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  • 1.
    Bravo, Giangiacomo
    et al.
    Linnéuniversitetet, Fakulteten för samhällsvetenskap (FSV), Institutionen för samhällsstudier (SS).
    Farjam, Mike
    Linnéuniversitetet, Fakulteten för samhällsvetenskap (FSV), Institutionen för samhällsstudier (SS).
    Prospects and Challenges for the Computational Social Sciences2017Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, nr 11, s. 1057-1069Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Computational social sciences (CSS) refer to computer-enabled investigations of human behaviour and social interaction. They include three main components - (i) computational modelling and social simulation, (ii) the analysis of digital traces of online interactions, (iii) virtual labs and online experiments - and allow researchers to perform studies that were even hard to imagine a few decades ago. Moreover, CSS favour a more systematic test of theories and increase the possibility of study replication, two factors holding the potential to help social sciences reach a higher scientific status. Despite the huge potential of CSS, we follow previous works in identifying several impediments to a larger adoption of computational methods in social sciences. Most of them are linked with the humanistic attitude and a lack of technical skills of many social scientist. Significant changes in the basic training of social scientist and in the relation patterns with other disciplines and departments are needed before the potential of CSS can be fully exploited.

  • 2.
    Bravo, Giangiacomo
    et al.
    Linnéuniversitetet, Fakulteten för samhällsvetenskap (FSV), Institutionen för samhällsstudier (SS).
    Laitinen, Mikko
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för språk (SPR).
    Levin, Magnus
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för språk (SPR).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Petersson, Göran
    Linnéuniversitetet, Fakulteten för Hälso- och livsvetenskap (FHL), Institutionen för medicin och optometri (MEO).
    Big Data in Cross-Disciplinary Research: J.UCS Focused Topic2017Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, nr 11, s. 1035-1037Artikel i tidskrift (Övrigt vetenskapligt)
  • 3.
    Danylenko, Antonina
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Lundberg, Jonas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Decisions: Algebra, Implementation, and First Experiments2014Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 20, nr 9, s. 1174-1231Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Classification is a constitutive part in many different fields of Computer Science. There exist several approaches that capture and manipulate classification information in order to construct a specific classification model. These approaches are often tightly coupled to certain learning strategies, special data structures for capturing the models, and to how common problems, e.g. fragmentation, replication and model overfitting, are addressed. In order to unify these different classification approaches, we define a Decision Algebra which defines models for classification as higher order decision functions abstracting from their implementations using decision trees (or similar), decision rules, decision tables, etc. Decision Algebra defines operations for learning, applying, storing, merging, approximating, and manipulating models for classification, along with some general algebraic laws regardless of the implementation used. The Decision Algebra abstraction has several advantages. First, several useful Decision Algebra operations (e.g., learning and deciding) can be derived based on the implementation of a few core operations (including merging and approximating). Second, applications using classification can be defined regardless of the different approaches. Third, certain properties of Decision Algebra operations can be proved regardless of the actual implementation. For instance, we show that the merger of a series of probably accurate decision functions is even more accurate, which can be exploited for efficient and general online learning. As a proof of the Decision Algebra concept, we compare decision trees with decision graphs, an efficient implementation of the Decision Algebra core operations, which capture classification models in a non-redundant way. Compared to classical decision tree implementations, decision graphs are 20% faster in learning and classification without accuracy loss and reduce memory consumption by 44%. This is the result of experiments on a number of standard benchmark data sets comparing accuracy, access time, and size of decision graphs and trees as constructed by the standard C4.5 algorithm. Finally, in order to test our hypothesis about increased accuracy when merging decision functions, we merged a series of decision graphs constructed over the data sets. The result shows that on each step the accuracy of the merged decision graph increases with the final accuracy growth of up to 16%.

