Earticle

Users of smartphone-based Instant Messenger such as Whatsapp, wechet, LINE and KAKAO TALK are rapidly increasing. LINE acquired more than 50 million members by December 2012 just 1 year after its release, and as of April 2014, the number of LINE mobile instant messenger users was 400 million Users. Given the rarity of research on the use of instant messengers in smartphones, this study analyzed factors affecting user satisfaction by conducting a survey on 220 users of mobile instant messengers in smartphones. The survey results showed that self-disclosure, flow, and social presence significantly affected user satisfaction. This study will benefit researchers and contribute to industries that possess interest in factors affecting user satisfaction regarding the use of smartphone applications and mobile messengers.

This paper presents a game-based learning study in Malaysia in which a game has been designed for remedial Mathematics students at primary school level. Mathematics is a core subject taught in all primary schools in Malaysia. However, for students who are recognized as weak in Mathematics, teachers use remedial modules supplied by the Ministry of Education in teaching and learning activities. A research study has been initiated to propose digital game-based learning for the remedial students, aiming to enhance the effectiveness of the modules. A prototype game is designed to be playable on tablet computers. The game contains lessons under the title of Whole Numbers: Addition in Year Two syllabus. This paper reveals the concepts and rationale behind the study and the game design. The research plan is also included to justify the ecological validity of the study.

In language education, little attention has been paid to the importance of variations of a language. It is focused only on acquiring standard forms. However, knowing standard forms is not sufficient for understanding the target language in real situations. That is why when we learn a target language; we have to pay attention to its linguistic variations. It is believed that e-learning is the most appropriate means of teaching and learning those dialects, because it can offer multiple speaking contexts in the same time and space. Using and incorporating multimedia content is very helpful in enabling language learners to experience more realistic and practical usage of those variations. From this perspective, this paper gives a brief idea of building e-learning applications that are capable of teaching and learning not only standard, but also variations of a language.

With the deep application of RFID, the deployment of the RFID system is gradually to have the large-scale, networked, distributed development trend. The previous abnormal data analysis and processing way can not meet with the need of data processing. According to the survey of international famous consulting company (Gartner) for the "information overload", results show that more than 90% of the enterprises think the enterprise competitiveness is closely related to its information capacity. In order to obtain the useful information, we must analyze, process the data, and exclude "exception" data. So, it is very important to realize the detection of abnormal data. This paper presents the RFID supply an abnormal data analysis and processing system of RFID supply chain based on distance, rules and middleware technology .This system is divided into three levels: data processing layer, anomaly analysis lay and graphical display layer. Finally, through the performance analysis of the system, the effectiveness of the system has been verified.

Wireless sensor networks (WSNs) connect devices and enable collaboration on sensing and monitoring various physical phenomena for applications such as target tracking, infrastructure security, battlefield surveillance, health monitoring, and traffic control. WSNs are generally comprised of a large number of tiny sensor nodes that are battery powered. Therefore, existing medium access control (MAC) protocols for WSNs have mainly been designed for energy saving. However, existing WSN systems have considerable drawbacks, and especially limit coordination of sensor nodes for mission-critical applications, due to their large-scale nature. In this paper, we propose an energy-efficient and delay-guaranteeing algorithm inspired by biological systems, which have gained considerable attention as approaches for computing and problem solving. We introduce both a local status indicator and an active-status indicator for a sensor node to represent its local environmental conditions and its node state, respectively, in WSNs. From consideration of the analogies between cellular signaling systems and WSN systems, we formulate a new mathematical model that considers the networking challenges of WSNs. The proposed bio-inspired algorithm determines the state of the sensor node, as required by each application and as determined by the local environmental conditions, and the states of adjacent nodes. Simulation results indicate that the proposed scheme provides significant energy savings, as well as reliable delay-guarantee, by controlling the states of the sensor nodes.

Due to their popularity and rapid propagation capability, social media have become important communication channel on public opinions because they work as a main virtual space to raise new public issues, discussion on these issues, and make opinion directions on them. With the recognition of the importance of social media, many enterprises and governments try to use social media as marketing tools or public opinion sensing tools. Nuclear power has become one of trending public issues in Korea after Fukushima nuclear disaster. Nuclear power is a double-edged sword because it is the most efficient way to generate electricity currently and it also has dangerous potential risk such as radiation leakage. Governments and policy makers need to monitor public opinion on nuclear power continuously because it can be changed depending on related events. In this study, we aim to propose an approach to sense public opinions on nuclear power and to analyze their trend based on opinion mining techniques. Also we propose a measure to trace the changes on directions of public opinion on nuclear power. To show the usefulness of the proposed approach, we had gathered tweets on nuclear power in Korean from 2009 to 2013. After classification of tweets from 2009 to 2011, sentimental dictionary including positive and negative terms had been constructed. The performance testing using tweets from 2012 to 2013 showed that the proposed approach can be applied in practice.

Sentiment analysis in news is different from normal text sentiment analysis. News usually have a specific topic, a focus semantic emotion, therefore, this paper, based on the principal of using Emotion Dependency Tuple (EDT) as the basic unit of news emotion analysis, resolves topic sentiment analysis in news into three progressive sub-problem, namely, topic sentence recognition, EDT extraction and topic sentiment analysis. We use an improved TF-IDF and cross entropy to extract feature set of topics. Then, based on space vector model, calculate the topic association of a sentence and extract topic sentence. Finally, we construct topic sentence based on EDT and complete clustering of news topic sentiment. This method is evaluated using COAE2014 dataset, and differential means shows that our results close to the best results. This shows that the topic based EDT could effectively improve performance of sentiment analysis in news.

Massive Open Online Courses (MOOC) platforms provide a rich environment for knowledge creation through its massiveness and inherited collaborative tools. However, it also restricts spontaneous knowledge sharing by the existing LMS barriers between the main multimedia content and the collaborative tools. None the less, the collaboration still massive due to the number of participants. The separation of the multimedia content and the discussion tools is the first focus point of this paper. Moreover, this article is presenting a new added value to the MOOC architecture so to link the learner’s discussions and its summary with the multimedia contents. The added-value component involves a summarization algorithm that summarizes the shared collaborative textual discussion collected from the various learners viewing relevant MOOC multimedia/video contents. The affectivity of the summarization component was tested using the popular ROUGE software package from University of Southern California. The new MOOC architecture represents an enhanced learning environment that enables learners to share the multimedia information along with its annotated collaborative information with the power of summarizing the final outcome of the presented annotations relevant to a specific shared multimedia content.