Security information sharing via Twitter: 'Heartbleed' as a case study

The current paper outlines an exploratory case study in which we examined the extent to which specific communities of Twitter users engaged with the debate about the security threat known as ‘Heartbleed’ in the first few days after this threat was exposed. The case study explored which professional groups appeared to lead the debate about Heartbleed, the nature of the communication (tweets and retweets), and evidence about behaviour change. Using keywords from the Twitter user profiles, six occupational groups were identified, each of which were likely to have a direct interest in learning about Heartbleed (including legal, financial, entrepreneurial, press, and IT professionals). The groups participated to different degrees in the debate about Heartbleed. This exploratory case study provides an insight into information sharing, potential communities of influence, and points for future research in the absence of a voice of authority in the field of cybersecurity

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Mobilizing the Past for a Digital Future : The Potential of Digital Archaeology

Mobilizing the Past is a collection of 20 articles that explore the use and impact of mobile digital technology in archaeological field practice. The detailed case studies present in this volume range from drones in the Andes to iPads at Pompeii, digital workflows in the American Southwest, and examples of how bespoke, DIY, and commercial software provide solutions and craft novel challenges for field archaeologists. The range of projects and contexts ensures that Mobilizing the Past for a Digital Future is far more than a state-of-the-field manual or technical handbook. Instead, the contributors embrace the growing spirit of critique present in digital archaeology. This critical edge, backed by real projects, systems, and experiences, gives the book lasting value as both a glimpse into present practices as well as the anxieties and enthusiasm associated with the most recent generation of mobile digital tools. This book emerged from a workshop funded by the National Endowment for the Humanities held in 2015 at Wentworth Institute of Technology in Boston. The workshop brought together over 20 leading practitioners of digital archaeology in the U.S. for a weekend of conversation. The papers in this volume reflect the discussions at this workshop with significant additional content. Starting with an expansive introduction and concluding with a series of reflective papers, this volume illustrates how tablets, connectivity, sophisticated software, and powerful computers have transformed field practices and offer potential for a radically transformed discipline.https://dc.uwm.edu/arthist_mobilizingthepast/1000/thumbnail.jp