  • 4.
    Golub, Koraljka
    et al.
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för kulturvetenskaper (KV).
    Hansson, Joacim
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för kulturvetenskaper (KV).
    (Big) Data in Library and Information Science: A Brief Overview of Some Important Problem Areas2017Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, nr 11, s. 1098-1108Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Libraries hold a long history of a multidimensional focus on collecting, storing, organizing, preserving and providing access to information resources for various types of users. Data is nothing new to Library and Information Science (LIS) and Big Data presents a quantitative expansion of an already well-known object of study. Scholarly communication, data sharing and data curation are three areas related to data in LIS and are discussed in this paper in the light of current developments as well as from the perspective of attaining the research area relevance in the discipline over time. Big Data, new technologies and networked research environments will continue to increase both in numbers and size. LIS is rapidly developing tools to meet the opportunities arising - through educational initiatives and the development of new research areas such as data curation and altmetrics. Since social and political demands for open data grow, these issues are pressing.

  • 5.
    Heberle, Andreas
    et al.
    Karlsruhe University of Applied Sciences, Germany.
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Softwerk AB, Växjö.
    Gustafsson, Anders
    Södra Skog, Växjö.
    Vorrei, Orjan
    Södra Skog, Växjö.
    Digitalization Canvas - Towards Identifying Digitalization Use Cases and Projects2017Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, nr 11, s. 1070-1097Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Nowadays, many companies are running digitalization initiatives or are planning to do so. There exist various models to evaluate the digitalization potential of a company and to define the maturity level of a company in exploiting digitalization technologies summarized under buzzwords such as Big Data, Artificial Intelligence (AI), Deep Learning, and the Industrial Internet of Things (IIoT). While platforms, protocols, patterns, technical implementations, and standards are in place to adopt these technologies, small-to mediumsized enterprises (SME) still struggle with digitalization. This is because it is hard to identify the most beneficial projects with manageable cost, limited resources and restricted know-how. In the present paper, we describe a real-life project where digitalization use cases have been identified, evaluated, and prioritized with respect to benefits and costs. This effort led to a portfolio of projects, some with quick and easy wins and some others with mid-to long-term benefits. From our experiences, we extracted a general approach that could be useful for other SMEs to identify concrete digitalization activities and to define projects implementing their digital transformation. The results are summarized in a Digitalization Canvas.

  • 6.
    Jansen, Marc
    et al.
    University of Applied Sciences Ruhr West.
    Bollen, Lars
    University of Twente.
    Bailoan, Nelson
    Universidad de Chile.
    Hoppe, Ulrich
    University of Duisburg-Essen.
    Using Cloud Services to Develop Learning Scenarios from a Software Engineering Perspective2013Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 19, nr 14, s. 2017-2053Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The term "Cloud Computing" does not primarily specify new types of core technologies but rather addresses features to do with integration, inter-operability and accessibility. Although not new, virtualization and automation are core features that characterize Cloud Computing. In this paper, we intend to explore the possibility of integrating cloud services with educational scenarios without re-defining neither the technology nor the usage scenarios from scratch. Our suggestion is based on certain solutions that have already been implemented and tested for specific cases.

  • 7.
    José, Rui
    et al.
    Department of Information Systems, University of Minho, Portugal.
    Rodrigues, Helena
    Department of Information Systems, University of Minho.
    Otero, Nuno
    Linnéuniversitetet, Fakultetsnämnden för naturvetenskap och teknik, Institutionen för datavetenskap, fysik och matematik, DFM.
    Ambient Intelligence: Beyond the Inspiring Vision2010Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 16, nr 12, s. 1480-1499Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Ambient Intelligence (AmI) has emerged in the past 10 years as a multidisciplinary field within ubiquitous computing, attracting considerable research, funding and public attention and leading to many research groups, and conferences specifically focused on Ambient Intelligence topics. From its conception, AmI has always been a field strongly driven by a particular vision of how ICT technologies would shape our future. This has given the AmI vision, essentially as proposed by ISTAG, an excessively central role in shaping the field and setting its research agenda. We argue that this inspiring vision should no longer be the main driver for AmI research and that we should now re-interpret its role in the background of 10 years of research.

