting maps, users can search for information by pointing in a direction and database queries can be created from GPS location and compass data. Users can also get guidance to locations through point and sweep gestures, spatial sound, and simple graphics. This paper describes two studies testing two applications with multimodal user interfaces for navigation and information retrieval. The applications allow users to search for information and get navigation support using combinations of point and sweep gestures, nonspeech audio, graphics, and text. Tests show that users appreciated both applications for their ease of use and for allowing users to interact directly with the surrounding environment.

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In software development there is always the need for faster development process. One way to speed up the process is to reuse existing components, in which loose coupling of the components is an important prerequisite. Publish/subscribe is one popular way to provide loose coupling, and there exists many middleware solutions for it. However, most such solutions are aimed for large inter-device networks, and are poorly suited for resource-constrained low-level contexts. As our solution we present the design of SERF, a novel lightweight software framework implementing pub/sub event messaging and peerto-peer architectures. We also describe the experiences and lessons learned that led to the development of the framework. Upon analysis we find out that SERF offers the advantages of loosely coupled modules and pub/sub, but also point out that the framework is less suitable outside its framed problem area.

Radii-based niching evolutionary algorithms are criticized for the difficulty of the proper choice of the radius parameter. Detect-multimodal method enables the identification of niches without an explicit user-defined radius parameter. Although robust, the detect-multimodal method based algorithms are computationally expensive. We propose a novel algorithm called Attraction Basin Estimating Genetic Algorithm (ABE), which estimates the radius parameter based on the detect-multimodal method and uses the estimated radius to identify niches. Our experiments demonstrate that ABE has a similar ability to solve the multimodal optimization problem as Topological Species Conservation algorithm which is based on the detect-multimodal method, but much more efficiently.

Multimodal optimization aims to discover all or most optima as opposed to only the best optimum. Evolutionary Algorithms provide a natural advantage in this field, because they are population based. However, Standard Evolutionary Algorithms tend to converge only to a single optimum. The radiusbased niching evolutionary algorithms aim to solve this problem. However, they are criticized for the difficulty of the proper choice of the radius parameter. Detect-multimodal method does not necessitate using the radius parameter. It separates niches by detecting if two solutions are in same optimum. Although robust, the current detect-multimodal based niching method are computationally expensive. Inspired by the idea of combining radius-based niching method and detect-multimodal based niching method, we propose the Attraction Basin Estimating Genetic Algorithm (ABE) in this paper. It estimates the radius which is called attraction basin in this paper using detect-multimodal method, and use the estimated radius to separate species in the same way as radius-based method. We compare the proposed method with a detect-multimodal based method: Topological Species Conservation Algorithm. The experiments demonstrate that ABE has the similar ability to solve multimodal optimization problems as Topological Species Conservation, but significantly more efficiently.