A model of bystanders' effect on volunteering (in case of an observed crime or critical situation) is extended to incorporate the possibility of a sense of guilt (after nonintervention). Based on sound theoretical and experimental foundations, an existing model of the spread of unpopular norms is used to allow dispersion of an unpopular norm (a mild crime) so that a population of agents may follow or accept it. The question asked is why a society (as a whole) is not able to overturn an unpopular norm through interventions. An agent-based model is proposed which captures all necessary ingredients to explore this question. Several what-if questions are asked by varying simulation parameters. The model and simulation reveal that a sense of guilt of bystanders improves the volunteering tendency. The simulation results also provide pieces of evidence of clear differentiation between the individual and societal perception of a crime. Through the simulation results, we were able to conclude that guilt, not only, improves the volunteering tendency, but also, very clearly differentiates between the individual and societal perception of an unpopular norm. Overall, it was learned that people intervene only when they are able to overcome the inhibitions of the crowd. However, even interventions do not guarantee to overturn an unpopular norm. In fact, irrespective of the state of interventions, there is no neutralization without guilt.

Development of crowd evacuation systems is a challenge due to involvement of complex interrelated aspects, diversity of involved individuals and/or environment, and lack of direct evidence. Evacuation modeling and simulation is used to analyze various possible outcomes as different scenarios unfold, typically when the complexity of scenario is high. However, incorporation of different aspect categories in a unified modeling space is a challenge. In this paper, we addressed this challenge by combining individual, social and technological models of people during evacuation, while pivoting all these aspects on a common agent-based modeling framework and a grid-based hypothetical environment. By simulating these models, an insight into the effectiveness of several interesting evacuation scenarios is provided. Based on the simulation results, a couple of useful recommendations are also given. The most important recommendation is not to use potential field indicating the exits dynamics as an exit strategy particularly for a spatial complexity environment.

Due to the large cognitive strain put upon surgeons' attention during laparoscopic cholecystectomies, automated interface systems that provide meaningful information and feedback without incurring increased cognitive load are necessary. This work outlines (i) a method based on visual data analysis for estimating a surgeon's visual distraction from the surgical monitor and (ii) an approach for using this information to provide unobtrusive reminders to (novice) surgeons during critical sections of the surgical workflow. A large corpus of sensor data was collected during 38 real-world laparoscopic cholecystectomies, and the algorithms were trained and optimized using this data. The algorithm for detecting visual distraction achieved a recognition rate of 93.8%, and a temporal attention-sensitive algorithm for triggering feedback based on thresholds was successfully devised. Further insights into patterns of attention/distraction of surgeons could be derived by means of qualitative analysis.

Laparoscopic surgery has revolutionised state of the art in surgical health care. However, its complexity puts a significant burden on the surgeon's cognitive resources resulting in major biliary injuries. With the increasing number of laparoscopic surgeries, it is crucial to identify surgeons' cognitive loads (CL) and levels of focus in real time to give them unobtrusive feedback when detecting the suboptimal level of attention. Assuming that the experts appear to be more focused on attention, we investigate how the skill level of surgeons during live surgery is reflected through eye metrics. Forty-two laparoscopic surgeries have been conducted with four surgeons who have different expertise levels. Concerning eye metrics, we have used six metrics which belong to fixation and pupillary based metrics. With the use of mean, standard deviation and ANOVA test we have proven three reliable metrics which we can use to differentiate the skill level during live surgeries. In future studies, these three metrics will be used to classify the surgeons' cognitive load and level of focus during the live surgery using machine learning techniques.

The eyes are an interesting modality for pervasive interactions, though their applicability for mobile scenarios is restricted by several issues so far. In this paper, we propose the idea of contour-guided gaze gestures, which overcome former constraints, like the need for calibration, by relying on unnatural and relative eye movements, as users trace the contours of objects in order to trigger an interaction. The interaction concept and the system design are described, along with two user studies, that demonstrate the method's applicability. It is shown that users were able to trace object contours to trigger actions from various positions on multiple different objects. It is further determined, that the proposed method is an easy to learn, hands-free interaction technique, that is robust against false positive activations. Results highlight low demand values and show that the method holds potential for further exploration, but also reveal areas for refinement.

