Fluidity is a key quality of human-human and more natural human-agent interaction (HAI). The concept of fluidity is difficult to define formally, however, interaction partners and users perceive the difference between more and less fluid interaction. As an initial informal definition, fluidity in interaction can be considered the abilities to seamlessly transition in turn-taking, to allow appropriate overlap of turns between agents, including multimodally, and to allow action using prediction. The purpose of this workshop is to bring agent designers together to attempt to define fluidity in interaction more precisely and propose ways in which we can make HAI more fluid.

Achieving truly fluid interaction with robots with speech interfaces remains a hard problem. Despite technical advances in sensors, processors and actuators, the experience of Human-Robot Interaction (HRI) remains laboured and frustrating. Some of the barriers to this stem from a lack of a suitable development platform for HRI to improve the interaction, particularly for mobile manipulator robots. In this paper we briefly overview some existing systems and propose a high-fidelity Virtual Reality (VR) HRI simulation environment with Wizard-of-Oz (WoZ) cabability applicable to multiple robots including mobile manipulators and social robots.

Abstract The study proposes a novel time‐frequency analysis technique, titled time‐frequency synchroextracting transform which enhances the accuracy of synchroextracting transform. The synchroextracting transform is a powerful postprocessing method that sharpens the energy distribution of spectrogram. However, its limitation is the sole use of time or frequency factors that cannot accurately portray signals containing both ‘slow‐varying’ and ‘fast‐changing’ components. The proposed method applies the synchroextracting procedure in both time and frequency dimensions to improve the energy concentration. A fast Fourier transform‐‐based algorithm is also provided for efficient implementation. The proposed method is validated via numerical and empirical data. Compared with advanced methods including synchrosqueezing transform and synchroextracting transform, the proposed method is suitable to analyse wider range of signals and has better noise robustness than the original synchroextracting transform.

A global land data assimilation system (LDAS-Monde) was developed by CNRM. It uses a version of the interactions between soil, biosphere, and atmosphere (ISBA) land surface model able to simulate photosynthesis and plant growth. Vegetation variables such as leaf area index (LAI) and surface soil moisture can be jointly assimilated in the model. Sequential assimilation of LAI is possible thanks to the fully photosynthesis-driven phenology. The simulated LAI is flexible and can be analyzed at a given date. Also, the assimilation of LAI alone can be used to analyze the root-zone soil moisture. The assimilation of level 1 microwave observations is being investigated. Recent results and potential applications of LDAS-Monde are presented.

Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things. Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on hypertext induced topic search improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.

We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

We present a new data structure for the representation of an integrated circuit layout. It is a modified HV/VH tree using arrays as the primary container in bisector lists and leaf nodes. By grouping and sorting objects within these arrays together with a customized binary search algorithm, our new data structure provides excellent performance in both memory usage and region query speed. Experimental results show that in comparison with the original HV/VH tree, which has been regarded as the best layout data structure to date, the new data structure uses much less memory and can become 30% faster on region query.

This paper is to develop an ontology-based mobile environment for collaborative product design and manufacture, which enables geographically dispersed team members to collaborate over the internet within the total design or manufacturing process, including product design specification, conceptual design, detail design, manufacture and recycling. In this paper, a database which based on a new method named ontology is built up to demonstrate the all related information in order to achieve an efficient and convenient management. According these two achievements, a new data management system is built and tested in the mobile device and compared to the traditional data management system at the end of this paper.

As part of two EU-Asia projects, Asia IT&C and Asia-Link, the research in Web-enable collaboration for design and manufacture has been carried out. The major Web/Internet techniques involved in the projects include a Web-enable environment for collaboration, online collaborative CAD/CAM, remote execution of large size programs (RELSP), and distributed product design. Two related techniques are presented in details: (1) a client-server approach for RELSP, where the Internet techniques involved include CORBA, Microsoft's Internet information server, Tomcat server, JDBC and ODBC; (2) Web-services supported collaborative CAD which enables geographically dispersed designers jointly conduct a design task in the way of speaking and seeing each other and instantaneously modifying the CAD drawing online.