This study is focus on Opensource Opencourseware Prototype System (OOPS). The Opensource Opencourseware Prototype System was built by Mr. Lucifer Chu from 2004.This OOPS project recruit volunteer translators all over the world through internet to translate Opencourseware materials from Massachusetts Institute of Technology (MIT) into Chinese.This study is probing into the cause of the volunteers attend, indentity and contribution. This research is a qualitative case study, data sources were adopted include OOPS¡¦ archival information, volunteer¡¦s feedback, questionnaires from OOPS website. The study has the following conclusion after the ayalysis.OOPS offers a good way for the self-elarning person to get ride of the obestacle from English language in taiwan.At the same time it shows the indentity from the participator of OOPS. Keywords¡XMIT opencourseware,OOPS,Network Social Movement

This research studies the impact of Object Oriented Programming Systems (OOPS) and their underlying concepts on Land and Geographic Information Systems (LIS/GIS) in Australasia. This research considers GIS software development and conceptual data modelling aspects, and the strong relationship with proposed spatial data transfer standards. / Conventional programming techniques appear to have reached their limit in coping with complex and diversified applications. "Something better' is envisaged for future software developments and data models in LIS/GIS. The relatively new object-oriented design method is reviewed and a much improved object-oriented software module for the daily maintenance operations in a Digital Cadastral Data Base (DCDB) is presented as an example. This development allows a significant improvement of the spatial accuracy of DCDB systems and its graphical user interface (GUI) represents a much better data quality visualisation tool. / The choice of the right conceptual data model for GIS has a strong impact on proposed spatial data transfer standards and the way in which future Australian GIS communities will 'view' or model their real world. It has therefore been important to critically review these proposals in the Australian context.

Lorsqu'une erreur survient dans le noyau Linux, celui-ci émet un rapport d’erreur appelé "kernel oops" contenant le contexte d’exécution de cette erreur. Les kernel oops décrivent des erreurs réelles de Linux, permettent de classer les efforts de débogage par ordre de priorité et de motiver la conception d’outils permettant d'améliorer la fiabilité du code de Linux. Néanmoins, les informations contenues dans un kernel oops n’ont de sens que si elles sont représentatives et qu'elles peuvent être interprétées correctement. Dans cette thèse, nous étudions une collection de kernel oops provenant d'un dépôt maintenu par Red Hat sur une période de huit mois. Nous considérons l’ensemble des caractéristiques de ces données, dans quelle mesure ces données reflètent d’autres informations à propos de Linux et l’interprétation des caractéristiques pouvant être pertinentes pour la fiabilité de Linux. Nous constatons que ces données sont bien corrélées à d’autres informations à propos de Linux, cependant, elles souffrent parfois de problèmes de duplication et de manque d’informations. Nous identifions également quelques pièges potentiels lors de l'étude des fonctionnalités, telles que les causes d'erreurs fréquentes et les causes d'applications défaillant fréquemment. En outre, un kernel oops fournit des informations précieuses et de première main pour un mainteneur du noyau Linux lui permettant d'effectuer le débogage post-mortem car il enregistre l’état du noyau Linux au moment du crash. Cependant, le débogage sur la seule base des informations contenues dans un kernel oops est difficile. Pour aider les développeurs avec le débogage, nous avons conçu une solution afin d'obtenir la ligne fautive à partir d’un kernel oops, i.e., la ligne du code source qui provoque l'erreur. Pour cela, nous proposons un nouvel algorithme basé sur la correspondance de séquences approximative utilisé dans le domaine de bioinformatique. Cet algorithme permet de localiser automatiquement la ligne fautive en se basant sur le code machine à proximité de celle-ci et inclus dans un kernel oops. Notre algorithme atteint 92% de précision comparé à 26 % pour l’approche traditionnelle utilisant le débogueur gdb. Nous avons intégré notre solution dans un outil nommé OOPSA qui peut ainsi alléger le fardeau pour les développeurs lors du débogage de kernel oops. / When a failure occurs in the Linux kernel, the kernel emits an error report called “kernel oops”, summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this thesis, we study a collection of kernel oopses over a period of 8 months from a repository that is maintained by Red Hat. We consider the overall features of the data, the degree to which the data reflects other information about Linux, and the interpretation of features that may be relevant to reliability. We find that the data correlates well with other information about Linux, but that it suffers from duplicate and missing information. We furthermore identify some potential pitfalls in studying features such as the sources of common faults and common failing applications. Furthermore, a kernel oops provides valuable first-hand information for a Linux kernel maintainer to conduct postmortem debugging, since it logs the status of the Linux kernel at the time of a crash. However, debugging based on only the information in a kernel oops is difficult. To help developers with debugging, we devised a solution to derive the offending line from a kernel oops, i.e., the line of source code that incurs the crash. For this, we propose a novel algorithm based on approximate sequence matching, as used in bioinformatics, to automatically pinpoint the offending line based on information about nearby machine-code instructions, as found in a kernel oops. Our algorithm achieves 92% accuracy compared to 26% for the traditional approach of using only the oops instruction pointer. We integrated the solution into a tool named OOPSA, which would relieve some burden for the developers with the kernel oops debugging.

OOPS stands for Opensource Opencourseware Prototype System, a project sponsored by Fantasy Foundation. Aiming to benefit Chinese readers, this project recruit volunteer translators all over the world through internet to translate Opencourseware materials from Massachusetts Institute of Technology (MIT) into Chinese. This research was a qualitative case study conducted between 2004/2 ~ 2007/1. Multiple data sources were surveyed, including OOPS¡¦ online discussion forum, and archival information from OOPS website. Online archival data ranged from media reports, activity reports, e-newsletters, volunteer reports, survey summaries, and sub-group websites. Interviews with group leaders were also conducted. Evidence collected through these means were used to describe how OOPS employed the Internet to coordinate translation efforts and promote Opencourseware. In addition, this research applied Science of Complexity to explain the self organizing phenomenon within the network arisen from its participants. This research looked further into how Science of Complexity can clarify the organic process of a self organizing network going from simple to complex. This research found 1) the degree of openness in a collaborative knowledge network influenced its degree of self organization; 2) volunteer¡¦s willingness to participate was related to environment¡¦s fitness; 3) critical mass, diversity, variety, interaction and feedback promoted evolution; 4) a collaborative knowledge network¡¦s key to an organic expansion depended on participants¡¦ outgrowth and self organization; and 5) effective facilitation at the edge of chaos would foster new organization growth.