This paper presents an agent-based model of population desegregation and provides a thorough analysis of the social behavior leading to it, namely, the contact hypothesis. Based on the parameters of frequency and intensity of influence of group leaders on the population, the proposed model is constituted by two layers: 1) a physical layer of the population that is influenced by and 2) a virtual layer of group leaders. The model of negotiation and survival of group leaders are governed by the nature-inspired evolutionary process of queen ants, also known as Foundress Dilemma. The motivation of using a virtual grouping concept (instead of taking a subset of population as the group leaders) is to stay focused on finding the conditions leading individuals in a society tolerating a significantly diversified (desegregated) neighborhood, rather than, indulging into complex details, which would be more relevant to studies targeting the evolution of societal group and leaders. A geographic information system-driven simulation is performed, which reveals that: 1) desegregation is directly proportional to the frequency of group leaders' contact with the population and 2) mostly, it remains ineffective with an increase in the intensity of group leaders' contact with the population. The mechanism of group selection (the conflict resolution model resolving the Foundress Dilemma) reveals an exciting result concerning negative influence of cooperative group leaders. Most of the time, desegregation decreases with increase in cooperative leaders (the leaders enforcing desegregation) when compared with fierce leaders (the leaders enforcing segregation).

In addition to seamless connectedness and smartness, the objects in Internet of Things (IoT) are expected to have social capabilities. In literature, these objects are named as "social objects". In this paper, an intuitive paradigm of social interactions between these objects is argued and model. The purpose of the model is to analyze the impact of social-like capabilities on the interaction pattern of social objects in a peer-to-peer resource sharing scenario. Adopting an agent-based modeling method and simulation, a comparative study of competitive vs. cooperative behavior of peers is demonstrated. The simulation results reveal that the social capabilities of peers imparts a significant increase in the quality of interactions between social objects. Typically, the nature of underlying structure of network connectivity has a profound impact. The small-world network is not only closer to the social paradigm that surround us, but also, competes with an unbounded network; such as a mesh network.

Driven by technological advancements, gesture interfaces have recently found their way into vehicular prototypes of various kind. Unfortunately, their application is less than perfect and detailed information about preferred gesture execution regions, spatial extent, and time behavior are not available yet. Providing car (interior) manufacturer with gesture characteristics would allow them to design future in-vehicle concepts in a way to not interfere with gestural interaction. To tackle the problem, this research aims as preliminary work for a later standardization of the diverse properties of gestures and gesture classes similarly to what is already standardized in norms such as ISO 3958/4040 for placement and reachability of traditional controls and indicators. We have set up a real driving experiment recording trajectories and time behavior of gestures related to car and media control tasks. Data evaluation reveals that most of the subjects perform gestures in the same region (bounded by a "triangle" steering wheel, rear mirror, and gearshift) and with similar spatial extent (on average below 2 sec.). The generated density plots can be further used for an initial discussion about gesture execution in the passenger compartment. The final aim is to propose a new standard on permitted gesture properties (time, space) in the car.

Public display technology (like digital signage) and private one (like smartphones) has advanced significantly in capacity, capability and spread over the past years. For the provision of information services in display technology rich settings we draw the analogy to biological ecosystems as a community of living organisms together with nonliving components of the environment, linked together through information flows, and by that acting as a global system. We identify individuals in the information society with their situational information demands and their implicit and explicit interaction styles as the living, and the plethora of display devices as the non-living part of an information ecosystem. A local interactions oriented service architecture is developed, the SAPERE pervasive service substrate. A situation aware social feedback mechanism is proposed and implemented in the SAPERE architecture, demonstrating how incentivizing local interactions can stand as a principle to attain desirable global information balance.

Experienced poker players have the ability to suppress and hide emotions and reactions to avoid providing information about the quality of the dealt private cards and the own probability of winning to the adversaries. Besides unswayable luck and bravery, bluffing is the only skill that could massively improve the own chance of winning. This paper investigates whether a subliminal reaction in terms of changing facial surface skin temperature can be linked to the quality of the dealt private cards (i.e., the probability of winning the actual hand). Therefore, a dataset containing thermal imaging has been recorded during a No Limit Texas Hold'Em Poker tournament-session with six players in total and two players being observed with a high-resolution thermal imaging camera and manual provision of their dealt private cards as ground-truth. Preliminary results show that the facial skin temperature varies massively (±1.2°C), which constitutes the research hypothesis that a significant change in the surface face skin temperature can be linked to the quality of the dealt cards in terms of winning chance for an actually played hand.

