In the burgeoning landscape of metaverse gaming, understanding player behaviour and preferences is crucial for both academic and practical applications. This study delves into the dynamic realm of metaverse gaming, exploring the influence of various factors on user satisfaction and continuance intention. The primary purpose was to examine the roles of self-expression, challenge and achievement, social interaction, violence catharsis, and game difficulty in shaping player experiences in the metaverse. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical method, the study analysed responses from 171 metaverse game users. The findings revealed intriguing patterns: while self-expression significantly impacts continuance intention, it does not affect immediate satisfaction. Challenge and achievement enhance satisfaction but not continuance intention, emphasizing the complex relationship between these constructs. Social interaction emerged as a critical factor positively influencing both satisfaction and continuance intention. Contrary to popular belief, violence catharsis did not significantly affect either satisfaction or continuance intention. Additionally, increased game difficulty was found to negatively impact both satisfaction and continuance intention. Satisfaction was shown to drive continuance intention. These insights bear significant implications for game developers and marketers, highlighting the need for nuanced game design strategies that emphasize social interaction and balanced challenges.

Forecasting network traffic is a challenging task for better network management. In this poster, we present a Border Gateway Protocol (BGP) traffic volume prediction framework that uses real BGP data from two famous Internet exchange points (IXPs) to train the LSTM network and generate future volume-based predictions. Our experimental evaluation shows that LSTM can indeed be used to predict BGP traffic volume with a very low prediction errors.

The megatrends and Industry 4.0 in ICT (Information Communication & Technology) are concentrated in IoT (Internet of Things), BigData, CPS (Cyber Physical System), and AI (Artificial Intelligence). These megatrends do not operate independently, and mass storage technology is essential as large computing technology is needed in the background to support them. In order to evaluate the performance of high-capacity storage based on open source Ceph, we carry out the network performance test of Abyss storage with domestic and overseas sites using KOREN (Korea Advanced Research Network). And storage media and network bonding are tested to evaluate the performance of the storage itself. Additionally, the security test is demonstrated by Cuckoo sandbox and Yara malware detection among Abyss storage cluster and oversea sites. Lastly, we have proposed the draft design of Data Lake framework in order to solve garbage dump problem.

Cloud computing paradigm has been driving the cloud-leveraged refactoring of existing ICT services including VoIP. In this paper, we design and prototype secured mobile VoIP services with open-source Asterisk PBX software by employing Docker lightweight virtualization for mobile devices. We also experimentally verify the quality of secured voice and the associated communication delay over distributed global connectivity environment.

The VoIP technology is being revisited to support diversified services and mobile computing has been changing to mobile cloud computing paradigm. In this paper, we design and implement cloud-leveraged secured voice services, enabled by the combination of open-source software PBX (private branch exchange), shared hardware platform, and mobile devices. We also test and verify the end-to-end delay of the secured voice communication. In addition, we propose a preliminary idea to improve the portability by adopting Docker-based containers.

The focusing concept of Internet of Things (IoT) is quickly changing from inter-connecting uniquely-identifiable embedded devices to 'things as a service'. Thus, diverse IoT services are being incubated by software-driven frameworks and shared/virtual/programmable resources. As a beginning step, in this paper, we discuss how to prepare and inter-connect hyper-converged SmartX Boxes so that it can play the miniaturized playground for IoT-Cloud service realization.

Exploring PubMed to find relevant information is challenging and time-consuming because PubMed typically returns a long list of articles as a result of query. Semantic network helps users to explore a large document collection and to capture key concepts and relationships among the concepts. The semantic network also serves to broaden the user's knowledge and extend query keyword by detecting and visualizing new related concepts or relations hidden in the retrieved documents. The problem of existing semantic network techniques is that they typically produce many redundant relationships, which prevents users from quickly capturing the underlying relationships among concepts. This paper develops an online PubMed search system, which displays semantic networks having no redundant relationships in real-time as a result of query. To do so, we propose an efficient semantic network construction algorithm, which prevents producing redundant relationships during the network construction. Our extensive experiments on actual PubMed data show that the proposed method is significantly faster than the method removing redundant relationships afterward. Our method is implemented and integrated into a relevance feedback PubMed search engine, called RefMed, "http://dm.postech.ac.kr/refmed", and will be demonstrated through the website.

Recently, novel recommender systems have attracted considerable attention in the research community. Recommending popular items may not always satisfy users. For example, although most users likely prefer popular items, such items are often not very surprising or novel because users may already know about the items. Also, such recommender systems hardly satisfy a group of users who prefer relatively obscure items. Existing novel recommender systems, however, still recommend mainly popular items or degrade the quality of recommendation. They do so because they do not consider the balance between novelty and preference-based recommendation. This paper proposes an efficient novel-recommendation method called Personal Popularity Tendency Matching (PPTM) which recommends novel items by considering an individual's Personal Popularity Tendency (or PPT). Considering PPT helps to diversify recommendations by reasonably penalizing popular items while improving the recommendation accuracy. We experimentally show that the proposed method, PPTM, is better than other methods in terms of both novelty and accuracy.

When a user finds the answer using question answering system, sometimes the user may not be satisfied with answers. This is because it is difficult to deliver the intention of a user question to the system due to the lack of expression ability and polysemy and etc. So, the system must provide additional ways to search the correct answers. In this paper, we propose a new method to recommend questions that can satisfy the purpose of user question with high probability. We define the supplementary similarity to calculate the similarity between sentences which have a few common terms. And we define the fuzzy relation product operator to find the questions that are recommended when user is not satisfied with the answers. User can select the question among the list of recommended questions and has more chance to find the answers that the user can be satisfied with. We experiment and show that the performance of the system gets high as the recommendation is repeated.