University of Klagenfurt

'Mode 3' allows and emphasises the co-existence and co-evolution of different knowledge and innovation paradigms: the competitiveness and superiority of a knowledge system is highly determined by its adaptive capacity to combine and integrate different knowledge and innovation modes via co-evolution, co-specialisation and co-opetition knowledge stock and flow dynamics. The 'Quadruple Helix' emphasises the importance of also integrating the perspective of the media-based and culture-based public. What results is an emerging fractal knowledge and innovation ecosystem, well-configured for the knowledge economy and society.

Human appropriation of net primary production (HANPP), the aggregate impact of land use on biomass available each year in ecosystems, is a prominent measure of the human domination of the biosphere. We present a comprehensive assessment of global HANPP based on vegetation modeling, agricultural and forestry statistics, and geographical information systems data on land use, land cover, and soil degradation that localizes human impact on ecosystems. We found an aggregate global HANPP value of 15.6 Pg C/yr or 23.8% of potential net primary productivity, of which 53% was contributed by harvest, 40% by land-use-induced productivity changes, and 7% by human-induced fires. This is a remarkable impact on the biosphere caused by just one species. We present maps quantifying human-induced changes in trophic energy flows in ecosystems that illustrate spatial patterns in the human domination of ecosystems, thus emphasizing land use as a pervasive factor of global importance. Land use transforms earth's terrestrial surface, resulting in changes in biogeochemical cycles and in the ability of ecosystems to deliver services critical to human well being. The results suggest that large-scale schemes to substitute biomass for fossil fuels should be viewed cautiously because massive additional pressures on ecosystems might result from increased biomass harvest.

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000-2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

The Triple Helix innovation model focuses on university-industry-government relations. The Quadruple Helix embeds the Triple Helix by adding as a fourth helix the ‘media-based and culture-based public’ and ‘civil society’. The Quintuple Helix innovation model is even broader and more comprehensive by contextualizing the Quadruple Helix and by additionally adding the helix (and perspective) of the ‘natural environments of society’. The Triple Helix acknowledges explicitly the importance of higher education for innovation. However, in one line of interpretation it could be argued that the Triple Helix places the emphasis on knowledge production and innovation in the economy so it is compatible with the knowledge economy. The Quadruple Helix already encourages the perspective of the knowledge society, and of knowledge democracy for knowledge production and innovation. In a Quadruple Helix understanding, the sustainable development of a knowledge economy requires a coevolution with the knowledge society. The Quintuple Helix stresses the necessary socioecological transition of society and economy in the twenty-first century; therefore, the Quintuple Helix is ecologically sensitive. Within the framework of the Quintuple Helix innovation model, the natural environments of society and the economy also should be seen as drivers for knowledge production and innovation, therefore defining opportunities for the knowledge economy. The European Commission in 2009 identified the socioecological transition as a major challenge for the future roadmap of development. The Quintuple Helix supports here the formation of a win-win situation between ecology, knowledge and innovation, creating synergies between economy, society, and democracy. Global warming represents an area of ecological concern, to which the Quintuple Helix innovation model can be applied with greater potential.

This article develops an inter-disciplinary and trans-disciplinary framework of analysis that relates knowledge, innovation and the environment (natural environments) to each other. For that purpose the five-helix structure model of the Quintuple Helix is being introduced. The Triple Helix model, designed by Etzkowitz and Leydesdorff (2000), focuses on the relations of universities, industry and governments. The Quadruple Helix (Carayannis & Campbell, 2009) blends in the perspective of a media-based and culture-based public. The Quintuple Helix finally frames knowledge and innovation in the context of the environment (natural environments). Therefore, the Quintuple Helix can be interpreted as an approach in line with sustainable development and social ecology. “Eco-innovation” and “eco-entrepreneurship” should be processed in such a broader understanding of knowledge and innovation.

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Web caching is an important technique to scale the Internet. One important performance factor of Web caches is the replacement strategy. Due to specific characteristics of the World Wide Web, there exist a huge number of proposals for cache replacement. This article proposes a classification for these proposals that subsumes prior classifications. Using this classification, different proposals and their advantages and disadvantages are described. Furthermore, the article discusses the importance of cache replacement strategies in modern proxy caches and outlines potential future research topics.

