miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Handbook of research on user interface design and evaluation for mobile technology

In recent years, mobile technology has been one of the major growth areas in computing. Designing the user interface for mobile applications, however, is a very complex undertaking which is made even more challenging by the rapid technological developments in mobile hardware. Mobile human-computer interaction, unlike desktop-based interaction, must be cognizant of a variety of complex contextual factors affecting both users and technology. The Handbook of Research on User Interface Design and Evaluation provides students, researchers, educators, and practitioners with a compendium of research on the key issues surrounding the design and evaluation of mobile user interfaces, such as the physical environment and social context in which a mobile device is being used and the impact of multitasking behavior typically exhibited by mobile-device users. Compiling the expertise of over 150 leading experts from 26 countries, this exemplary reference tool will make an indispensable addition to every library collection.

GEO Label:user and producer perspectives on a label for geospatial data

One of the aims of the Science and Technology Committee (STC) of the Group on Earth Observations (GEO) was to establish a GEO Label- a label to certify geospatial datasets and their quality. As proposed, the GEO Label will be used as a value indicator for geospatial data and datasets accessible through the Global Earth Observation System of Systems (GEOSS). It is suggested that the development of such a label will significantly improve user recognition of the quality of geospatial datasets and that its use will help promote trust in datasets that carry the established GEO Label. Furthermore, the GEO Label is seen as an incentive to data providers. At the moment GEOSS contains a large amount of data and is constantly growing. Taking this into account, a GEO Label could assist in searching by providing users with visual cues of dataset quality and possibly relevance; a GEO Label could effectively stand as a decision support mechanism for dataset selection. Currently our project - GeoViQua, - together with EGIDA and ID-03 is undertaking research to define and evaluate the concept of a GEO Label. The development and evaluation process will be carried out in three phases. In phase I we have conducted an online survey (GEO Label Questionnaire) to identify the initial user and producer views on a GEO Label or its potential role. In phase II we will conduct a further study presenting some GEO Label examples that will be based on Phase I. We will elicit feedback on these examples under controlled conditions. In phase III we will create physical prototypes which will be used in a human subject study. The most successful prototypes will then be put forward as potential GEO Label options. At the moment we are in phase I, where we developed an online questionnaire to collect the initial GEO Label requirements and to identify the role that a GEO Label should serve from the user and producer standpoint. The GEO Label Questionnaire consists of generic questions to identify whether users and producers believe a GEO Label is relevant to geospatial data; whether they want a single "one-for-all" label or separate labels that will serve a particular role; the function that would be most relevant for a GEO Label to carry; and the functionality that users and producers would like to see from common rating and review systems they use. To distribute the questionnaire, relevant user and expert groups were contacted at meetings or by email. At this stage we successfully collected over 80 valid responses from geospatial data users and producers. This communication will provide a comprehensive analysis of the survey results, indicating to what extent the users surveyed in Phase I value a GEO Label, and suggesting in what directions a GEO Label may develop. Potential GEO Label examples based on the results of the survey will be presented for use in Phase II.

Teallach - a flexible user - interface development environment for object database applications

The Teallach project has adapted model-based user-interface development techniques to the systematic creation of user-interfaces for object-oriented database applications. Model-based approaches aim to provide designers with a more principled approach to user-interface development using a variety of underlying models, and tools which manipulate these models. Here we present the results of the Teallach project, describing the tools developed and the flexible design method supported. Distinctive features of the Teallach system include provision of database-specific constructs, comprehensive facilities for relating the different models, and support for a flexible design method in which models can be constructed and related by designers in different orders and in different ways, to suit their particular design rationales. The system then creates the desired user-interface as an independent, fully functional Java application, with automatically generated help facilities.

Comparing interaction techniques for serious games through brain-computer interfaces:a user perception evaluation study

This paper examines the application of commercial and non-invasive electroencephalography (EEG)-based brain-computer (BCIs) interfaces with serious games. Two different EEG-based BCI devices were used to fully control the same serious game. The first device (NeuroSky MindSet) uses only a single dry electrode and requires no calibration. The second device (Emotiv EPOC) uses 14 wet sensors requiring additional training of a classifier. User testing was performed on both devices with sixty-two participants measuring the player experience as well as key aspects of serious games, primarily learnability, satisfaction, performance and effort. Recorded feedback indicates that the current state of BCIs can be used in the future as alternative game interfaces after familiarisation and in some cases calibration. Comparative analysis showed significant differences between the two devices. The first device provides more satisfaction to the players whereas the second device is more effective in terms of adaptation and interaction with the serious game.

Client QoE-oriented segment selection for DASH

A segment selection method controlled by Quality of Experience (QoE) factors for Dynamic Adaptive Streaming over HTTP (DASH) is presented in this paper. Current rate adaption algorithms aim to eliminate buffer underrun events by significantly reducing the code rate when experiencing pauses in replay. In reality, however, viewers may choose to accept a level of buffer underrun in order to achieve an improved level of picture fidelity or to accept the degradation in picture fidelity in order to maintain the service continuity. The proposed rate adaption scheme in our work can maximize the user QoE in terms of both continuity and fidelity (picture quality) in DASH applications. It is shown that using this scheme a high level of quality for streaming services, especially at low packet loss rates, can be achieved. Our scheme can also maintain a best trade-off between continuity-based quality and fidelity-based quality, by determining proper threshold values for the level of quality intended by clients with different quality requirements. In addition, the integration of the rate adaptation mechanism with the scheduling process is investigated in the context of a mobile communication network and related performances are analyzed.

Benchmarking of distributed computing engines spark and GraphLab for big data analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.