VOEvent Standard for Fast Radio Bursts

Fast radio bursts are a new class of transient radio phenomena currently detected as millisecond radio pulses with very high dispersion measures. As new radio surveys begin searching for FRBs a large population is expected to be detected in real-time, triggering a range of multi-wavelength and multi-messenger telescopes to search for repeating bursts and/or associated emission. Here we propose a method for disseminating FRB triggers using Virtual Observatory Events (VOEvents). This format was developed and is used successfully for transient alerts across the electromagnetic spectrum and for multi-messenger signals such as gravitational waves. In this paper we outline a proposed VOEvent standard for FRBs that includes the essential parameters of the event and where these parameters should be specified within the structure of the event. An additional advantage to the use of VOEvents for FRBs is that the events can automatically be ingested into the FRB Catalogue (FRBCAT) enabling real-time updates for public use. We welcome feedback from the community on the proposed standard outlined below and encourage those interested to join the nascent working group forming around this topic.Comment: 11 pages, 2 figures, parameter definition table in appendi

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps

Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History-based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. Thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision-making process is capable of reducing the growth of users' privacy loss by 70% in a Google Drive-based network and by 40% in an author collaboration network. This is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201

Crime scene examiners and volume crime investigations: an empirical study of perception and practice

Most police forces in the UK employ specially trained crime scene examiners (CSEs) to provide forensic science support to the investigation of crime. Previous research has shown wide variations in the management, deployment, and performance of this staff group. There is also evidence that informal elements of professional and organisational culture, in particular the role characterisations of crime scene examiners, also have a bearing on their effective use in the investigation of high volume property crime. These issues are explored as part of a more extensive study of forensic science provision in the two largest police forces in Scotland and by the four main Scottish Police Services Authority Forensic Services (SPSA FS) units. A range of staff in these organisations described their understandings of the role of crime scene examiners – as evidence collectors, forensic investigators, specialist advisers, or any combination of these. Whilst two thirds (62%) of respondents recognised the complexity and scope of the role of CSEs including its cognitive elements, a substantial minority (38%) categorised the role as having a single element – collecting evidence – and therefore perceived it as limited largely mechanical in character. The reasons for, and consequences of, this perception are considered, and the paper concludes with a challenge to reconsider this limited view of what crime scene examiners can contribute to volume crime investigations