Recent advances in wireless/mobile communication and body worn sensors, together with ambient intelligence and seamless integrated pervasive technology have paved the way for applications operating based on social signals, i.e., sensing and processing of group behavior, interpersonal relationships, or emotions. Thinking in large, it should be apparent that modeling social systems allowing to study crowd behavior emerging from individual entities' (agents') condition and/or characteristics is, in fact, a challenging task. To address the heterogeneity, analytical agent-based models (ABMs) are gaining popularity due to its capability of directly representing individual entities and their interactions, unfortunately, ABMs (in which each agent has unique behavior) are not very well suited for large populations, expressed by exponentially rising simulation time. To solve this problem, the questions (i) how does the parallel execution of such models scale with capabilities of both the machine (number of cores, cluster size, etc.) and agents (behavioral adaptation function, interaction extent, etc.) and (ii) what is, in comparison, the performance coefficient applying the approach of model execution on graphical processors (GPUs) with its different pipelining architecture, need answers. To this end, we have performed simulation runs with parameter variation on a real parallel and distributed hardware platform using Cilk as well as on a GPU employing OpenCL. Simulation efficiency for two realistic models with varying complexity on a scale of 107 agents has shown the usefulness of both approaches.

In the last decade the shift of information and communication systems from a purely system level to the social level is already observable. However, the integration of social / cognitive aspects into so-called social computing is not an easy task due to conceptual differences in domains. A Socio-Technical System (STS) is a recent term intending to differentiate between a social system mediated by natural sciences or information technology. Even if the mediation of social / cognitive aspects is "theoretically" governed by technology, the gap between "socio" and "technical" is historical and huge. Furthermore the fact that with every passing year, the technical systems become more intelligent with respect to interaction with people and their pervasiveness, a special attention should be given to modelling both social and technical components and interaction between them. For example while modelling (and simulating) an emergency situation from a public facility, the possible availability of technology at the environment (e.g. situation-aware exit signs, interactive displays, etc.) and personal (e.g. cell phones, specialized wearables etc.) level, along with its social / cognitive influence must not be overruled. To address this challenge, we have integrated cognitive decision making model abstracted from psychological, neurological and social theories of human behaviour during evacuation situations into CA based simulation. Keeping focus on a scenario in which a small population of agents is technologically assisted, some of the most interesting finding are: (i) the inclusion of a representative and authentic social behaviour model into modelling a socio-technical system essentially produces fundamental differences in methodologies, (ii) the technologically assisted agents emerge as leaders during evacuation changing the intentions of many agents within their influence (iii) even a small population of such leaders in sufficiently large population is enough to guarantee a remarkable difference, particularly improving usage of possibly under-utilized exits.

This paper presents a novel approach towards identity management strategies for application in large scale interactive systems while separating pure detection from the identity management processes. Detection is achieved by employing a scalable, modal, network-based, real-time multi-camera tracking system in which numerous cameras are used to cover large areas. Objects are detected by employing blob detection and image pre-processing algorithms. The proposed identity handling algorithm covers standard tracking as well as solving Split, Merge, and Handover problems between adjacent cameras. General ID handling strategies are introduced which allow reliable tracking and simple access to high-level movement data for later movement analysis. Results in terms of accuracy and technical feasibility are given, gathered from a pilot installation at a public event.

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices that just happen to be available instead of pre-defining them at the design time of the system in order to achieve a recognition goal at runtime. Whenever a user and/or application states a recognition goal at runtime to the system, the available sensing devices configure an ensemble of the best available set of sensors for the specified recognition goal. This paper presents an approach to show how machine learning technologies (classification, fusion and anomaly detection) are integrated in a prototypical opportunistic activity and context recognition system (referred to as the OPPORTUNITY Framework). We define a metric that quantifies the ensemble's capabilities according to a recognition goal and evaluate the approach with respect to the requirements of an opportunistic system (e.g. to compute an ensemble's configuration and reconfiguration at runtime).

