Higher education is the most important way to cultivating software engineering talents in China, but there is an education gap between the industry and the universities. The current curriculum in university lacked in transferring the experience and skills to students. In this paper, we present how to use project-based learning and collaborative learning in software engineering talent cultivation. We established a learning organization in college of informatics of South China Agricultural University which was named Human Computer Interaction Studio (HCIS). Students can join HCIS on a voluntary basis, and then they can take part in a project team to become a team member. The team accepts a project according to their interest and ability, and then the project manager allocates tasks, formulated work and learning plan. The members of a team would exchange information, discuss some questions and accomplish their tasks together. The study had achieved many good effects in practice, employment and academia. Students can gain expertise, necessary experience and some skills after they used project-based learning and collaborative learning. The results indicated that using project based learning and collaborative learning can be a good method for software engineering talent cultivation in university.

High-speed broadband telecommunication systems are built with extensive redundancy and complex management systems to ensure robustness. The presence of a fault may be detected by the offending component, its parent component or by other components. This can potentially result in a net effect of a large number of alarm events being raised. There can be a considerable amount of alarm depending on the size and configuration of the network. Data visualization can reduce mountains of data to visually insightful representations, which can aid decision-making and identification of faults. The paper explores data visualizations to provide a context to assist in the identification of such faults.

Visualising large quantities of hierarchical information is a difficult topic in information visualisation and it has been attracting much effort since the emergence of this research area. The paper presents the FlexTree, an approach to visualising, navigating and analysing large hierarchies. It is based on the focus+context technique and combines the power of the histogram with traditional two dimensional (2D) node-link diagrams. This approach maintains the context of a large hierarchy while providing easy and consistent access to details of multiple focal points. Simple aesthetic rules and an interactive design were applied to the system. As a demonstration of the approach a computer file system hierarchy with 6,351 file folders and 130,400 files on a personal computer has been successfully visualised.

Visualization integrated with data mining can offer 'human-assisted computer discovery' and 'computer-assisted human discovery'. Such a visual environment; reduces the time to understand complex data, thus enabling practical solutions to many real world problem to be developed far more rapidly than either humans or computers operating independently. In doing so the remarkable perceptual abilities that humans possess can be utilized, such as the capacity to recognize lanes quickly, and detect the subtlest changes in size, color, shape, movement or texture. One such complex real world problem is fault management in global telecommunication systems. These system have a large amount of built in redundancy to ensure robustness and quality of service. Unfortunately, this means that when a fault does occur, it can trigger a cascade of alarm events as individual parts of the system discover and report fallen making it difficult to locate the origin of the fault. This alarm behavior has been described as appearing to an operator as non-deterministic, yet it does result in a large data mountain that is ideal for data mining. The paper presents a visualization data mining prototype that incorporates the principles of human and computer discovery, the combination of computer-assisted human discovery with human-assisted computer discovery through a three-tier framework. The prototype is specifically designed to assist in the semi-automatic discovery of previously unknown alarm rules that can then be utilized in commercial role based component solutions, "business rules", which are at the heart of many of todays fault management systems.