• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.1-17
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• 2007

A virtual enterprise (VE) can be mapped into a multi-agent system (MAS) that consists of various agents with specific role(s), communicating with each other to accomplish common goal(s). However, a MAS for enacting VE requires more advanced mechanism such as context that can guarantee autonomy and dynamism of VE members considering heterogeneity and complex structure of them. This paper is to suggest a context-based MAS as a platform for constructing and managing virtual enterprises. In the Context-based MAS a VE is a collection of Actor, Interaction (among Actors), Actor Context, and Interaction Context. It can raise the speed and correctness of decision-making and operation of VE enactment using context, i.e., information about the situation (e.g., goal, role, task, time, location, media) of Actors and Interactions, as well as simple data of their properties. The Context-based MAS for VE we proposed('VECoM') may consists of Context Ontology, Context Model, Context Analyzer, and Context Reasoner. The suggested approach and system is validated through an example where a VE tries to find a partner that could join co-development of new technology.

• Journal of Korean Library and Information Science Society
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• v.35 no.2
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• pp.199-218
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• 2004

The purpose of this study is to examine the emotional and behavioral characteristics of information seeking of university students in context in which a class assignment should be completed. An attempt was made to understand better individualized and selective behavior observed while seeking information in the commonly defined context. Data was collected from content analysis of self-reports by 54 university students majoring in library and information science. Major findings were analyzed in relation to the preception and feelings over, and barriers and strategies of seeking information, and the use of information resources.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.179-210
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• 2015

Despite the increasing adoption of gamification and its huge potential in tourism, research in gamification is still limited. As preliminary findings show that the effectiveness of gamification depends on the context of its application and the players' use of the gamified app, this paper fills in these gaps: by exploring and analysing the application of gamification in a specific e-commerce tourism context; and assessing the gamification's effectiveness by measuring the players' gamification usage and the latter's behavioural and psychological outcomes. The gamified TripAdvisor website and its Facebook enabled gamification app are used as the specific context of the study. Findings from a survey conducted on TripAdvisor users provide useful practical and theoretical implications to gamification designers and researchers alike on how game mechanics can be designed for enhancing the users' motivation, flow, task involvement and engagement with the 'play' tasks, and so, increasing the gamification's effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.3-22
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• 2001

An attention task, in which context information should be extracted from the first presented pattern, and the recognition answer of the second presented pattern should be generated using the context information, is employed in this paper. An Elman-type recurrent neural network is utilized to extract and keep the context information. A reinforcement signal that indicates whether the answer is correct or not, is only a signal that the system can obtain for the learning. Only by this learning, necessary context information became to be extracted and kept, and the system became to generate the correct answers. Furthermore, the function of an associative memory is observed in the feedback loop in the Elman-type neural network.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.308-310
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• 2022

With the rise of the Al wave, task-oriented dialogue systems have become one of the popular research directions in academia and industry. Currently, task-oriented dialogue systems mainly adopt pipelined form, which mainly includes natural language understanding, dialogue state decision making, dialogue state tracking and natural language generation. However, pipelining is prone to error propagation, so many task-oriented dialogue systems in the market are only for single-round dialogues. Usually single- domain dialogues have relatively accurate semantic understanding, while they tend to perform poorly on multi-domain, multi-round dialogue datasets. To solve these issues, we developed a paddle-based multi-domain task-oriented Chinese dialogue system. It is based on NEZHA-base pre-training model and CrossWOZ dataset, and uses intention recognition module, dichotomous slot recognition module and NER recognition module to do DST and generate replies based on rules. Experiments show that the dialogue system not only makes good use of the context, but also effectively addresses long-term dependencies. In our approach, the DST of dialogue tracking state is improved, and our DST can identify multiple slotted key-value pairs involved in the discourse, which eliminates the need for manual tagging and thus greatly saves manpower.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.101-111
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• 2016

Startup has lot of limitation such as time and budget shortage, and few human resources, and may be under the very stressful condition. Naturally in these context, there is always conflict among the startup team members, but the impact of conflict on teamwork or team performance has not been empirically tested. This study examines the relationship among the conflict, teamwork and team performance in startup context. Different two types of conflict and four factors of teamwork for team performance are identified from a literature review and tested; task conflict and relationship conflict as antecedents, teamwork was composed of communication, collaboration, coordination and cohesion, leading to team performance. 142 data points were collected from startup representatives to test these hypotheses. PLS data analysis indicated that the task conflict positively effects on all teamwork factors, but relationship conflict has statistically significant effect on only two teamwork factors, collaboration and coordination in negative relationship. Teamwork factors effects on team performance except communication. Based on the results, we proposed practically several team management skills for startup managers, leaders and even members, and explained theoretical contributions.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.2
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• pp.68-77
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• 2003

