An Investigation of the Utility of Microblogging in a Virtual Organisation

Virtualisation is one possible business strategy of an organisation. The nature of virtual organisations is that individuals or teams are distributed over different work sites. This leads to barriers in communication,coordination and collaboration between these entities due to dispersed expertise, time zones, languages, cultures, etc. To address these issues, virtual organisations have invested in ICT for supporting collaboration between cross-site colleagues. One very new collaborative technology is microblogging. Microblogging supports asynchronous communication between multiple persons. Microblogging is based upon transmission of short messages that can be sent from Web-based microblogging systems, instant messaging tools, email or mobile phones. Microblogging has some relevant features like simplicity, immediacy, accessibility and presence. This paper describes our investigation of the utility of microblogging, particularly the Twitter tool, for collaboration support in a virtual organisation. Since microblogging is very new and was introduced only recently, no work has been done on this exact topic. The investigation involved conducting an online survey to collect participants’ opinions about the utility of Twitter in the workplace after using Twitter over a three-week period. The study yielded quantitative and qualitative results regarding participants’ experience of Twitter. It was found that microblogging could be adapted to virtual organisations quickly due to ease of use in terms of taking less time and effort for creating microblogs. Twitter could be used in virtual organisations for collaboration support because it is believed that the use of Twitter could somewhat improve communication between cross-site co-workers. However, to be well accepted by virtual organisations, Twitter needs improvement and addition to its existing functionality

Book reviews online

As the number of new academic books published each year continues to rise, such that it becomes evermore difficult to keep abreast of them in one's discipline, the book‐review procedure takes on an increasing importance. This paper outlines the design and development of an automated system for handling book reviews. Descriptions are given of some prototypes that have been developed for use on an intranet server and/or the Internet. These systems, based on SGML and HTML, are briefly discussed and compared

The Calibration and Data Products of the Galaxy Evolution Explorer

We describe the calibration status and data products pertaining to the GR2 and GR3 data releases of the Galaxy Evolution Explorer (GALEX). These releases have identical pipeline calibrations that are significantly improved over the GR1 data release. GALEX continues to survey the sky in the Far Ultraviolet (FUV, ~154 nm) and Near Ultraviolet (NUV, ~232 nm) bands, providing simultaneous imaging with a pair of photon counting, microchannel plate, delay line readout detectors. These 1.25 degree field-of-view detectors are well-suited to ultraviolet observations because of their excellent red rejection and negligible background. A dithered mode of observing and photon list output pose complex requirements on the data processing pipeline, entangling detector calibrations and aspect reconstruction algorithms. Recent improvements have achieved photometric repeatability of 0.05 and 0.03 mAB in the FUV and NUV, respectively. We have detected a long term drift of order 1% FUV and 6% NUV over the mission. Astrometric precision is of order 0.5" RMS in both bands. In this paper we provide the GALEX user with a broad overview of the calibration issues likely to be confronted in the current release. Improvements are likely as the GALEX mission continues into an extended phase with a healthy instrument, no consumables, and increased opportunities for guest investigations.Comment: Accepted to the ApJS (a special GALEX issue

The analysis of facial beauty: an emerging area of research in pattern analysis

Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical application

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

We have witnessed an increase in the availability of data from diverse sources over the past few years. Cloud computing, big data and Internet-of-Things (IoT) are distinctive cases of such an increase which demand novel approaches for data analytics in order to process and analyze huge volumes of data for security and business use. Cloud computing has been becoming popular for critical structure IT mainly due to cost savings and dynamic scalability. Current offerings, however, are not mature enough with respect to stringent security and resilience requirements. Mechanisms such as anomaly detection hybrid systems are required in order to protect against various challenges that include network based attacks, performance issues and operational anomalies. Such hybrid AI systems include Neural Networks, blackboard systems, belief (Bayesian) networks, case-based reasoning and rule-based systems and can be implemented in a variety of ways. Traffic in the cloud comes from multiple heterogeneous domains and changes rapidly due to the variety of operational characteristics of the tenants using the cloud and the elasticity of the provided services. The underlying detection mechanisms rely upon measurements drawn from multiple sources. However, the characteristics of the distribution of measurements within specific subspaces might be unknown. We argue in this paper that there is a need to cluster the observed data during normal network operation into multiple subspaces each one of them featuring specific local attributes, i.e. granules of information. Clustering is implemented by the inference engine of a model hybrid NN system. Several variations of the so-called value-difference metric (VDM) are investigated like local histograms and the Canberra distance for scalar attributes, the Jaccard distance for binary word attributes, rough sets as well as local histograms over an aggregate ordering distance and the Canberra measure for vectorial attributes. Low-dimensional subspace representations of each group of points (measurements) in the context of anomaly detection in critical cloud implementations is based upon VD metrics and can be either parametric or non-parametric. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate ordered sets of objects featuring VD metrics (as obtained from distributed network measurements) is proposed. Each node of the SOFM stands for a structured local distribution of such objects within the input space. The so-called Neighborhood-based Outlier Factor (NOOF) is defined for such reduced/aggregate ordered sets of objects as a value-difference metric of histogrammes. Measurements that do not belong to local distributions are detected as anomalies, i.e. outliers of the trained SOFM. Several methods of subspace clustering using Expectation-Maximization Gaussian Mixture Models (a parametric approach) as well as local data densities (a non-parametric approach) are outlined and compared against the proposed method using data that are obtained from our cloud testbed in emulated anomalous traffic conditions. The results—which are obtained from a model NN system—indicate that the proposed method performs well in comparison with conventional techniques