Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.

In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.

Summary A number of web applications provide completely automated machine translation services, allowing users to easily translate information of interest. However, these services still generate inaccurate results when translating technical terminologies. Therefore, we propose a new method that collects reliable pairs of English–Korean technical terms and translates the given English terminology to Korean. To collect the pairs, we utilize textual big data, such as Korean academic papers, and develop a new statistical model to determine appropriate characteristics. Our method is evaluated in terms of the reliability of English–Korean pairs and the precision of translation. We thus confirm that our method can produce highly reliable data and can positively influence the translation quality of technical terminologies. Copyright © 2014 John Wiley & Sons, Ltd.

Academic research information service is a must for surveying relevant studies in research and development activities. However, in-depth usability evaluation studies about the service are not enough when compared with its importance. This study emphasizes needs of the evaluation in a way that introduces the case of OntoFrame improved by repeated usability evaluations. We perform two different kinds of usability evaluations; first is for confirming service objectives through FGI (Focus Group Interview), second is for verifying service usefulness through FGI and usability test Many usability problems were found and solved in the next version indebted with the evaluations.

This paper describes a compound unit (CU) recognizer as a pattern‐based approach and its hybridization with rule‐based translation. A compound unit is a combined concept including collocations, idioms, and compound nouns. CU recognition reduces part of speech ambiguities by combining several words into a unit and consequently lessening the parsing load. It also provides pretranslated natural equivalents. Our focus in this paper is to obtain flexibility and efficiency from pattern‐based machine translation, and high‐quality translation by hybridization. A modified trie, our search index structure using “method” strategy is used to manage heterogeneous property of the constituents. Syntactic verification is integrated to obtain precise CU recognition by means of pruning wrongly recognized units that are caused by improper variable hypotheses. The experimental result with verification shows that the precision of CU recognition is increased to 99.69% with 31 CFG rules on the cyclic trie structure for 1,268 Wall Street Journal articles of the Penn Treebank. Another experiment with CU recognition also shows that it raises the understandability of translation for Web documents.