The transformation of Sweden from a haven for online pirates to the hope of the digital music industry [Musikbranschens föregångsland]

1974 was the year when the Swedish pop group ABBA won the Eurovision Song Contest in Brighton and when Blue Swede reached number one on the Billboard Hot 100 in the US. Although Swedish pop music gained some international success even prior to 1974, this year is often considered as the beginning of an era in which Swedish pop music had great success around the world. With brands such as ABBA, Europe, Roxette, The Cardigans, Ace of Base, In Flames, Robyn, Avicii, Swedish House Mafia and music producers Stig Andersson, Ola Håkansson, Dag Volle, Max Martin, Andreas Carlsson, Jorgen Elofsson and several others have the myth of the Swedish music miracle kept alive for nearly more than four decades. Swedish music looks to continue reap success around the world, but since the millennium, Sweden's relationship with music has been more focused on relatively controversial Internet-based services for music distribution developed by Swedish entrepreneurs and engineers rather than on successful musicians and composers. This chapter focusses on the music industry in Sweden. The chapter will discuss the development of the Internet services mentioned above and their impact on the production, distribution and consumption of recorded music. Ample space will be given in particular to Spotify, the music service that quickly has fundamentally changed the music industry in Sweden. The chapter will also present how the music industry's three sectors - recorded music, music licensing and live music - interact and evolve in Sweden.

Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Cell image classification using histograms, higher order statistics and adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Social Media Analytics : Ein interdisziplinärer Ansatz und seine Implikationen für die Wirtschaftsinformatik [Social Media Analytics - An Interdisciplinary Approach and Its Implications for Information Systems]

Social Media Analytics ist ein neuer Forschungsbereich, in dem interdisziplinäre Methoden kombiniert, erweitert und angepasst werden, um Social-Media-Daten auszuwerten. Neben der Beantwortung von Forschungsfragen ist es ebenfalls ein Ziel, Architekturentwürfe für die Entwicklung neuer Informationssysteme und Anwendungen bereitzustellen, die auf sozialen Medien basieren. Der Beitrag stellt die wichtigsten Aspekte des Bereichs Social Media Analytics vor und verweist auf die Notwendigkeit einer fächerübergreifenden Forschungsagenda, für deren Erstellung und Bearbeitung der Wirtschaftsinformatik eine wichtige Rolle zukommt.

Social media analytics: an interdisciplinary approach and its implications for information systems

Social Media Analytics is an emerging interdisciplinary research field that aims on combining, extending, and adapting methods for analysis of social media data. On the one hand it can support IS and other research disciplines to answer their research questions and on the other hand it helps to provide architectural designs as well as solution frameworks for new social media-based applications and information systems. The authors suggest that IS should contribute to this field and help to develop and process an interdisciplinary research agenda.

Identity, addressing, authenticity and audit requirements for trust in ERP, analytics and big/open data in a “Cloud” computing environment : a review and proposal

Enterprises, both public and private, have rapidly commenced using the benefits of enterprise resource planning (ERP) combined with business analytics and “open data sets” which are often outside the control of the enterprise to gain further efficiencies, build new service operations and increase business activity. In many cases, these business activities are based around relevant software systems hosted in a “cloud computing” environment. “Garbage in, garbage out”, or “GIGO”, is a term long used to describe problems in unqualified dependency on information systems, dating from the 1960s. However, a more pertinent variation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems, such as ERP and usage of open datasets in a cloud environment, the ability to verify the authenticity of those data sets used may be almost impossible, resulting in dependence upon questionable results. Illicit data set “impersonation” becomes a reality. At the same time the ability to audit such results may be an important requirement, particularly in the public sector. This paper discusses the need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment and analyses some current technologies that are offered and which may be appropriate. However, severe limitations to addressing these requirements have been identified and the paper proposes further research work in the area.

Google Glass as a technique of self and the revitalisation of the monad

As the boundaries between public and private, human and technology, digital and social, mediated and natural, online and offline become increasingly blurred in modern techno-social hybrid societies, sociology as a discipline needs to adapt and adopt new ways of accounting for these digital cultures. In this paper I use the social networking site Pinterest to demonstrate how people today are shaped by, and in turn shape, the digital tools they are assembled with. Digital sociology is emerging as a sociological subdiscipline that engages with the convergence of the digital and the social. However, there seems to be a focus on developing new methods for studying digital social life, yet a neglect of concrete explorations of its culture. I argue for the need for critical socio-cultural ‘thick description’ to account for the interrelations between humans and technologies in modern digitally mediated cultures.

Measuring Online user engagement : the limitations of Web analytics

The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this chapter outlines an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry.

Bluetooth vehicle trajectory by fusing Bluetooth and loops : motorway travel time statistics

Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth MAC Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveller. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways loops are closely spaced, whereas BMS are placed few kilometres apart. In this research, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilised to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific motorway, Brisbane. Comparing the model with the linear interpolation based trajectory provides significant improvements.

Twitter data analytics – or : the pleasures and perils of studying Twitter

It might still sound strange to dedicate an entire journal issue exclusively to a single internet platform. But it is not the company Twitter Inc. that draws our attention; this issue is not about a platform and its features and services. It is about its users and the ways in which they interact with one another via the platform, about the situations that motivate people to share their thoughts publicly, using Twitter as a means to reach out to one another. And it is about the digital traces people leave behind when interacting with Twitter, and most of all about the ways in which these traces – as a new type of research data – can also enable new types of research questions and insights.

Participatory Data Analytics: Collaborative interfaces for data composition and visualisation

This research proposes the development of interfaces to support collaborative, community-driven inquiry into data, which we refer to as Participatory Data Analytics. Since the investigation is led by local communities, it is not possible to anticipate which data will be relevant and what questions are going to be asked. Therefore, users have to be able to construct and tailor visualisations to their own needs. The poster presents early work towards defining a suitable compositional model, which will allow users to mix, match, and manipulate data sets to obtain visual representations with little-to-no programming knowledge. Following a user-centred design process, we are subsequently planning to identify appropriate interaction techniques and metaphors for generating such visual specifications on wall-sized, multi-touch displays.

Understanding the quantitative skill base on introductory statistics : a case study from business statistics

Basic mathematical skills are critical to a student’s ability to successfully undertake an introductory statistics course. Yet in business education this vitally important area of mathematics and statistics education is under-researched. The question therefore arises as to what level of mathematical skill a typical business studies student will possess as they enter the tertiary environment, and whether there are any common deficiencies that we can identify with a view to tackling the problem. This paper will focus on a study designed to measure the level of mathematical ability of first year business students. The results provide timely insight into a growing problem faced by many tertiary educators in this field.

When does Google hand over your data to governments?

Governments around the world want to know a lot about who we are and what we’re doing online and they want communications companies to help them find it. We don’t know a lot about when companies hand over this data, but we do know that it’s becoming increasingly common.