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The BaBar experiment is currently operating near the rate limit of its ability to log event data to disk and tape using the existing hardware and software systems. Consequently we have chosen to design and implement a new system for logging event data. The new system is designed to be scalable, so that the data rate can be increased by adding systems at one of three levels. It also has the property that data can be logged at almost unlimited burst rates without introducing dead time. The key to these features lies in the use of many nodes within the level three trigger system of BaBar. This allows the events to first be logged to local disks within the trigger system, and then later to be merged to any of multiple merge servers in non-real-time.

Computer gaming is a key component of the rapidly growing entertainment industry. Computer games are a multi-billion pound worldwide part of the entertainment industry. However, they are a relatively new type of software product and as such the manner in which they are designed and developed is still evolving. Computer games can be highly complex software systems that involve the expertise of a wide range of professionals ranging from programmers to animators, artists and musicians. When designing computer games, new game concepts can be difficult to communicate. In addition, the creative process could be benefited if game design ideas were capable of being shared widely and discussed. In this paper, we are to some extent shifting the focus from real world situation to what we shall term game world situation. That is, a logically simulated environment is created by defining situations and events based on the hierarchy structure of the situation. Our study defines event type, causality events and event normalization, and aims to develop a logical simulation method. These methods allow us to understand the communication of new game environment design. Specially, the introduction of the definition of events allows us to approach game design in a structural manner rather than by their classification. The proposed method was implemented in the context of changing among natural phenomena in a game environment.

The event based paradigm has gathered momentum as witnessed by current efforts in areas ranging from event driven architectures, complex event processing, and business process management and modeling to grid computing, web services notifications, event stream processing and message-oriented middleware. The increasing popularity of event based systems has opened new challenging issues for them. One such issue is measuring complexity of these systems. A well-developed system should be maintainable, pluggable, scalable and less complex. In this paper, an event based approach is proposed to derive software metrics for measuring system complexity. Events taking place in a system are documented using the proposed event template. An event-flow model is constructed from event templates. The event-flow model of an event based system is represented as an event-flow graph. The proposed event-flow complexity metric for analysis model is derived from an event-flow graph. The metric has also been evaluated in terms of Weyuker's properties. Results of evaluation show that it satisfies 8 out of 9 Weyuker's properties. A prototype tool is also developed to automatically generate event interdependency matrices and compute absolute and relative complexity of an entire system. The proposed technique can be very effective especially for real time systems where lots of events take place.

A computation model is proposed for senior citizen health self-care through adaptive multi-level computation cycles according to the principles of slow intelligence. An experimental system, called the TDR system, is being implemented on a smart phone to serve as the test bed for the proposed approach. In this paper, we describe the computation model, the basic concepts and the prototype experimental system. The main theoretical concept is centered on adaptive multi-level computation cycles, events and event icons. Further experimental as well as theoretical investigations of the proposed computation model are discussed.

This paper describes a new and innovative and approach for representing, recognizing and interpreting human activity from video, contributing to an automated system capable of recognition of complex human behaviors. This technology is directly applicable for monitoring public safety and law enforcement, and capturing of activities is crucial for supporting virtual collaborations between citizens, and between citizens and government. Digital video stores of terabytes are now common, and will continue to increase until they dominate stored data. The government of the future will have to manage, organize, recall, and interpret information from this resource, This paper addresses one important facet of this.

The approach presented here is a model based on the hierarchical synergy of three other models: the Local/Global (L-G) graph, the Stochastic Petri Net (SPN) graph and a neural network (NN) model. The application focus is the description of activity of actors in a video (or multi-sensor) scene, from the snapshot state description through higher levels of organization into events. The concept of importance is the distinction and interaction between structural knowledge, or knowledge about physical state, and functional knowledge, knowledge about change and events. The L-G graph provides a powerful description of the structural image features presented in an event, and the SPN model offers a description of the functional behavior. The NN (or other adaptive) model provides the capability of leaning behavioral patterns for classification of posture and activity, and forecasting possible events in a free environment.

A discussion of fundamental aspects of quantum theory is presented, stressing the essential role of “events.”

Open innovation (OI) events are potent instruments for the development of dynamic ecosystems. However, the literature analyses the structure and mechanisms of OI events insufficiently to demonstrate their efficacy, making it difficult to justify the investments necessary for their success. With better data confirming their impact, funding for OI events should improve by becoming more accessible and, therefore, more conducive to efficient value creation. This regional study contributes to the literature on innovation ecosystems and field-configuring events by responding to the call for more effective measures of OI events to coordinate and improve the ecosystems’ overall competitiveness. Based on an analysis of six in-depth case studies, 28 semi-structured interviews, and secondary sources, we identify 54 best practices and 34 indicators of an event’s success for various actor types. Moreover, we suggest 11 measures of the short- and long-term impacts of an event on its ecosystem.

One aspect of the psychology of weather and climate concerns the multiple meanings that may be associated with weather and climate as event. Atmospheric scientists and journalists have increasingly described both weather and climate as event. In this paper, the authors documented the increasing use of weather and climate as events in the scholarly literature of the American Meteorological Society and in newspaper articles over time. The authors also conducted pathfinder network scaling analyses with event-related terms to assess the meanings of events in academic and journalistic writing. The analyses suggested four contexts of event meanings: (1) study of ordinary weather or climate occurrences, (2) the study and attribution of severe and extreme weather, (3) societal impacts of weather, and (4) the public lexicon. Communicating about weather and climates as event contributes to the development and evolution of the public idea of climate change. The burgeoning of event in discourse contributes to the public idea of climate change in at least three ways: (1) events contribute specificity to the more general idea of climate change; (2) events contribute experientiality of climate change, and (3) events contribute exemplification to the public idea of climate change to the extent that weather events can be attributed to climate change.

In the first part of the paper I present a definition of becoming that overcomes the irrelevant as well as misleading debates between presentists and eternalists. Since my definition essentially requires an ontology of events occurring in temporal succession, I go on showing that not only the theory of relativity, but also quantum mechanics, in its various interpretations, requires such an ontology, despite the limitations in the possibility of representing quantum processes in a spatiotemporal arena.

The past few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in applications. Unreliability and a dynamic nature are frequently present in the field of WSN, making anomaly detection necessary. Although events are often functions of more than one attribute and the energy in sensors is limited, the combination of data fusion and spatiotemporal correlations can overcome these limitations effectively. In this paper, we propose a spatiotemporal correlation-based anomaly detection model to differentiate normal and abnormal events in WSNs. The update phase occurs when abnormal events are detected. We demonstrate the usability and advantages of applying the spatiotemporal relevance in anomaly detection. Experimental results indicate its high performance in handling multi-dimensional sensor data.

Seismic data can be used to ima the acoustic impedance variations in the earth. In order to convert such data an image that more. closely matches the vision of geology, image enhancement techniques including pattern recognition methods must be applied. A syntax-dependent approach employing a string-to-string matching algorithm matches peaks between traces on a seismic record. A filtering process then enforces matching coherence by correcting matches that deviate seriously from the general trend around anomalous pairs. Connected pairs form lateral coherent events which have a confidence measure. These events are targets of any seismic investigation. Clustering technique can be used to associate the events with geologic zones. The algorithm performs well in a test run and detects most of the strong reflections…