To facilitate user-centered software engineering, developers need an easy to grasp understanding of the user. The use of personas helps to keep specific user needs in mind during the design process. Technology acceptance is of particular interest for the design of innovative applications previously unknown to potential users. Therefore, our research focuses on defining a typology of relevant user characteristics with respect to technology acceptance and transferring those findings to the description of personas. The presented work focuses on the statistical relationship between technology acceptance and personality. We apply sub-group discovery as a statistical tool. Based on the statistically derived subgroups and patterns we define the mentioned personas to help developers to understand different forms of technology acceptance. By integrating the specifically defined personas into existing methods in the field of software engineering the feasibility of the presented approach is demonstrated.

While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks.

"Collective intelligence denotes a phenomenon according to which the purposeful interaction between individuals creates intelligent solutions and behaviors that might not have come to existence without this concerted effort of a community. The members of such communities form a social network, typically over the Internet. They are engaged with each other over a sustained period of time to develop an area of innovation through collaboration and exchange of ideas, experiences and information. Leading-edge information and communication technologies (ICT) offer ample opportunities for enabling collective intelligence. COLLIN aims to become the flagship conference in the areas collective intelligence and ICT-enabled social networking, which is attracting more and more researchers and practitioners from both academia and industry"--Preface.

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information.

This publication explores the degree of accuracy to which the propagation delay of WLAN packets can be measured using today’s commercial, inexpensive equipment. The aim is to determine the distance between two wireless nodes for location sensing applications. We conducted experiments in which we measured the time difference between sending a data packet and receiving the corresponding immediate acknowledgement. We found the propagation delays correlate closely with distance, having only a measurement error of a few meters. Furthermore, they are more precise than received signal strength indications. To overcome the low time resolution of the given hardware timers, various statistical methods are applied, developed and analyzed. For example, we take advantage of drifting clocks to determine propagation delays that are forty times smaller than the clocks’ quantization resolution. Our approach also determines the frequency offset between remote and local crystal clocks. ER -