David Corney

Sensing Trending Topics in Twitter

Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.

Review: Plant species identification using digital morphometrics: A review

Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The growing interest in biodiversity and the increasing availability of digital images combine to make this topic timely. The global shortage of expert taxonomists further increases the demand for software tools that can recognize and characterize plants from images. A robust automated species identification system would allow people with only limited botanical training and expertise to carry out valuable field work. We review the main computational, morphometric and image processing methods that have been used in recent years to analyze images of plants, introducing readers to relevant botanical concepts along the way. We discuss the measurement of leaf outlines, flower shape, vein structures and leaf textures, and describe a wide range of analytical methods in use. We also discuss a number of systems that apply this research, including prototypes of hand-held digital field guides and various robotic systems used in agriculture. We conclude with a discussion of ongoing work and outstanding problems in the area.

Identifying and Verifying News through Social Media: Developing a user-centred tool for professional journalists

Identifying and verifying new information quickly are key issues for journalists who use social media. This article examines what tools journalists think they need to cope with the growing volume and complexity of news on social media, and what improvements are needed in existing systems. It gives some initial results from a major European Union research project (Social Sensor), involving computer scientists, journalists, and media researchers, that is designing a new tool to search across social media for news stories, to surface trends, and to help with verification. Preliminary results suggest that an effective tool should focus on the role of key influencers, and should be customisable to suit the particular needs of individual journalists and news organisations.

Designing Food with Bayesian Belief Networks

The food industry is highly competitive, and in order to survive, manufacturers must constantly innovate and match the ever changing tastes of consumers. A recent survey [1] found that 90% of the 13,000 new food products launched each year in the US fail within one year. Food companies are therefore changing the way new products are developed and launched, and this includes the use of intelligent computer systems. This paper provides an overview of one particular technique, namely Bayesian Belief networks, and its application to a typical food design problem. The characteristics of an “ideal” product are derived from a small data set.

The brightness of colour

Background The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this ‘illusion’ to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies. Results Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI. Conclusions The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond.

Food bytes: intelligent systems in the food industry

Computers have transformed the design of everything from cars to coffee cups. Now the food industry faces the same revolution, with intelligent computer models being used in the design, production and marketing of food products. The combined market capitalisation of the world’s biggest food, cosmetics, tobacco, clothing and consumer electronics companies is

Automating Digital Leaf Measurement: The Tooth, the Whole Tooth, and Nothing but the Tooth

2 trillion, forming the world’s 500 richest companies. Many of these “fast‐moving consumer goods” companies now apply intelligent computer models to the design, production and marketing of their products. Manufacturers aim to develop and produce high volumes of these commodities with minimum costs, maximum consumer appeal, and of course, maximum profits. Products have limited lifetimes following the fashions of the consumer‐driven marketplace. With food and drink, little is known about many of the underlying characteristics and processes. Product development and marketing must therefore be rapid, flexible and use raw data alongside existing expert knowledge. Intelligent systems, such as neural networks, fuzzy logic and genetic algorithms, mimic human skills such as the ability to learn from incomplete information, to adapt to changing circumstances, to explain their decisions and to cope with novel situations. These systems are being used to tackle a growing range of problems, from credit card fraud detection and stock market prediction to medical diagnosis and weather forecasting. This paper introduces intelligent systems and highlights their use in all aspects of the food and drink industry, from ingredient selection, through product design and manufacture, to packaging design and marketing.

Mining Newsworthy Topics from Social Media

Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships. The required analysis of leaves typically involves considerable manual effort, which in practice limits the number of leaves that are analyzed, potentially reducing the power of the results. In this work, we describe a novel algorithm to automate the marginal tooth analysis of leaves found in digital images. We demonstrate our methods on a large set of images of whole herbarium specimens collected from Tilia trees (also known as lime, linden or basswood). We chose the genus Tilia as its constituent species have toothed leaves of varied size and shape. In a previous study we extracted leaves automatically from a set of images. Our new algorithm locates teeth on the margins of such leaves and extracts features such as each tooth’s area, perimeter and internal angles, as well as counting them. We evaluate an implementation of our algorithm’s performance against a manually analyzed subset of the images. We found that the algorithm achieves an accuracy of 85% for counting teeth and 75% for estimating tooth area. We also demonstrate that the automatically extracted features are sufficient to identify different species of Tilia using a simple linear discriminant analysis, and that the features relating to teeth are the most useful.

Automated plant identification using artificial neural networks

Newsworthy stories are increasingly being shared through social networking platforms such as Twitter and Reddit, and journalists now use them to rapidly discover stories and eye-witness accounts. We present a technique that detects “bursts” of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic tf-idf approach and group together bursty phrases that often appear in the same messages in order to identify emerging topics. We demonstrate our methods by analysing tweets corresponding to events drawn from the worlds of politics and sport, as well as more general mainstream news. We created a user-centred “ground truth” to evaluate our methods, based on mainstream media accounts of the events. This helps ensure our methods remain practical. We compare several clustering and topic ranking methods to discover the characteristics of news-related collections, and show that different strategies are needed to detect emerging topics within them. We show that our methods successfully detect a range of different topics for each event and can retrieve messages (for example, tweets) that represent each topic for the user.

A Logical Framework for Template Creation and Information Extraction

This paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to act as a tool to help identify plants using morphological characters collected automatically from images of botanical herbarium specimens. A methodology is presented here to provide a practical way for taxonomists to use neural networks as automated identification tools, by collating results from a population of neural networks. A case study is provided using data extracted from specimens of the genus Tilia in the Herbarium of the Royal Botanic Gardens, Kew, UK. A classification accuracy of 44% was achieved on this challenging multiclass problem.