Flexible role transition management in scripting language / Zainura Idrus

Networked collaborative virtual environment (NCVE) allows users from diverse locations to work together via virtual workspaces. It is a complex environment requiring coordination amongst team members who are physically invisible and have loose-tie team relationships. To enhance team coordination, roles have been utilized to manage the segregation of tasks among users. Research shows that role transition is a key factor in a successful business process. It acts as a medium for a team to resolve conflict amongst its members. If the changes in roles are not managed effectively, the collaborative works can be disrupted and impose undue pressure on users. However, most studies in managing dynamic groups for NCVE are more inclined to resolve domain specific role transition issues. Furthermore, most existing role-transitions in NCVE must be dealt with manually by external entities to the NCVE system, which are solely done through human intervention. As a result, role transitions are hardly matched or coped with. Hence, this research explores the feasibility of having a socio-technical approach in managing role transitions that can be embedded in NCVE systems to assist both users and computer automation in managing role-transition. This research begins by conducting a case study, which is aimed at observing real-life scenarios in a call center environment

Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains

Towards interface design for virtual database / Zanariah Idrus...[et al.]

Today, big data has become as one of the important contribution in database management. It led to innovative ways of storing and organizing data which include structured and unstructured data. The unstructured data such as in news, reports, chats and surveys are basically loaded with heavy text data and numerous format. Thus, these data become challenging to be used for diverse purpose and are not appropriate to be stored in database. However, virtual database method has the capability to organize the unstructured data, and reconstruct into firm and concrete data. This approach carry out two major processes in databases which are mining and managing the data. However, the main problem is the insufficient support between people using databases and the heap of data collection. This is due to unawareness of clustered data organization as information is stored implicitly. Thus, this paper presents the conception of clustered data using the interface design model. Alignment of features and connections between the interface and knowledge composition allow users to access knowledge proficiently

Dynamic user management model in interactive networked collaborative environment / Siti Zaleha Zainal Abidin … [et al.]

Interactive networked collaborative virtual environment (NCVE) provides ways for people to work together, faster and cheaper despite of locations. Results can be achieved without travelling time and spending more money. Unfortunately, users' existence is not visibly presence which results in lacking of user participation that seriously degrades communication quality. This leads to misinterpretation of vital information and jeopardize the group work. Therefore, it is crucial to present users so that others know with whom they are interacting with (presence), when to communicate (state) and what to do (role) during the collaboration. This research investigates the activities of users in order to identify all the possible job functions exist during collaboration. Then, their job functions are classified in relation to the management of dynamic invisible users (their presence, states and roles). The users' role, presence and state will be generalized to form a generic management model of dynamic users based on logical notation. This work employs data collection through literature review and a case study. For a case study, a monopoly game will be implemented by changing some of its rules to gather all the possible roles exist throughout the game. The game is also used as a platform to do an abstraction of real life phenomena. A mapping of users' job functions found in both literature review and the case study will be conducted to produce comprehensive elements for managing users in collaborative systems. All the elements and their relations are classified and represented as a set of mathematical equations. The model helps to manage users dynamically throughout the collaborative session. This session-based dynamic role feature is rarely found in any collaborative work as compared to application-based role. Furthermore, managing users in virtual space is challenging because it involves security, privacy and permission to information resources

Language-based user management in interactive networked collaborative environment / Assoc. Prof. Siti Zaleha Zainal Abidin, Dr. Nasiroh Omar and Zainura Idrus

In interactive networked collaborative virtual environment (NCVE), users work in a group to involve in activities such as game, business transaction and learning. However, their existence is not visibly presence. Therefore, there is a crucial need to represent the invisible users so that users know with whom they are interacting with (presence), when to communicate (state) and what to do (role) during the collaboration. Unlike any group work or organizations, users in virtual space are dynamic. They can change their role and states frequently such as joining or leaving the group work at any time. They can engage in the collaboration only for a period of time or until the goals are achieved. This research will investigate and propose the generic way of managing invisible users (their presence, states and roles) through language-based approach. The novelty lies on the new design of language constructs for managing dynamic users. The language constructs offer faster and easier development of collaborative applications (game, commerce, defense, banking, education)

Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains

Experimenting with pair programming in an introductory programming course (Pengujian kebolehgunaan perpustakaan digital)

This paper describes how pair programming is conducted in the collaborative learning of an introductory programming course at University Technology of MARA (UiTM), aimed at increasing students’ motivation and confidence. The paper begins by defining the term pair programming and discussing the rationale for bringing industry’s pair programming into the classroom, as supported strongly by findings from numerous research. The mechanism for implementation and certain issues that might compromise the effectiveness of the pairing are also discussed. Our findings show that pairing not only does not compromise students’ learning but that it enhances students’ enjoyment, confidence and quality of programs. Thus, suggesting that pair programming is an effective pedagogical tool for teaching any programming cours