Abstract: Image data from cameras showing movement of a number of people, and sound data, is archived and processed to determine the position and orientation of each person's head and to determine at whom each person is looking. The speaker is determined by determining at which person most people are looking. Alternatively, the sound data is processed to determine the direction from which the sound came, and it is determined who is speaking by determining which person's head is in a position corresponding to the direction from which the sound came. The personal speech recognition parameters for the speaker are selected and used to convert the sound data to text data. Image data to be archived is chosen by selecting the camera which best shows the speaker and the participant to whom he is speaking. Data is stored in a meeting archive database.

Abstract: Image data from a plurality of cameras 2-1, 2-2, 2-3 showing the movements of a number of people, for example in a meeting, and sound data from a directional microphone array 4 is processed by a computer processing apparatus 24 to archive the data in a meeting archive database 60. The image data is processed to determine the three-dimensional position and orientation of each person's head and to determine at whom each person is looking. The sound data is processed to determine the direction from which the sound came. Processing is carried out to determine who is speaking by determining which person has his head in a position corresponding to the direction from which the sound came. Having determined which person is speaking, the personal speech recognition parameters for that person are selected and used to convert the sound data to text data. Image data to be archived is chosen by selecting the camera which best shows the speaking participant and the participant to whom he is speaking.

Abstract: It is common in distributed systems to replicate data. In many cases, this data evolves in a consistent fashion, and this evolution can be modeled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behavior, for example, to control synchronization times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modeled and outline how the probabilistic model may be utilized in various applications, concentrating on a news database example.

Abstract: It is common in distributed systems to replicate data. In many cases, this data evolves in a consistent fashion, and this evolution can be modeled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behavior, for example, to control synchronization times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modeled and outline how the probabilistic model may be utilized in various applications, concentrating on a news database example.