A general reduction of carbon dioxide emissions is an important topic currently discussed by both society and government -- lower allowed emission values would strongly affect automotive manufacturers as road transport produces, for example, about one fifth of the CO2 emissions in the European Union. But that's not all, also the individual driver could be affected from regulatory mechanisms as it is feasible, not least due to the broad availability of wireless and information technology in cars, to demand the assembly of a "personal carbon dioxide profile" including all the emissions accumulating from operating vehicles, traveling by plane, and even from using public transport, and in succession to charge a person based on its effective CO2 consumption. One problem arising in this field is that an individual usually is not aware about his/her CO2 consumption, neither about which means of transportation produces what amount of carbon dioxide (what is the personal fraction of CO2 for a large plane with 250 passengers traveling 5, 000km?), nor how exactly to drive a vehicle economic or wasting with respect to CO2 emission or what the difference between these two extreme values (in terms of cost) is. To counteract the issue of driving economy, which is the only the driver can directly control, we propose a inattentive operating vibro-tactile notification system integrated into the car (safety belt or seating), helping the driver in his/her (i) subjective CO2 valuation and (ii) reduction of CO2 emissions while driving.

A considerable increase in road traffic has provoked a total change in the operating paradigms of vehicles, shifting vehicle handling from “just steering” towards a complex adaptation task. With the emergence of wireless communication technology, vehicle operation can now incorporate for the first time ever beside the local driver-vehicle interaction also more significant information obtained from cars in the surrounding. With this foundation it would be possible to build collectively operating driver assistance systems, negotiating the interests of all road participants in a certain area with the final goal to improve global parameters such as road throughput or traffic fluidity, also having an effect on the individual car (driver), e. g. increased travel speed, less congestions, or a reduced level of cognitive load. The question addressed with this paper is whether or not the vehicle speed can be sustained while merging onto a motorway, leading to a more harmonious integration of the merging cars into the flowing traffic on the main road. To achieve this we propose the application of ambient intelligence (AmI) technology operating on the collective behavior of all cars in the periphery of the entrance ramp. To prove our hypothesis we applied the AmI technology to a data driven, true to scale simulation model of the Madrid motorway M30, one of the most busiest roads in Spain. The comparison of simulation runs with high volume of traffic showed that technology assistance could help to increase road throughput and minimize the variance of traffic flow, but on the other side demands solutions for one of the bigger problems of data driven simulation – missing or noisy data compromising simulation results.

In order to develop complexity science based modeling, prediction and simulation methods for large scale socio-technical systems in an Ambient Intelligence (AmI) based smart environment, we propose a scenario based modeling approach. With a case study on AmI technology to support the evacuation from emergency scenarios, i.e. the Life Belt, a wearable computing systems for vibro-tactile directional guidance, we introduce the concept of model scaling from a micro to a macro level. Aligned with the scenario, we present how crowd simulation strategies encoded into a small scale simulation setup can be extended to a mixed-level simulation based on combining model aspects also coming from the large scale model. The experimental results of a real evacuation trail at a local railway station are incorporated to compare the evacuation efficiency for three strategies: (i) Potential Map, (ii) Evacuees familiarity of the exits and (iii) Exits usage optimization. A comparison with the earlier results from small scale simulation suggest that a real large scale simulation results may not be similar to that of small scale simulation due to dynamics of crowd built up and complexity of building structure.

Trends in miniaturizing of technology (modern microelectronics, system-on-chip technologies, ultra compact memory systems, Micro-mechanics, Nano-devices) and exponential growth and globalization of communication infrastructures have opened an unexpectedly wide terrain for ICT research in general, and Pervasive Computing in particular.

The today mainly incorporated sensory modalities vision and hearing are most often highly charged due to an increasing number, as well as a rising complexity of diversified assistance systems in vehicles. These systems, originally developed for "supporting" the driver in its tasks, increasingly directs to operation errors caused by cognitive overload. The sense of touch - as additional interaction channel - should have the ability to release cognitive load from visual and auditory channels by adapting in a natural, intuitive and non-distracting manner. A drawback of haptics is that feedback is reliant to both driver and environmental conditions. For instance, different sizes and weights of drivers' result in a changed haptic perceptivity, and vibro-tactile feedback potentially is interpreted erroneous due to personal sitting attitudes. A vibro-tactile seat, endowed with a set of unobtrusive embedded sensors and actuators, is proposed to counter action these issues. The input channel is used for determining the occupied seating surface and, in addition, is also responsible for observing disruptive vibrations, originating in the environment. These information is employed in order to reconfigure vibro-tactile output dynamically with the aim to guarantee consistent haptic perception for any person.

