When stress becomes shared: exploring the emergence of team stress

Cognition, Technology & Work - Team stress is an emergent cognition in which members jointly appraise their current task situations. The sharedness of stress appraisals has been elaborately...     

When noise becomes voice: designing interactive technology for crowd experiences through imitation and invention

In this paper, we present crowd experience as a novel concept when designing interactive technology for spectator crowds in public settings. Technology-mediated experiences in groups have already been given serious attention in the field of interaction design. However, crowd experiences are distinctive because of the spontaneous, uninhibited behavior exhibited. In crowds, extreme sociality and the experience of performing identity in public emerge spontaneously. By bridging crowd theory and pragmatics of experience, we establish an understanding of crowd experience as a distinct sociality within interaction design that unfolds through imitation and invention. We deploy that understanding in an exploration of spectator experiences at three football matches in which an experimental prototype, BannerBattle, was deployed. BannerBattle is an interactive banner on which spectators can grab space in competition with their rivals. The more noise and movement they make, the more screen real estate they gain. BannerBattle therefore enabled us to explore the emergence of imitative and at times inventive behavior in enriched crowd experience, by augmenting and supporting spectator performance in this way. We discuss the value of a conceptual understanding of crowd experience for technology as an unexplored potential for designing new interactive technology at spectator venues.     

Catuity becomes all American

How an Australian company that built its business on smart card ticketing has turned itself into a US company with a string of blue chip partners in the loyalty business     

Toward a paradigm for distributed intelligence

This paper describes a proposed model for a distributed intelligence system. The system is based on a communicating network of nodes where each node is a concurrent blackboard system. A preliminary concurrent blackboard system, which carries out a Penrose tiling of a planar area is discussed. This work leads to the general model—an interconnected network of such systems. The shell for such a system has been developed in the declarative, parallel programming language STRAND. This shell is described and analyzed in some detail, specifically as it functions at a node in the system. Two applications of the proposed approach are discussed.     

The origin of a binary-search paradigm

In a binary-search algorithm for the computation of a numerical function, the interval in which the desired output is sought is divided in half at each iteration. The paper considers how such algorithms might be derived from their specifications by an automatic system for program synthesis. The derivation of the binary-search concept has been found to be surprisingly straight-forward. The programs obtained, though reasonably simple and efficient, are quite different from those that would have been constructed by informal means.     

Google becomes an entertainment company

The Internet has created new competitors for Apple that provide a study in contrasts. The biggest of the new entrants is Google. Until recently, Google pursued a business strategy markedly different from Apple's, which developed a DRM infrastructure focused on the iPod and its iTunes software to grow its business as a content distributor. Google has relied on delivering user-targeted paid advertising to personal computers in exchange for free content delivered by lightweight Web clients that interface its powerful search engines via its massive server farms. Advertising accounted for most of its $1.5 billion profit in 2005. However, in its quest for new revenue and growth, it's becoming apparent that Google's strategy is morphing rapidly to adapt to changing market conditions.     

Short-term emotion assessment in a recall paradigm

The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on short-time periods. Three specific areas of the valence–arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states. An acquisition protocol based on the recall of past emotional life episodes has been designed to acquire data from both peripheral and EEG signals. Pattern classification is used to distinguish between the three areas of the valence–arousal space. The performance of several classifiers has been evaluated on 10 participants and different feature sets: peripheral features, EEG time–frequency features, EEG pairwise mutual information (MI) features. Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEGs to assess valence and arousal in emotion recall conditions. The obtained accuracy for the three emotional classes is 63% using EEG time–frequency features, which is better than the results obtained from previous studies using EEG and similar classes. Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70%. Furthermore, the rejection of non-confident samples finally led to a classification accuracy of 80% for the three classes.     

IPTV service based on a content-zapping paradigm

Internet protocol television (IPTV) generically designates a real-time distribution service for multimedia contents over an IP network, such as the Internet. There are many advantages of IPTV deployment over current digital or air broadcast TV signals: integration, the use of the switched internet protocol (IP), the possibility to build home networks that can be used to share multimedia contents over different devices, the easy implementation of video on demand services and the usage of better compression and encryption standards. In order to implement this kind of service, it is extremely useful to have a system that can efficiently classify multimedia contents and users and distribute them in a customized way. This paper proposes a novel IPTV service for the distribution of personalized multimedia contents over IP networks based on the concept of content-zapping, in contrast to traditional channel-zapping: each client system receives a multimedia streaming that is automatically composed by the system based on the user preferences and the user will only interact with the system by requesting a content change or marking a content as favorite. The paper will describe the general functionality of the service and will present the detailed architecture of the IPTV server, the key component of the service infrastructure. The server must maintain a list of media contents residing in other systems and must keep a dynamic classification of the multimedia contents that are stored in its database. This classification is built and gradually refined based on the interactions between clients and multimedia contents. Special attention is given in the paper to the classification model, describing the general ideas that are used to automatically suggest multimedia contents to a specific user (that is characterized by his complete profile). A specific content may be suggested to the user based on the knowledge of the user profile and/or based on specific and dynamic information, such as the user position, the local temperature, date and time. The availability of this information obviously depends on the specific user device that is being used. The proposed system allows any client device to connect, allowing a high level of interoperability. It is also possible to use all the device capabilities and sensors, like Global Positioning System (GPS), accelerometers, light sensors, noise sensors, etc., thus creating a context environment that helps classify each user profile. These context-awareness mechanisms applied to mobile devices with wireless network (802.11b/g/n, WiMAX, GSM, UMTS, etc.) capabilities allow a better user experience and more accurate multimedia suggestions, due to the deep knowledge about the user device, network and environment. The system also allows the users to suggest contents to other users in the same “group of friends”. Several performance tests were already conducted and the results obtained show that the proposed system is very stable and fast, even for high increases on the number of users.     

Convergence analysis of a domain decomposition paradigm

We describe a domain decomposition algorithm for use in several variants of the parallel adaptive meshing paradigm of Bank and Holst. This algorithm has low communication, makes extensive use of existing sequential solvers, and exploits in several important ways data generated as part of the adaptive meshing paradigm. We show that for an idealized version of the algorithm, the rate of convergence is independent of both the global problem size N and the number of subdomains p used in the domain decomposition partition. Numerical examples illustrate the effectiveness of the procedure.