International Journal of Software Engineering and Knowledge Engineering

A combination of three algorithms is proposed that gives a reasonable rate of success in image similarity searches. All use region based statistical measures using a combination of color features, linear features, and intensities combined with a collection of methods for subdividing the image into regions. All possible combinations were evaluated and the best (highest success rate) was selected to be used as a practical image similarity measure. Component algorithms are combined using a simple decision fusion voting scheme, giving success rates in the range of 50–60%. For a web search application this is quite reasonable.

Distributed multimedia streaming systems are increasingly popular due to technological advances, and numerous streaming services are available today. On servers or proxy caches, there is a huge scaling challenge in supporting thousands of concurrent users that request delivery of high-rate, time-dependent data like audio and video, because this requires transfers of large amounts of data through several sub-systems within a streaming node. Unnecessary copy operations in the data path can therefore contribute significantly to the resource consumption of streaming operations. Despite previous research, off-the-shelf operating systems have only limited support for data paths that have been optimized for streaming. Additionally, system call overhead has grown with newer operating systems editions, adding to the cost of data movement. Frequently, it is argued that these issues can be ignored because of the continuing growth of CPU speeds. However, such an argument fails to take problems of modern streaming systems into account. The dissipation of heat generated by disks and high-end CPUs is a major problem of data centers, which would be alleviated if less power-hungry CPUs could be used. The power budget of mobile devices, which are increasingly used for streaming as well, is tight, and reduced power consumption an important issue. In this paper, we prove that these operations consume a large amount of resources, and we therefore revisit the data movement problem and provide a comprehensive evaluation of possible streaming data I/O paths in the Linux 2.6 kernel. We have implemented and evaluated several enhanced mechanisms and show how to provide support for more efficient memory usage and reduction of user/kernel space switches for content download and streaming applications. In particular, we are able to reduce the CPU usage by approximately 27% compared to the best approach without kernel modifications, by removing copy operations and system calls for a streaming scenario in which RTP headers must be added to stored data for sequence numbers and timing.

Massively multiplayer role-playing gaming (MMORPG) has become a very popular entertainment in Asia. Along with the success of the massively multiplayer role-playing gaming industry in Asia, online gaming-related crimes have grown at an amazing rate. Most of the criminal cases are related to virtual properties since markets have developed for the virtual properties giving them real world values. There has been little research and resulting technologies for MMORPG virtual property protection. In order to reduce the crimes and protect online gaming systems, one potential solution is protecting the virtual properties in online gaming systems. In this paper, we propose a virtual property management language to meter the use of virtual property. The language provides a framework for managing the use of virtual properties and recording the history of transactions to trace the life of virtual properties.

During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. However, it is necessary to deal with occlusions when the subject is wearing sunglasses, scarves and such. In the same way, ear recognition is arising as a new promising biometric for people recognition, even if the related literature appears to be somewhat underdeveloped. In this paper, several hybrid face/ear recognition systems are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The distribution of similarities in the input images is exploited as a signature for the identity of the subject. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.

The earliest design decisions often have a significant impact on software quality and are the most costly to revoke. One of the challenges in architecture design is to reduce the frequency of retrofit problems in software designs; not being able to improve the quality of a system cost effectively, a problem which frequently occurs during late stages. Software architecture assessment is essential in the design of a high quality system. However, assessing the effect of individual design decisions with respect to quality is often complicated by the fact that it is hard to identify exactly how particular qualities and quality factors are improved or impaired by design decisions. In this paper we present a framework that formalizes some of the relationships between software architecture and software quality; it compiles existing design knowledge (quality improving patterns) in a format suitable for architecture assessment. This framework may prevent the retrofit problem and can assist in reasoning about intra- and inter- quality tradeoffs. We illustrate our framework by creating an instance for it for the qualities usability, security and safety.

Embedded software development involves many knowledge acquisition (KA) tasks that collectively account for a significant proportion of development costs. This paper presents a pilot study that used case studies to investigate the extent to which knowledge acquired in learning tasks was used in developing a final product. Data obtained from the case studies were used to construct event chains, which trace the amount of time spent on KA tasks and the types of knowledge acquired in individual learning tasks. Knowledge acquired was separated into three categories: knowledge of data, knowledge of process, and knowledge of innovation. Each top-level category was further partitioned into productive and nonproductive knowledge depending on whether the knowledge was, or was not used, in the construction of the final product. The event chains were processed to visualize the chronological progression of how knowledge acquired in the separate categories moved between subcategories of productive and nonproductive knowledge as development progressed. Results of this analysis show trends in KA for the case studies investigated, which we plan to use in the design of future work on this topic.