Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Image processing for the extraction of nutritional information from food labels

Current techniques for tracking nutritional data require undesirable amounts of either time or man-power. People must choose between tediously recording and updating dietary information or depending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution involves a C++ library that combines image pre-processing, optical character recognition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label\u27s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

On the importance of metrics in practical applications

[EN] Students motivation for learning mathematical concepts can be increased when showing the usefulness of these concepts in practical problems. One important mathematical concept is the concept of metric space and, more related to the applications, the concept of metric function. In this work we aim to illustrate how important is to appropriately choose the metric when dealing with a practical problem. In particular, we focus on the problem of detection of noisy pixels in colour images. In this context, it is very important to appropriately measure the distances and similarities between the image pixels, which is done by means of an appropriate metric. We study the performance of different metrics, including recent fuzzy metrics, within a specific filter to show that it is indeed a critical choice to appropriately solve the task.Camarena, J.; Morillas, S.; Cisneros, F. (2011). On the importance of metrics in practical applications. Modelling in Science Education and Learning. 4:119-128. doi:10.4995/msel.2011.3066SWORD119128