In Search for the Right Measure: Assessing Types of Developed Knowledge While Using a Gamified Web Toolkit

Game-based learning has been used to teach topics in diverse domains, but it is still hard to determine when such approaches are an efficient learning technique. In this paper we focus on one open challenge – the limited understanding in the community of the types of knowledge these games help to develop. Using a taxonomy that distinguishes between declarative, procedural and conditional knowledge, we evaluate a game-based toolkit to analyse and solve an information security problem within a holistic crime prevention framework. Twenty-eight participants used the toolkit. We designed a portfolio of learning assessment measures to capture learning of different types of knowledge. The measures included two theoretical open-answer questions to explore participants' understanding, three problem-specific open-answer questions to test their ability to apply the framework, and 9 multiple-choice questions to test their ability to transfer what was learned to other contexts. The assessment measures were administered before and after use of the tookit. The application questions were analysed by classifying suggested ideas. The theoretical questions were qualitatively analysed using a set of analytical techniques. The transferability questions were statistically analysed using ttests. Our results show that participants' answers to the application questions improved in quality after the use of the toolkit. In their answers to the theoretical questions most participants could explain the key features of the toolkit. Statistical analysis of the multiple-choice questions testing transferability however failed to demonstrate significant improvement. Whilist our participants understood the CCO framework and learned how to use the toolkit, participants didn't demonstrate transfer of knowledge to other situations in information security. We discuss our results, limitations of the study design and possible lessons to be learned from these

"Show this thread": policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic

Crisis and disruption are often unpredictable and can create opportunities for crime. During such times, policing may also need to meet additional challenges to handle the disruption. The use of social media by officials can be essential for crisis mitigation and crime reduction. In this paper, we study the use of Twitter for crime mitigation and reduction by UK police (and associated) agencies in the early stages of the Covid-19 pandemic. Our findings suggest that whilst most of the tweets from our sample concerned issues that were not specifically about crime, especially during the first stages of the pandemic, there was a significant increase in tweets about fraud, cybercrime and domestic abuse. There was also an increase in retweeting activity as opposed to the creation of original messages. Moreover, in terms of the impact of tweets, as measured by the rate at which they are retweeted, followers were more likely to ‘spread the word’ when the tweet was content-rich (discussed a crime specific matter and contained media), and account holders were themselves more active on Twitter. Considering the changing world we live in, criminal opportunity is likely to evolve. To help mitigate this, policy makers and researchers should consider more systematic approaches to developing social media communication strategies for the purpose of crime mitigation and reduction during disruption and change more generally. We suggest a framework for so doing

A ‘criminal personas’ approach to countering criminal creativity

This paper describes a pilot study of a ‘criminal personas’ approach to countering criminal creativity. The value of the personas approach has been assessed by comparing the identification of criminal opportunity, through ‘traditional’ brainstorming and then through ‘criminal personas’ brainstorming The method involved brainstorm sessions with Computer Forensics Practitioners and with Product Designers, where they were required to generate criminal scenarios, select the most serious criminal opportunities, and propose means of countering them. The findings indicated that there was merit in further research in the development and application of the ‘criminal personas’ approach. The generation of criminal opportunity ideas and proposal of counter criminal solutions were both greater when the brainstorm approach involved the group responding through their given criminal personas

Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M (x, theta) denote the time evolution model, where x and theta are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that theta is recovered when the tuning process is initialised with perturbed model parameters theta' and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in a semi-realistic test setting to obtain reliable results and thus build confidence on the tuning in fully realistic cases. A large set of convergence tests is carried in semi-realistic cases by applying two different ensemble-based parameter estimation methods and the atmospheric forecast model of the Integrated Forecasting System (OpenIFS) model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.Peer reviewe

Detecting prolonged sitting bouts with the ActiGraph GT3X

The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data. The activPAL, a highly valid posture classification device, served as reference criterion. Both sensors were worn by 38 office workers over a median duration of 9 days. An automated feature selection extracted the relevant signal information for a minute-based posture classification. The machine learning algorithm with optimal feature number to predict the time in prolonged sitting bouts (>= 5 and >= 10 minutes) was searched and compared to the activPAL using Bland-Altman statistics. The comparison included optimized and frequently used cut-points (100 and 150 counts per minute (cpm), with and without low-frequency-extension (LFE) filtering). The new algorithm predicted the time in prolonged sitting bouts most accurate (bias <= 7 minutes/d). Of all proprietary ActiGraph methods, only 150 cpm without LFE predicted the time in prolonged sitting bouts non-significantly different from the activPAL (bias <= 18 minutes/d). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias <= 7 minutes/d). To study the health effects of ActiGraph measured prolonged sitting, we recommend using the new algorithm. In case a cut-point is used, we recommend 150 cpm without LFE to measure prolonged sitting and 100 cpm with LFE to measure total sitting time. However, both cpm cut-points are not recommended for a detailed bout analysis.NoneAccepte

Transcriptome sequencing of black grouse (Tetrao tetrix) for immune gene discovery and microsatellite development

The black grouse (Tetrao tetrix) is a galliform bird species that is important for both ecological studies and conservation genetics. Here, we report the sequencing of the spleen transcriptome of black grouse using 454 GS FLX Titanium sequencing. We performed a large-scale gene discovery analysis with a focus on genes that might be related to fitness in this species and also identified a large set of microsatellites. In total, we obtained 182 179 quality-filtered sequencing reads that we assembled into 9035 contigs. Using these contigs and 15 794 length-filtered (greater than 200 bp) singletons, we identified 7762 transcripts that appear to be homologues of chicken genes. A specific BLAST search with an emphasis on immune genes found 308 homologous chicken genes that have immune function, including ten major histocompatibility complex-related genes located on chicken chromosome 16. We also identified 1300 expressed sequence tag microsatellites and were able to design suitable flanking primers for 526 of these. A preliminary test of the polymorphism of the microsatellites found 10 polymorphic microsatellites of the 102 tested. Genomic resources generated in this study should greatly benefit future ecological, evolutionary and conservation genetic studies on this species