Abstract Bioenergy deployment offers significant potential for climate change mitigation, but also carries considerable risks. In this review, we bring together perspectives of various communities involved in the research and regulation of bioenergy deployment in the context of climate change mitigation: Land‐use and energy experts, land‐use and integrated assessment modelers, human geographers, ecosystem researchers, climate scientists and two different strands of life‐cycle assessment experts. We summarize technological options, outline the state‐of‐the‐art knowledge on various climate effects, provide an update on estimates of technical resource potential and comprehensively identify sustainability effects. Cellulosic feedstocks, increased end‐use efficiency, improved land carbon‐stock management and residue use, and, when fully developed, BECCS appear as the most promising options, depending on development costs, implementation, learning, and risk management. Combined heat and power, efficient biomass cookstoves and small‐scale power generation for rural areas can help to promote energy access and sustainable development, along with reduced emissions. We estimate the sustainable technical potential as up to 100 EJ : high agreement; 100–300 EJ : medium agreement; above 300 EJ : low agreement. Stabilization scenarios indicate that bioenergy may supply from 10 to 245 EJ yr −1 to global primary energy supply by 2050. Models indicate that, if technological and governance preconditions are met, large‐scale deployment (>200 EJ ), together with BECCS , could help to keep global warming below 2° degrees of preindustrial levels; but such high deployment of land‐intensive bioenergy feedstocks could also lead to detrimental climate effects, negatively impact ecosystems, biodiversity and livelihoods. The integration of bioenergy systems into agriculture and forest landscapes can improve land and water use efficiency and help address concerns about environmental impacts. We conclude that the high variability in pathways, uncertainties in technological development and ambiguity in political decision render forecasts on deployment levels and climate effects very difficult. However, uncertainty about projections should not preclude pursuing beneficial bioenergy options.

Qualinet White Paper on Definitions of Quality of Experience Output from the fifth Qualinet meeting, Novi Sad, March 12, 2013

Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. In this paper, we present KVASIR, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (experienced endoscopists). In this respect, KVASIR is important for research on both single- and multi-disease computer aided detection. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

Brand community members have a strong interest in the product and in the brand. They usually have extensive product knowledge and engage in product‐related discussions; they support each other in solving problems and generating new product ideas. Therefore, brand communities can be a valuable source of innovation. So far, little is known about the member's ability and willingness to participate in a company's innovation process. How does passion for the brand, affiliation to the brand community, and trust in the brand affect the willingness to engage in a company's innovation process? What is the effect of brand passion on brand knowledge and on domain‐specific skills, which are considered important prerequisites for qualified and creative contributions to new product development? What is the effect of personality traits on the willingness and ability to engage in new product development? This research addresses these questions, which are interesting for managers who are thinking about opening up their innovation process and collaborating with brand communities and for academics exploring the opportunities of online communities for new product development and trying to develop promising new forms of open innovation networks. Drawing on brand community literature, relationship theory, creativity theory, and personality traits research, this paper introduces a comprehensive set of antecedents affecting brand community members' willingness to engage in new product development. It is argued that consumer creativity, identification with the brand community, and brand‐specific emotions and attitudes (passion and trust) as well as brand knowledge are important determinants of consumers' willingness to share their knowledge with producers. The paper also identifies two personality traits (i.e., extraversion and openness) that have significant influence on brand passion, creativity, and identification with the community. The hypotheses are tested on a sample of 550 members of the Volkswagen Golf GTI car community. Structural equation modeling was used to test the relationship among the constructs. Though a positive disposition toward a brand may be advantageous for consumers that are willing to interact with producers during new product development, our results show that it is consumer interest in innovations and the innovative process that drives them to get involved. Further, brand community members with more knowledge and more innovative skills seem to be more willing to contribute than less qualified community members.

The advent of distributed version control systems has led to the development of a new paradigm for distributed software development; instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull-based development by offering workflow support tools, such as code reviewing systems and integrated issue trackers. In this work, we explore how pull-based software development works, first on the GHTorrent corpus and then on a carefully selected sample of 291 projects. We find that the pull request model offers fast turnaround, increased opportunities for community engagement and decreased time to incorporate contributions. We show that a relatively small number of factors affect both the decision to merge a pull request and the time to process it. We also examine the reasons for pull request rejection and find that technical ones are only a small minority.

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems , and outline open challenges in the area.

STUDY QUESTION: What is the length of the diagnostic delay for endometriosis in Austria and Germany, and what are the reasons for the delay? SUMMARY ANSWER: The diagnostic delay for endometriosis in Austria and Germany is surprisingly long, due to both medical and psychosocial reasons. WHAT IS KNOWN ALREADY: Diagnostic delay of endometriosis is a problematic phenomenon which has been evaluated in several European countries and in the USA, but has not been reported for Germany and Austria. STUDY DESIGN, SIZE, DURATION: A cross-sectional, questionnaire-based multicentre study was conducted in tertiary referral centers in Austria and Germany. From September 2010 to February 2012, 171 patients with histologically confirmed endometriosis were included. PARTICIPANTS, SETTING, METHODS: Patients with a previous history of surgically proven endometriosis, internal diseases such as rheumatic disorders, pain symptoms of other origin, gynecological malignancy or post-menopausal status were excluded from the analysis. Patients with histologically confirmed endometriosis completed a questionnaire about their psychosocial and clinical characteristics and experiences. Of 173 patients, two did not provide informed consent and were excluded from the study. MAIN RESULTS AND THE ROLE OF CHANCE: The median interval from the first onset of symptoms to diagnosis was 10.4 (SD: 7.9) years, and 74% of patients received at least one false diagnosis. Factors such as misdiagnosis, mothers considering menstruation as a negative event and normalization of dysmenorrhea by patients significantly prolonged the diagnostic delay. No association was found between either superficial and deep infiltrating endometriosis or oral contraceptive use and the prolongation of diagnosis. LIMITATIONS AND REASONS FOR CAUTION: There was a possible selection bias due to inclusion of surgically treated patients only. WIDER IMPLICATIONS OF THE FINDINGS: Several factors causing prolongation of diagnosis of endometriosis have been reported to date. The principal factors observed in the present study are false diagnosis and normalization of symptoms. Teaching programs for doctors and public awareness campaigns might reduce diagnostic delay in Central Europe. STUDY FUNDING/COMPETING INTEREST(S): No competing interests exist.