Driving a vehicle is a task affected by an increasing number and a rising complexity of Driver Assistance Systems (DAS) resulting in a raised cognitive load of the driver, and in consequence to the distraction from the main activity of driving. A number of potential solutions have been proposed so far, however, although these techniques broaden the perception horizon (e. g. the introduction of the sense of touch as additional information modality or the utilization of multimodal instead of unimodal interfaces), they demand the attention of the driver too. In order to cope with the issues of workload and/or distraction, it would be essential to find a non-distracting and noninvasive solution for the emergence of information.In this work we have investigated the application of heart rate variability (HRV) analysis to electrocardiography (ECG) data for identifying driving situations of possible threat by monitoring and recording the autonomic arousal states of the driver. For verification we have collected ECG and global positioning system (GPS) data in more than 20 test journeys on two regularly driven routes during a period of two weeks.The first results have shown that an indicated difference of the arousal state of the driver for a dedicated point on a route, compared to its usual state, can be interpreted as a warning sign and used to notify the driver about this, perhaps safety critical, change. To provide evidence for this hypothesis it would be essential in the next step to conduct a large number of journeys on different times of the day, using different drivers and various roadways.

To support the evacuation process of crowds from emergency situations, we have developed a wearable device, LifeBelt, for vibro tactile guidance of individuals in panic towards exits. Since visual and auditory perception is overwhelmed in situations of panic, exit sign and loudspeaker based evacuation often appears ineffective. LifeBelt as a coordinated navigation device builds on a subtle directional vibration stimulation to navigate and guide individuals to escape. This paper compares the effectiveness of a LifeBelt supported vs. non-supported evacuation from spaces with multiple exits. Based on an extended Moore's model of neighborhood for next step behavior, and a predicted shortest time to escape metric, we investigate on the efficiency effect of (i) individuals following the nearest exit (without LifeBelt), and (ii) individuals adhering LifeBelt recommendations towards the earliest escape exit. Large scale simulations show that even in situations of growing panic, LifeBelt improves evacuation efficiency by more than 34,5%.

With the emergence of pervasive computing technologies into vehicles, driving has moved from an active task of steering towards an interaction or adaptation task with respect to the driver-vehicle feedback loop. Up to now vehicular interfaces have mostly been evaluated from a single-driver single-car viewpoint, however, driving is a more complex task involving beside the local interaction - the interrelationship between all the cars in a certain community of interest. The question investigated in this research work is how a vehicle's local parameters in a bulk of cars (e. g. vehicle speed, braking parameters) affect the global behavior of this system (traffic congestion, driving speed variation, throughput). To explore this, two traffic models have been developed and simulated using the NetLogo simulation environment. Simulation results have shown that the intercar distance has a direct impact on both the throughput and the mean trip time. The proactive driving approach using vibro-tactile driver notification followed in the second, advanced model achieved much better results regarding these parameters compared to the simple manual-driven case. Finally, the outcomes legitimate the implementation of a prototype, and the installation of such a technology into a large number of cars in order to provide evidence for the improved traffic flow and decreased probability of traffic accidents in real driving scenarios.

To individuals evacuating, many multi-exit environments do not allow visibility of all the exits due to line-of-sight constraint. In addition, the environment can be dark or smoky, not allowing visibility to even a single exit. In such a situation, given that each individual in the crowd is accompanied with a helping device globally connected with a central server, a 'directional guidance' towards an optimal exit is a real possibility. In this context, the 'occupant density' around exits (within a static `exit area') has been used in conjunction with the corresponding distances to devise a probabilistic strategy for optimal exit suggestion. In this paper, we related the exit area with the level of visibility of the environment (the more the visibility, the more the exit area and vice versa). In this way, a more realistic human-behavioral model is implemented in which an individual viewing (seeing) an exit would always direct towards that exit, irrespective of the directional guidance provided. When an individual is not at any of the exit areas (not viewing even a single exit), a directional guidance is provided assuming that the individual is adhering to it. Additionally we used the measure of 'effective throughput' instead of occupant density, in conjunction with the corresponding distances. Through simulation results, we found a marked improvement in the evacuation time, when effective throughput was modeled instead of occupant density.

