Investigating factors that influence social presence and learning outcomes in distance higher education

There are many factors that influence distance learning especially in higher education where collaborative and communicative discourse is necessary for pursuing knowledge. Social presence, among other factors, is an important concept to be facilitated, developed and sustained in distance higher education as it promotes and supports discourse based learning. This study examines the relationship among demographic and other variables, social presence and learning satisfaction. Results showed demographic variables, such as gender, online learning experience and work status were not significant factors in terms of influencing on either social presence or learning satisfaction. While media integration and instructor’s quality teaching were significant predictors of both social presence and learning satisfaction, interactivity among participants was a predictor of social presence but not of learning satisfaction. Along with the study findings, some implications were discussed for online learning practitioners in higher education setting.     

A leakage-aware L2 cache management technique for producer-consumer sharing in low-power chip multiprocessors

This paper proposes a novel leakage management technique for applications with producer-consumer sharing patterns. Although previous research has proposed leakage management techniques by turning off inactive cache blocks, these techniques can be further improved by exploiting the various run-time characteristics of target applications in CMPs. By exploiting particular access sequences observed in producer-consumer sharing patterns and the spatial locality of shared buffers, our technique enables a more aggressive turn-off of L2 cache blocks of these buffers. Experimental results using a CMP simulator show that our proposed technique reduces the energy consumption of on-chip L2 caches, a shared bus, and off-chip memory by up to 31.3% over the existing cache leakage power management techniques with no significant performance loss.     

Comparison of 3D and 2D menus for cell phones

Few prior studies have directly compared 3D and 2D menus for cell phones. Because the technology available for cell phone interfaces has changed in recent years, interface guidelines for cell phones need to be re-evaluated, especially with regard to the use of 3D interfaces. In the present study, we first compared performance of tasks for menus with different breadths using three 3D menus (revolving stage, 3D carousel, and collapsible cylindrical tree) shown on the small display screen of an iPhone® simulator. Performance was best with the revolving stage menu, and there was a nonsignificant tendency for it to be rated as preferred by the participants. Then, we compared the 3D revolving stage menu to a 2D overview menu, for tasks of different complexity and menus of different breadths, on an actual iPhone. The 3D menu was preferred by users at high breadth levels, and the 2D menu showed better performance than the 3D menu with low memory load. From the results, recommendations for the design of menus for small displays were developed.     

Examining knowledge contribution from the perspective of an online identity in blogging communities

Knowledge contribution is one of the essential factors behind the success of blogging communities (BCs). This research studies knowledge contribution behavior in a BC from the perspective of knowledge contributors and their characteristics using the lens of social identity theory. Social identity theory asserts that individuals are fundamentally motivated to present or communicate their identities in everyday social life through behavior. A similar line of reasoning can be used to argue that members of a BC would also be motivated to communicate their online identities through their behavior, that is, through knowledge contribution in the BC. Specifically, this study conceptualized the online identity and examined the effects of its personal (online kindness, online social skills, and online creativity) and social aspects (BC involvement) on knowledge contribution. The data was collected using an online survey from the members of Cyworld, a popular BC in South Korea and a few other countries (members from South Korea were included in this study). The results indicate that both the personal and social aspects of online identity and their interactions significantly influenced knowledge contribution. Based on the findings, this study offers suggestions to organizers of BCs to enhance the knowledge contribution from their members.     

On fuzzy renewal processes for fuzzy random variables and extended theorems

In this paper, we construct fuzzy renewal processes involving fuzzy random variables. We first extend the renewal processes to the fuzzy renewal processes where interarrival times, rewards, and stopping times are all fuzzy random variables. According to these fuzzy renewal processes, we then extend some theorems of renewal processes to those in fuzzy renewal processes. These are elementary renewal theorem, asymptotic expected average reward, and Wald's equation. In each part, we also give examples for applications. © 2010 Wiley Periodicals, Inc.     

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset.     

Region-based parallelization of irregular reductions on explicitly managed memory hierarchies

Multicore architectures are evolving with the promise of extreme performance for the classes of applications that require high performance and large bandwidth of memory. Irregular reduction is one of important computation patterns for many complex scientific applications, and it typically requires high performance and large bandwidth of memory. In this article, we propose region-based parallelization techniques for irregular reductions on multicore architectures with explicitly managed memory hierarchies. Managing memory hierarchy in software requires a lot of programming efforts and tends to be error-prone. The difficulties are even worse for applications with irregular data access patterns. To relieve the burden of memory management from programmers, we develop abstractions, particularly targeted to irregular reduction, for structuring parallel tasks, mapping the parallel tasks to processing units and scheduling data transfers between the memory hierarchies. Our framework employs iteration reordering based on regions of data along with dynamic scheduling of parallel tasks. We experimentally evaluate the effectiveness of our techniques for irregular reduction kernels on the Cell processor embedded in a Sony PlayStation3. Experimental results show the speedups of 8 to 14 on the six available SPEs.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

A fact-oriented ontological approach to human process modeling for knowledge-intensive business services

Modern business environment emphasizes the role of knowledge-intensive business services (KIBS). As well as the enterprises in any other kinds of industries, business process management (BPM) can be a source of core competency for the enterprises in KIBS industry. However, most of the business processes in KIBS are human processes which are collaborative, innovative, and dynamic, which cannot be supported well by current BPM technologies. Human interaction management (HIM) has emerged as an alternative theory to deal with human processes in KIBS. But, the current ways of process modeling and management in HIM have a problem of complexity, especially in terms of realizing its principle of ‘supportive activity management’. This research adopts fact-oriented ontological approach to address this problem. Based on fact-oriented approach, human-friendly condition modeling and state management during supportive activity management can be achieved.