    In this paper, we reflect on what it means for AmI to move behind its foundational vision and we identify a number of emerging trends around some of its core concepts, more specifically the notion of intelligence, the system view and the requirements process. The main motivation is to search for alternative research directions that may be more effective in delivering today the essence of the AmI vision, even if they mean abandoning some of the currently prevailing approaches and assumptions. Overall, these trends provide a more holistic view of AmI and may represent important contributions for bringing this field closer to realisation, delivery and real social impact.

  • 8.
    Laitinen, Mikko
    et al.
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för språk (SPR). Univ Eastern Finland, Finland.
    Lundberg, Jonas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Levin, Magnus
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för språk (SPR).
    Lakaw, Alexander
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för språk (SPR).
    Utilizing Multilingual Language Data in (Nearly) Real Time: The Case of the Nordic Tweet Stream2017Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, nr 11, s. 1038-1056Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents the Nordic Tweet Stream, a cross-disciplinary digital humanities project that downloads Twitter messages from Denmark, Finland, Iceland, Norway and Sweden. The paper first introduces some of the technical aspects in creating a real-time monitor corpus that grows every day, and then two case studies illustrate how the corpus could be used as empirical evidence in studies focusing on the global spread of English. Our approach in the case studies is sociolinguistic, and we are interested in how widespread multilingualism which involves English is in the region, and what happens to ongoing grammatical change in digital environments. The results are based on 6.6 million tweets collected during the first four months of data streaming. They show that English was the most frequently used language, accounting for almost a third. This indicates that Nordic Twitter users choose English as a means of reaching wider audiences. The preference for English is the strongest in Denmark and the weakest in Finland. Tweeting mostly occurs late in the evening, and high-profile media events such as the Eurovision Song Contest produce considerable peaks in Twitter activity. The prevalent use of informal features such as univerbated verb forms (e.g., gotta for (HAVE) got to) supports previous findings of the speech-like nature of written Twitter data, but the results indicate that tweeters are pushing the limits even further.

  • 9.
    Lundberg, Jonas
    et al.
    Linnéuniversitetet, Fakultetsnämnden för naturvetenskap och teknik, Institutionen för datavetenskap, fysik och matematik, DFM.
    Löwe, Welf
    Linnéuniversitetet, Fakultetsnämnden för naturvetenskap och teknik, Institutionen för datavetenskap, fysik och matematik, DFM.
    Points-to Analysis: A Fine-Grained Evaluation2012Ingår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 18, nr 20, s. 2851-2878Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Points-to analysis is a static program analysis that extracts reference information from programs, e.g., possible targets of a call and possible objects referenced by a field. Previous works evaluating different approaches to context-sensitive Points-to analyses use coarse-grained precision metrics focusing on references between source code entities like methods and classes. Two typical examples of such metrics are the number of nodes and edges in a call-graph. These works indicate that context-sensitive analysis with a call-depth k = 1 only provides slightly better precision than context-insensitive analysis. Moreover, these works could not find a substantial precision improvement when using the more expensive analyses with call-depth k > 1. The hypothesis in the present paper is that substantial differences between the context-sensitive approaches show if (and only if) the precision is measured by more fine-grained metrics focusing on individual objects (rather than methods and classes) and references between them. These metrics are justified by the many applications requiring such detailed object reference information. In order to experimentally validate our hypothesis we make a systematic comparison of ten different variants of context-sensitive Points-to analysis using different call-depths k >= 1 for separating the contexts. For the comparison we use a metric suite containing four different metrics that all focus on individual objects and references between them. The main results show that the differences between different context-sensitive analysis techniques are substantial, also the differences between the context-insensitive and the context-sensitive analyses with call-depth k = 1 are substantial. The major surprise was that increasing the call-depth k > 1 did not lead to any substantial precision improvements. This is a negative result since it indicates that, in practice, we cannot get a more precise Points-to analysis by increasing the call-depth. Further investigations show that substantial precision improvements can be detected for k > 1, but they occur at such a low detail level that they are unlikely to be of any practical use.

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