Derchian Tsaih

A knowledge-commercialised business model for collaborative innovation environments

Globalisation is currently not sufficient for competition among enterprises. Enterprises must adopt an open innovative business model and use a cooperative method across enterprises, cultures and industries to execute enterprise activities. To reduce innovation costs and the risks associated with new knowledge development, enterprises must import new knowledge or technologies through knowledge trading or exchange between enterprises. This study proposes an innovative business model in which knowledge suppliers and requesters can trade knowledge online. The study first identifies the knowledge business model from five perspectives: role; product and service; process; price; benefit. Then, this study presents several knowledge models, including the knowledge product structure model, the knowledge requirement model, the knowledge license model and a Knowledge Commerce Platform Framework based on the proposed knowledge-commercialised business model as an infrastructure for practising collaborative knowledge business. The innovative knowledge business model and platform proposed by the study can: (1) promote reusing collaboratively constructed knowledge; (2) create added value of enterprise knowledge by integrating and assembling the knowledge anew; (3) provide knowledge requesters and providers with a quick, secure and fair channel for knowledge trading and exchange.

The influence of learning style on satisfaction and learning effectiveness in the asynchronous web-based learning system

Purpose The purpose of this paper is to investigate the effect of user’s learning style (including accommodators, divergers, convergers, and assimilators) on user’s satisfaction on the web-based learning system and their learning effectiveness. Design/methodology/approach This experimental research used the college students from a technology institute in Taiwan as the subject sources. By using the Kolb’s learning style model, the students are classified as four types of learners: convergers, divergers, assimilators, and accommodators. The authors analyzed the relationships among the different learning styles with their effectiveness of learning and satisfaction of using the web-based learning system. The mediation effect of gender is also presented. Findings This research indicates that: first, the satisfaction of the web-based learning system has significant influence on the learning performance of learners; second, different learning styles learners have no significant effect to the satisfaction on using the web-based learning system; third, learning effectiveness has significant difference among different learning style learners on the web-based learning system; the learning effectiveness of accommodators and divergers was significantly higher than the assimilators; fourth, different learning styles learners show significant difference in gender proportion. In addition to accommodators, whose proportion of women is higher than men, the other three learning styles’ proportions in men are higher than women. Research limitations/implications This study was grounded in the Kolb’s learning style theory. The authors provide implications for academic studies in e-learning research stream that aimed at understanding the role of learning style as well as gender differences in the asynchronous web-based learning system. Practical implications Results from this study provided the implications for students, educators, and e-learning system designers. The design of teaching materials as well as functions of e-learning systems should take learners’ learning style into consideration to ensure the best learning outcome. Originality/value This study examined the students’ learning style as well as gender differences in the asynchronous web-based learning system. An experiment was conducted to ensure the data were collected in a controlled environment, thus, offer the value that most of the prior study lacks.

Winning Prediction in WoW Strategy Game Using Evolutionary Learning

Over the past decades, real-time strategy (RTS) games have steadily gained in popularity and have become common in video game leagues. However, a big challenge for creating human-level game AI is the different traits of races of opponents and their locations of enemy units are partially observable. To overcome this limitation, we explore evolutionary-based approach for estimating the location of enemy units that have been encountered. In this paper, we propose an efficient framework to predict the winning ratio between the different races used in the real-time strategy game. We represent state estimation as an optimization problem, and automatically learn parameters for the evolutionary-based model by learning a corpus of expert Star Craft replays. The evolutionary-based model tracks opponent units and provides conditions for activating tactical behaviors in our Star Craft boot. Our results show that incorporating a learned evolutionary-based model improves the performance of EISBot by 60% over baseline approaches.

Online Opponent Modeling for Action Prediction

It is difficult for humans to behave randomly. Over the past decades, psychological research shows that we cannot randomize our responses, even if we specially try. Therefore, predicting a players actions through opponent modelling is essential in design of game AI. For Online games, learning to anticipate one players actions also is a great benefit to play against a completely different player. In this paper, we propose a novel N-Gram based algorithm and use a suffix matching tree to model opponent behaviours. By the model, we can predict the players next style efficiently using the last few actions seen. Compared with our previous data stream mining approaches, naming MSSBE and MSSMB, an approximate 40% improvement is achieved in accuracy of prediction. The proposed method is simple in its implementation and has the potential for parallel processing in GPU.

Serious Game for Haka-Based Cultural Heritage Leraning

Games have been an important activity in human society with a meaningful impact in education. In this context, serious game joins entertainment and education for presentation, simulation or training of activities in several areas. This paper reports the importance of planning in serious games, particularly to identify the best approach to be adopted for the development. The main focus is the design of serious games for Haka-based cultural heritage education for children and adults. By focusing on those cultural heritage artifacts that pertain to the field of arts and archeology, thus driving the reader to reflect on the pedagogical impact that may derive from exploiting serious game potentialities. Serious Game, in fact, on the one hand, offers an easier access and a multi-perspective view of cultural h heritage artifacts, and, on the other, may also enrich and improve cultural heritage education thanks to the adoption of innovative learning and teaching methods.

Clustering spatial data for aggregate query processing in walkthrough: a hypergraph approach

Nowadays, classical 3D object management systems use only direct visible properties and common features to model relationships between objects. In this paper we propose a new Object-oriented HyperGraph-based Clustering (OHGC) approach based on a behavioral walkthrough system that uses traversal patterns to model relationships between users and exploits semantic-based clustering techniques, such as association, intra-relationships, and inter-relationships, to explore additional links throughout the behavioral walkthrough system. The final aim consists in involving these new links in prediction generation, to improve performance of walkthrough system. OHGC is evaluated in terms of response time and number of retrieved objects on a real traversal dataset.

Service-Oriented Architecture for Distributed Enterprise Portal for Maintaining Global Patch Consistency

This paper presents the design, implementation of an extensible, scalable, web services ripple propagate routable base, transnational hierarchical global patch consistent model called THGPCM (Transnational Hierarchical Global Patch Consistent Model). Improving the OPDS (Original Patching Data Source) enabled network equipments, capable of updating patch parameter that existed in enterprise with specified OPDS partially dependency relationships. The paper also proposed a new scalable patching replica routing service algorithm, that dynamic reconfiguration distributed forwarding data path within enterprises hierarchical region enterprise portal. On the basis of the ubiquitous replica consistency framework, many active patch flows can be pushed by real time, periodic, reservation, and queuing with priority / degradation / preemption etc. Apply scenario can reduce the global patch service cost of transnational enterprise network equipments and minimum the turnaround time of patch service delay.

An Efficient Framework for Distributed Enterprise Portal for Maintaining Global Patch Consistency

This paper presents the design, implementation of an extensible, scalable, web services ripple propagate routable base, transnational hierarchical global patch consistent model called THGPCM (Transnational Hierarchical Global Patch Consistent Model). Improving the OPDS (Original Patching Data Source) enabled network equipments, capable of updating patch parameter that existed in enterprise with specified OPDS partially dependency relationships. The paper also proposed a new scalable patching replica routing service algorithm, that dynamic reconfiguration distributed forwarding data path within enterprises hierarchical region enterprise portal. On the basis of the ubiquitous replica consistency framework, many active patch flows can be pushed by real time, periodic, reservation, and queuing with priority/degradation/preemption etc. Apply scenario can reduce the global patch service cost of transnational enterprise network equipments and minimum the turnaround time of patch service delay.