The Least-Laxity First(or LLF) scheduling algorithm assigns the highest priority to a task with the least laxity, and has been proved to be optimal for a uni-processor and sub-optimal for a multi-processor. However, this algorithm Is Impractical to implement because laxity tie results in the frequent context switches among tasks. In this paper, a Modified Least-Laxity First on Multiprocessor(or MLLF/MP) scheduling algorithm is proposed to solve this problem, i.e., laxity tie results in the excessive scheduling overheads. The MLLF/MP is based on the LLF, but allows the laxity inversion. MLLF/MP continues executing the current running task as far as other tasks do not miss their deadlines. Consequently, it avoids the frequent context switches. We prove that the MLLF/MP is also sub-optimal in multiprocessor systems. By simulation results, we show that the MLLF/MP has less scheduling overheads than LLF.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.388-402
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• 2003

In this paper, we carried out the word, 4 continuous digits. continuous, and task-independent word recognition experiments to verify the effectiveness of the re-defined phoneme-likely units (PLUs) for the phonetic decision tree based HM-Net (Hidden Markov Network) context-dependent (CD) acoustic modeling in Korean appropriately. In case of the 48 PLUs, the phonemes /ㅂ/, /ㄷ/, /ㄱ/ are separated by initial sound, medial vowel, final consonant, and the consonants /ㄹ/, /ㅈ/, /ㅎ/ are also separated by initial sound, final consonant according to the position of syllable, word, and sentence, respectively. In this paper. therefore, we re-define the 39 PLUs by unifying the one phoneme in the separated initial sound, medial vowel, and final consonant of the 48 PLUs to construct the CD acoustic models effectively. Through the experimental results using the re-defined 39 PLUs, in word recognition experiments with the context-independent (CI) acoustic models, the 48 PLUs has an average of 7.06%, higher recognition accuracy than the 39 PLUs used. But in the speaker-independent word recognition experiments with the CD acoustic models, the 39 PLUs has an average of 0.61% better recognition accuracy than the 48 PLUs used. In the 4 continuous digits recognition experiments with the liaison phenomena. the 39 PLUs has also an average of 6.55% higher recognition accuracy. And then, in continuous speech recognition experiments, the 39 PLUs has an average of 15.08% better recognition accuracy than the 48 PLUs used too. Finally, though the 48, 39 PLUs have the lower recognition accuracy, the 39 PLUs has an average of 1.17% higher recognition characteristic than the 48 PLUs used in the task-independent word recognition experiments according to the unknown contextual factor. Through the above experiments, we verified the effectiveness of the re-defined 39 PLUs compared to the 48PLUs to construct the CD acoustic models in this paper.

• The Journal of Industrial Distribution & Business
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• v.13 no.5
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• pp.27-40
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• 2022

Purpose: Research on job crafting has thus far focused on how alter job demand and resources behaviors relate to employee task performance. However, job crafting behaviors do not necessarily have an impact only on task performance, but also on employees' contextual performance, a phenomenon that has little research in job crafting research. Therefore, this study aims to investigate the effect of job crafting on task performance or contextual performance and the mediating effect of work engagement between them in the Chinese context. Research design, data and methodology: In order to achieve the above research goals and test the proposed hypotheses, we used a cross-sectional design and a self-administered questionnaire to collect quantitative data from September 8, 2021 to September 27, 2021 among knowledge workers in Shandong Province various financial companies and finally analyzed 211 questionnaires. Descriptive statistics and research model analysis were performed using SPSS 25.0 Version and AMOS 27.0 Version to test the developed hypotheses. Results: The results are as follows; firstly, the study showed that job crafting of employees had a significant positive impact on task performance and contextual performance. Secondly, the higher job crafting of employees, the higher their work engagement. Thirdly, this study showed that work engagement of employees had a positive impact on task performance and contextual performance. Fourthly, we predicted and found that work engagement of employees had a positive mediating effect between job crafting and task performance and a positive mediating effect between job crafting and contextual performance. Overall, this study showed that the proactive job crafting behaviors of employees enhance their engagement for their work, which in turn improves task performance and contextual performance. Conclusions: This paper develops job crafting research by exploring the positive impact of job crafting on employees' task performance or contextual performance through their work engagement. It also proposes that both job crafting behaviors and work engagement are important approaches to improve employees' task performance or contextual performance. Practical implications for organizations, such as increasing employee' work engagement, as well as the limitations and suggestions are concluded for the future research directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.