Wisdom represents a fruitful topic for psychological investigations for at least two reasons. First, the study of wisdom emphasizes the search for the continued optimization and the further cultural evolution of the human condition. Second, it exemplifies the collaboration of cognitive, emotional, and motivational processes. The growth and scope of psychological wisdom research over the past few decades demonstrate that it is possible to investigate this complex construct with empirical rigor. Since the 1970s, five main areas have been established: lay definitions of wisdom, conceptualizing and measuring wisdom, understanding the development of wisdom, investigating the plasticity of wisdom, and applying psychological knowledge about wisdom in life contexts.

The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.

Mental disorders represent a worldwide public health concern. Psychotherapies and pharmacotherapies are recommended as first line treatments. However, evidence has emerged that their efficacy may be overestimated, due to a variety of shortcomings in clinical trials (e.g., publication bias, weak control conditions such as waiting list). We performed an umbrella review of recent meta-analyses of randomized controlled trials (RCTs) of psychotherapies and pharmacotherapies for the main mental disorders in adults. We selected meta-analyses that formally assessed risk of bias or quality of studies, excluded weak comparators, and used effect sizes for target symptoms as primary outcome. We searched PubMed and PsycINFO and individual records of the Cochrane Library for meta-analyses published between January 2014 and March 2021 comparing psychotherapies or pharmacotherapies with placebo or treatment-as-usual (TAU), or psychotherapies vs. pharmacotherapies head-to-head, or the combination of psychotherapy with pharmacotherapy to either monotherapy. One hundred and two meta-analyses, encompassing 3,782 RCTs and 650,514 patients, were included, covering depressive disorders, anxiety disorders, post-traumatic stress disorder, obsessive-compulsive disorder, somatoform disorders, eating disorders, attention-deficit/hyperactivity disorder, substance use disorders, insomnia, schizophrenia spectrum disorders, and bipolar disorder. Across disorders and treatments, the majority of effect sizes for target symptoms were small. A random effect meta-analytic evaluation of the effect sizes reported by the largest meta-analyses per disorder yielded a standardized mean difference (SMD) of 0.34 (95% CI: 0.26-0.42) for psychotherapies and 0.36 (95% CI: 0.32-0.41) for pharmacotherapies compared with placebo or TAU. The SMD for head-to-head comparisons of psychotherapies vs. pharmacotherapies was 0.11 (95% CI: -0.05 to 0.26). The SMD for the combined treatment compared with either monotherapy was 0.31 (95% CI: 0.19-0.44). Risk of bias was often high. After more than half a century of research, thousands of RCTs and millions of invested funds, the effect sizes of psychotherapies and pharmacotherapies for mental disorders are limited, suggesting a ceiling effect for treatment research as presently conducted. A paradigm shift in research seems to be required to achieve further progress.

In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

The increasing complexity of products and services, rapidly changing market demands, or growing pressure from various societal groups are trends that require companies to enact new practices to remain competitive. The external search for information and its integration in the context of open innovation is one practice that can lead to increased success. However, the full range of potential open innovation partners has not yet been sufficiently explored, and their effects on innovation performance remain unclear. In this research, we investigated the roles different open innovation partners played in improving economic innovation performance and sustainability innovation performance. Furthermore, we asked whether striving to meet economic and sustainability innovation goals represents a conflict. Drawing on a cross-sectional sample of industrial firms and applying a benchmark approach to identify the relevant performance drivers, we found that, in addition to well-known partners such as universities and customers, increased collaboration with NGOs and intermediaries is beneficial for firms. Moreover, economic innovation performance positively correlates with sustainability innovation performance. This implies that economic and sustainability innovation goals can be reached simultaneously. Thereby, this paper contributes to the open innovation literature by revealing which collaboration partners fit best to strengthen innovation performance, and by clarifying the relationship between economic and sustainability innovation performance.