HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1

Lesson plan: Who am I? My digital footprint

The lesson plan describes the content and pedagogical patterns used to design and deliver a digital literacy session as part of the Birkbeck Library's provision on Information and Digital literacies program. This session primarily aims to give to the learners a space for reflection around their digital identity and the trails they leave in the cyberspace. It also aims to introduce the idea of the level of control learners can have on what is on the web about them and how to deal with the uncertainty

The Math-alachian Trail

This practitioner article highlights the use of digital math trails with in-service mathematics teachers at all levels. The purpose of a digital math trail is to connect learners with a physical space while engaging them in open-ended mathematics problems that promote critical thinking and problem-solving savvy. This article also provides simple steps to help teachers learn how to create their own digital math trails for their students

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning

Risk media and the end of anonymity

Whereas threats from twentieth century 'broadcast era' media were characterised in terms of ideology and ‘effects', today the greatest risks posed by media are informational. This paper argues that digital participation as the condition for the maintenance of today's self identity and basic sociality has shaped a new principal media risk of the loss of anonymity. I identify three interrelated key features of this new risk. Firstly, basic communicational acts are archival. Secondly, there is a diminishment of the predictable 'decay time' of media. And, thirdly, both of these shape a new individual and organizational vulnerability of 'emergence' – the haunting by our digital trails. This article places these media risks in the context of the shifting nature and function of memory and the potential uses and abuses of digital pasts

Time Trails: ‘presencing’ digital heritage within our everyday lives

The Time Trails project is a collaboration between the Centre for Intermedia at the University of Exeter, Royal Albert Memorial Museum and Art Gallery, 1010 Media, and Exeter City Football Club Supporters Trust (2013). It is a mobile web app to allow users to follow, annotate and create trails using text, images and videos, and to respond to them via social media. Two trails narrating the history of Exeter City Football Club and its Supporters Trust, used for mobile learning and as part of sport and cultural tourism experiences are presented. We show how Time Trails can be used as a presencing tool to establish new ways of encountering and learning on digital heritage within our daily lives

Data Degradation: Making Private Data Less Sensitive Over Time

Trail disclosure is the leakage of privacy sensitive data, resulting from negligence, attack or abusive scrutinization or usage of personal digital trails. To prevent trail disclosure, data degradation is proposed as an alternative to the limited retention principle. Data degradation is based on the assumption that long lasting purposes can often be satisfied with a less accurate, and therefore less sensitive, version of the data. Data will be progressively degraded such that it still serves application purposes, while decreasing accuracy and thus privacy sensitivity