To navigate in unfamiliar places is, for obvious reasons, particularly difficult for blind people. In this research we used vibro-tactile guidance cues with the goal of allowing users to reach their destination with the most efficiency. Our hypothesis was that adding distance information should impr

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project's objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

Embedded systems technologies and advances in micro electronics have accelerated the evolution of driver assistance systems towards more driving safety, comfort, entertainment and wayfinding. With the technological and quality progress, however, also goes the growing of interaction complexity, information overload, and intricate interface designs. Assessing interaction designs for in-car assistance services is an emerging and vibrant field of research. To avoid situations of possibly fatal danger when assessing driver assistance services in real driving situations, we propose trace-driven simulation to steer the experiments with users in a automotive driving simulator. Based on our own developments of driver assistance systems involving the sense of touch, i.e. exploiting haptics as a communication channel in vehicle-to-driver interactions, we demonstrate how pre-recorded traces of driving situations can control user studies. Our experiments show, that simulated driving journeys is a viable alternative to the more hazardous "on-the-road" user studies. With respect to haptics as an additional channel of communication we find that vibro-tactile stimuli are a promising means to raise driver attention when the visual and auditive channels fail due to overload.

According to statistics and future prospects in the next few years world-wide energy consumption will increase significantly. Therefore not only more energy efficient technologies but also more extensive energy saving concepts have to be realized. We have developed an rdquoimplicit interactionrdquo based power saving concept, which automatically schedules and controls energy consumers depending on the recognized activities of users. Moreover, dynamically schedulable power consumption loads are shifted in time, so as to make use of the cheapest energy prices without compromising user comfort. In order to calculate the potential savings of this concept we have implemented a simulation framework executing energy consumption models on a city scale, which allows for more complex scenarios than being restricted to some devices or buildings only. Both the framework architecture as well as large scale energy consumption simulations are presented. Simulation experiments give strong evidence for our implicit energy management concept as a promising source of energy savings.

Built with networked embedded systems technology, everyday objects like appliances, tools and environnments -- in general: "Digital Artefacts" -- become increasingly interconnected, diverse and heterogeneous, raising the challenge of an operative, and semantically meaningful interplay among each other. One approach to address this challenge is to design and implement systems able to "manage" and to "organize" themselves. Self-management stands for the ability of single Digital Artefact to describe itself, to select and use adequate sensors to capture information, and to describe its context. Self-organizing stands for the ability of a group of possibly heterogeneous digital artefacts to establish a spontaneous network based on interest, purpose or goal, and to negotiating and fulfilling a group goal. Thus, while self-management relates to individual artefacts, and concerns adaptation to changing individual goals and conditions at runtime, self-organization relates to whole ensembles, and concerns adaptation in order to meet group goals.

Head-worn displays are becoming a viable visual display option for mobile augmented reality. In this paper, we highlight the challenges and some recent progress towards compact and lightweight head-worn displays tending towards the eyeglass form factor. Single and dual-element eyeglass display designs that leverage the advances in free-form optics fabrication technology will be presented. There are three new contributions in this paper: first, we present a single-element eyeglass display design and the fabricated mirror prototype; second, we present our second generation dual-element eyeglass display design with the revised opto-mechanical packaging; third, we present our initial studies on the field of view limit with an 8 mm pupil for the dual-element design.

Built with networked embedded systems technology, everyday objects like appliances, tools and environnments -- in general: "Digital Artefacts" - become increasingly interconnected, diverse and heterogeneous, raising the challenge of an operative, and semantically meaningful interplay among each other. One approach to address this challenge is to design and implement systems able to "manage" and to "organize" themselves. Self-management stands for the ability of single Digital Artefact to describe itself, to select and use adequate sensors to capture information, and to describe its context. Self-organizing stands for the ability of a group of possibly heterogeneous digital artefacts to establish a spontaneous network based on interest, purpose or goal, and to negotiating and fulfilling a group goal. Thus, while self-management relates to individual artefacts, and concerns adaptation to changing individual goals and conditions at runtime, self-organization relates to whole ensembles, and concerns adaptation in order to meet group goals.

