There has been considerable efforts in modelling the semantics of Internet of Things and their specific context. Acquiring and managing metadata related to the physical devices and their surrounding environment becomes challenging due to the dynamic nature of environment. This paper focuses on managing metadata for Internet of Things with the help of crowds. Specifically, the paper proposes a collaborative approach for collecting and maintaining metadata through micro tasks that can be performed using variety of platforms e.g. mobiles, laptops, kiosks, etc. The approach allows non-experts to contribute towards metadata management through micro tasks, therefore resulting in reduced cost and time. Applicability of the proposed approach is demonstrated through a use case implementation for managing sensor metadata for energy management in small buildings.

This paper describes the Dublin City University terminology translation system used for our participation in the query translation subtask in the medical translation task in the Workshop on Statistical Machine Translation (WMT14). We deployed six different kinds of terminology extraction methods, and participated in three different tasks: FR-EN and EN-FR query tasks, and the CLIR task. We obtained 36.2 BLEU points absolute for FR-EN and 28.8 BLEU points absolute for EN-FR tasks where we obtained the first place in both tasks. We obtained 51.8 BLEU points absolute for the CLIR task.

Virtual simulation is one of the best methods for training students and understanding events and has been used in many fields of science yet, for example: space researches. For this reason, creating of an environment that can simulate the operations of satellites will be very usable and appropriate. This paper will present the details of a newly constructed 6-DoF experimental Satellite Virtual Simulator (SVS) facility at the Space Research Laboratory (SRL) at the K.N.Toosi University of Technology. SVS is used for studying the translational and rotational motions of a satellite and testing the control and communication concepts. The main components of the facility are 3-D screens, video projectors, user interface, central server system, 3-D glasses for users and its modeling software.

Technical advances and its increasing availability, mean that Machine Translation (MT) is now widely used for the translation of search queries in multilingual search tasks. A number of free-to-use high-quality online MT systems are now available and, although imperfect in their translation behaviour, are found to produce good performance in Cross-Language Information Retrieval (CLIR) applications. Users of these MT systems in CLIR tasks generally assume that they all behave similarly in CLIR applications, and the choice of MT system is often made on the basis of convenience. We present a set of experiments which compare the impact of applying two of the best known online systems, Google and Bing translation, for query translation across multiple language pairs and for two very different CLIR tasks. Our experiments show that the MT systems perform differently on average for different tasks and language pairs, but more significantly for different individual queries. We examine the differing translation behaviour of these tools and seek to draw conclusions in terms of their suitability for use in different settings.

This paper describes a text mining approach that utilises the PyLucene search engine and the GrapeNLP grammar engine for extracting links between temperature, humidity and the spread of COVID-19, from a vast collection of scientific publications. The approach was developed in response to a Kaggle challenge from a consortium of research groups to develop text and data mining techniques that can assist the medical community in finding answers to a series of important questions on COVID-19. For this challenge, a large corpus of scientific publications known as the COVID-19 Open Research Dataset (CORD-19) was provided by the consortium. The approach presented in this paper was winner of the competition task of extracting key insights and building summary tables of COVID-19 relevant factors such as temperature and humidity.