• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2007.11a
• /
• pp.12-15
• /
• 2007

Online parallel machine scheduling problems have been studied by many researchers and enormous results are appeared in the last 40 years. With the development of scheduling theory and application, new online scheduling problems where the partial information is known in advance, that is, semi-online, gained much interest due to their increased application in practice. So we consider the online scheduling of three machines with general eligibility and its semi-online variant where the total processing time is known in advance. For the online and semi-online problems, we develop algorithms with competitive ratio of 5/2 which are shown to be optimal.

• Journal of Korean Institute of Industrial Engineers
• /
• v.36 no.4
• /
• pp.248-254
• /
• 2010

In this paper, we survey the parallel machines scheduling problem with GoS eligibility constraints so as to minimize the makespan. Our survey covers off-line, online and semi-online scheduling problems. In the case of online scheduling, we only focus on online scheduling one by one. Hence we give an introduction to the problem and present important results of the problem.

• Educational Technology International
• /
• v.24 no.1
• /
• pp.53-80
• /
• 2023

This study investigates faculty support for quality assurance in online education, and offers suggestions for its improvement based on feedback from Instructional Design (ID) staff working at a public university in the U.S. Qualitative research using semi-structured interviews was conducted with seven ID staff in order to examine their perceptions regarding faculty support related to quality assurance in online education. The results of the data analysis indicate that four types of faculty support-quality assurance reviews using Quality Matter (QM) standards, templates, individual consultations with ongoing support, and monitoring-were offered for faculty. Faculty support for quality assurance in online education could be improved by developing specific quality assurance standards, recruiting external experts, examining learning effects, developing a quality assurance management system, and sharing documents among ID staff. This study highlights the necessity of quality assurance in online education and provides cases of faculty support in a real higher education setting.

• International Journal of Contents
• /
• v.15 no.4
• /
• pp.89-96
• /
• 2019

This study examines the impact of online collaborative English language learning to enhance learner motivation and classroom engagement in university English instruction. The role of learner motivation and classroom engagement has gained much attention under the premises of current constructivist framework of English as a foreign language education. To promote learner motivation and classroom interaction in English instruction, participants in this study engaged in integrative English learning activities through online group collaboration and peer-tutoring. They exchanged productive peer response and shared their learning experiences throughout the integrative English learning activities. Digital technology played an integral role in motivating the learning process of the participants. Data for this study were gathered through an online questionnaire survey and semi-structured interviews. The data were analyzed based on the ARCS motivational model of instructional design to identify the motivational aspects of integrative English learning activities. This study reveals that participants of this study regarded online collaborative English learning activities as the positive and motivating learning experience. The online collaborative English reading instruction had positive effect on improving EFL university students' learning performance. Participants of this study also identified affective and metacognitive benefits of online collaborative EFL learning activities for learner motivation and classroom engagement. This study reveals that the social networking platform in online group collaboration played a crucial role for the participants in understanding the integration of online group collaboration as the positive and effective language learning strategy. This study may have implications in suggesting the effective instructional design for promoting learner motivation and classroom interaction in EFL education.

• Journal of Korean Institute of Industrial Engineers
• /
• v.35 no.3
• /
• pp.213-217
• /
• 2009

We consider the online scheduling problems of three and four machines under eligibility constraint. Respectively for the cases of three and four machines, we prove that AW algorithm has competitive ratios of $\frac{5}{2}$ and 3 which are shown to be optimal. Also, we show that the same results hold for the semi-online cases with prior knowledge of the total and the largest processing time.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.165-174
• /
• 2021

This study aimed to ascertain whether electronic courses at the deanship of electronic learning and distance education at Umm Al-Qura University meet the quality standards developed by the Quality Matters (QM) organization. This endeavor adopted a mixed method of an explanatory sequential research design for an in-depth understanding of the topic under scrutiny. The sample of the study consisted of ten courses designed at the deanship and reviewed using an evaluation form. The results showed that the courses in focus did not meet the criteria of QM. Based on this finding, a semi-structured interview was designed to collect relevant data from the syllabus designers at the deanship. The interviews yielded information on the difficulties the course designers faced when designing QM-criteria-based courses. The results obtained from the interviews showed that the designers experienced administrative, technical, and faculty-member-related challenges that, when producing online courses, intercepted their way to achieving the QM standards. The study closed with some recommendations, the most important of which is a call for re-developing online courses in alignment with the well-recognized QM standards.

• Journal of Information Technology Applications and Management
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2011

As the sales volume of online shops increases, the job burden in the back-offices of the online shops also increases. Order picking is the most labor-intensive operation among the jobs in a back-office and mid-size pure click online shops are experiencing the time delay and complexity in order picking nowadays while fulfilling their customers' orders. Those warehouses of the mid-size shops are based on manual systems, and as order pickings are repeated, the warehouses get a mess and lots of products in those warehouses are getting missing, which results in severe delay in order picking. To overcome this kind of problem in online clothing shops, we research a methodology to locate warehousing products. When products arrive at a warehouse, they are packed into a box and located on a rack in the warehouse. At this point, the operator should determine the box to be put in and the location on the rack for the box to be put on. This problem could be formulated as an Integer Programming model, but the branch-and bound algorithm to solve the IP model requires enormous computation, and sometimes it is even impossible to get a solution in a proper time. So, we relaxed the problem, developed a set of heuristics as a methodology to get a semi-optimum in an acceptable time, and proved by an experiment that the solutions by our methodology are satisfactory and acceptable by field managers.

• Journal of Multimedia Information System
• /
• v.4 no.4
• /
• pp.285-288
• /
• 2017

In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.406-409
• /
• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.718-732
• /
• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.