6322581
The Rochester checkers player: multimodel parallel programming for animate vision
It is maintained that to exploit fully the parallelism inherent in animate vision systems, an integrated vision architecture must support multiple models of parallelism. To support this claim, the hardware base of a typical animate vision laboratory and the software requirements of applications are described. A brief overview is then given of the Psyche operating system, which was designed to support multimodel programming. A complex animate vision application, checkers, constructed as a multimodel program under Psyche, is also described. Checkers demonstrates the advantages of decomposing animate vision systems by function and independently selecting an appropriate parallel-programming model for each function.<<ETX>>
16263161
Operating System Support for Animate Vision
Animate vision systems couple computer vision and robotics to achieve robust and accurate vision, as well as other complex behavior. These systems combine low-level sensory processing and effector output with high-level cognitive planning-all computationally intensive tasks that can benefit from parallel processing. A typical animate vision application will likely consist of many tasks, each of which may require a different parallel programming model, and all of which must cooperate to achieve the desired behavior. These multi-model programs require an underlying software system that not only supports several different models of parallel computation simultaneously, but which also allows tasks implemented in different models to interact. This paper describes the Psyche multiprocessor operating system, which was designed to support multi-model programming, and the Rochester Checkers Player, a multi-model robotics program that plays checkers against a human opponent. Psyche supports a variety of parallel programming models within a single operating system by according first-class status to processes implemented in user space. It also supports interactions between programming models using model-independent communication, wherein different types of processes communicate and synchronize without relying on the semantics or implementation of a particular programming model. The implementation of the Checkers Player, in which different parallel programming models are used for vision, robot motion planning, and strategy, illustrates the use of the Psyche mechanisms in an application program, and demonstrates many of the advantages of multi-model programming for animate vision systems. 0 1992 Academic Press, Inc.
20724117
A Framework for Predictive Data Mining in the Telecommunications Sector
Nowadays, the Internet comprises of huge amount of electronic information concerning different companies’ financial performance. This amount greatly exceeds our capacity to analyze it, the problem being that we often lack tools to quickly and accurately process these data. DM techniques are interesting mechanisms that can be applied to rapidly changing industries, in order to get an overview of the situation. One such market is the international telecommunications industry. In this paper we construct a framework using DM techniques that enables us to make class predictions about telecommunication companies’ financial performance. Our methodology allows us to analyze the movements of the largest telecommunications companies, to see how companies perform financially compared to their competitors, what they are good at, who are the major competitors in this industry, etc. The dataset contains 88 companies from five different regions: Asia, Canada, Continental Europe, Northern Europe, and USA, and consists of seven financial ratios per company per year. The data used to calculate the ratios were collected from companies’ annual reports (between 1995 and 1999), using the Internet as the primary medium. We used data from 2000 and 2001 to test our classification models. We have obtained good maps (SOMs) in terms of easeof-readability and the average quantization error and clearly identified the six financial performance clusters. The results of our class prediction models also correspond very well with the SOM model.