The presentation addresse the issue of spatially constrained spontaneous interaction among digital artefacts. An explicit model for spatial proximity is investigated based on geometric descriptions of "zones of influence" (ZoI) of artefacts, enabling the triggering of their interactions based on real time verification of relations among Zols (intersection, inclusion).

Due to recent technological advances, it has become possible to integrate sensor and actuator technologies as well as wireless communication in everyday objects and environments. These developments open up a huge amount of innovative interaction scenarios, involving new forms of user interfaces. This half day tutorial gives an overview of the emerging field of everywhere interfaces, referring to computing devices that disappear within objects of everyday life and thus enable omnipresent physical interfaces to the digital world, describes the state of the art of sensor and actuator technologies and demonstrates the development of a smart artefact for controlling everyday environments.

Pervasive and ubiquitous computing has developed a vision where the "computer" is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situative services originating in a digital world, which are perceived through the physical world. It is expected that services with explicit user input and output will be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. Applications and services will therefore have to be greatly based on the notions of context and knowledge, and will have to cope with highly dynamic environments and changing resources. "Context" refers to any information describing the situation of an entity, like a person, a thing or a place.This talk will outline challenges posed by the emerging new models of self-managing systems, i.e. systems able to autonomously become aware of their context, to self-describe their situation e.g. for spontaneous interaction, and to adapt in a self-organizing manner based on the available resources and its performance.

"Smart Things" are commonly understood as wireless ad-hoc networked, mobile, autonomous, special purpose computing appliances, usually interacting with their environment implicitly via a variety of sensors on the input side and actuators on the output side. Such smart appliances have started to populate the "real world" with "hidden" or "invisible" services, thus building up an "invisible world" of services associated with real world objects. With the embedding of invisible technology into everyday things, however, also the intuitive perception of "invisible services" disappears. We believe that it has potential advantages to support the perception of smart appliance services via novel interactive visual experiences. For this purpose we have developed and built DigiScope, a see-through based visual real time perception system for "invisible worlds" to support interactive theater experience in mixed reality spaces. A case study is presented that demonstrates the use of DigiScope to percept the "invisible services" of our smart Internet appliance SmartCase. Opposed to previous work on mixed reality based augmentation of reality, the DigiScope approach allows for a multiuser, collaborative real time perceptual experience.

Time warp (TW), although generally accepted as a potentially effective parallel and distributed simulation mechanism for timed Petri nets, can reveal deficiencies in certain model domains. Particularly, the unlimited optimism underlying TW can lead to excessive aggressiveness in memory consumption due to saving state histories, and waste of CPU cycles due to over-optimistically progressing simulations that eventually have to be "rolled back". Furthermore, in TW simulations executing in distributed memory environments, the communication overhead induced by the roll-back mechanism can cause pathological overall simulation performance. In this work, an adaptive optimism control mechanism for TW is developed to overcome these shortcomings. By monitoring and statistically analyzing the arrival processes of synchronization messages, TW simulation progress is probabilistically throttled based on the forecasted time stamp of forthcoming messages. Two classes of arrival process characterizations are studied, reflecting that a natural trade-off exists among the computational and space complexity, and the respective prediction accuracy: While forecasts based on metrics of central tendency are computationally cheap but yield inadequate predictions for correlated arrivals (thus negatively affecting performance), time series based forecast methods give higher prediction accuracy, but at higher computational cost.

Self-managing systems able to dynamically re-configure with respect to time-varying workload mixes and changing system resource availability are of growing importance in heterogeneous multicomputer environments. Advanced performance evaluation techniques are needed to induce and assess the impact of such re-configurations where guaranteeing timeliness of reconfiguration activities is particularly challenging. A whole new class of methods supporting "pro-active" adaptivity based on the predicted system state at the re-configuration instant are needed to alleviate the shortcomings of "re-active" systems which bring reconfigurations in effect after the system state has changed. In this paper we argue for self-contained performance management of distributed applications, i.e. integrated performance tuning with the ability to automatically adapt the application behavior to the performance characteristics of the execution environment. Particularly, we study pro-active performance management for distributed simulation based on the Time Warp protocol executing on a network of workstations (NOWs).