Ändra sökning
Avgränsa sökresultatet
1 - 17 av 17
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Aghaee, Naghmeh
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Jobe, William Byron
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Smedberg, Åsa
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Hansson, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Tedre, Matti
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Interaction Gaps in PhD Education and ICT as a Way Forward: Results from a Study in Sweden2016Ingår i: The International Review of Research in Open and Distributed Learning, ISSN 1492-3831, Vol. 17, nr 3, 360-383 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many research studies have highlighted the low completion rate and slow progress in PhD education. Universities strive to improve throughput and quality in their PhD education programs. In this study, the perceived problems of PhD education are investigated from PhD students' points of view, and how an Information and Communication Technology Support System (ICTSS) may alleviate these problems. Data were collected through an online open questionnaire sent to the PhD students at the Department of (the institution's name has been removed during the double-blind review) with a 59% response rate. The results revealed a number of problems in the PhD education and highlighted how online technology can support PhD education and facilitate interaction and communication, affect the PhD students' satisfaction, and have positive impacts on PhD students' stress. A system was prototyped, in order to facilitate different types of online interaction through accessing a set of online and structured resources and specific communication channels. Although the number of informants was not large, the result of the study provided some rudimentary ideas that refer to interaction problems and how an online ICTSS may facilitate PhD education by providing distance and collaborative learning, and PhD students' self-managed communication.

  • 2.
    Aghaee, Naghmeh
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Smedberg, Åsa
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Jobe, William Byron
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    ICT for Communication and Collaborative Learning among PhD peers: Results of the Needs and Desires from a PhD Survey2014Ingår i: DSV writers hut 2014: proceedings, August 21-22, Åkersberga, Sweden / [ed] Gustaf Juell-Skielse, Stockholm: Department of Computer and Systems Sciences, Stockholm University , 2014, 33-40 s.Konferensbidrag (Refereegranskat)
  • 3.
    Aghaee, Nam
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Smedberg, Åsa B.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Jobe, William
    Communication and Collaboration Gaps among PhD Students and ICT as a Way Forward: Results from a Study in Sweden2015Ingår i: E-Learn: 20 th annual World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2015, 2015Konferensbidrag (Refereegranskat)
  • 4.
    Byungura, Jean Claude
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Hansson, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    User Perceptions on Relevance of a Learning Management System: An Evaluation of Behavioral Intention and Usage of SciPro System at University of Rwanda2015Ingår i: Expanding Learning Scenarios - Opening Out the Educational Landscape: Book of Abstracts / [ed] António Moreira Teixeira, András Szűcs, Ildikó Mázár, 2015, 64-64 s.Konferensbidrag (Refereegranskat)
  • 5.
    Byungura, Jean Claude
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Hansson, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Masengesho, Kamuzinzi
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    ICT Capacity Building: A Critical Discourse Analysis of Rwandan Policies from Higher Education Perspective2016Ingår i: European Journal of Open, Distance and e-Learning, ISSN 1027-5207, Vol. 19, nr 2, 46-62 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the development of technology in the 21st Century, education systems attempt to integrate technology-based tools to improve experiences in pedagogy and administration. It is becoming increasingly prominent to build human and ICT infrastructure capacities at universities from policy to implementation level. Using a critical discourse analysis, this study investigates the articulation of ICT capacity building strategies from both national and institutional ICT policies in Rwanda, focusing on the higher education. Eleven policy documents were collected and deeply analyzed to understand which claims of ICT capacity building are made. The analysis shows that strategies for building ICT capacities are evidently observed from national level policies and only in two institutional policies (KIST and NUR). Among 25 components of ICT capacity building used, the ones related to human capacity are not plainly described. Additionally, neither national nor institutional policy documents include the creation of financial schemes for students to acquire ICT tools whilst learners are key stakeholders. Although there is some translation of ICT capacity building strategies from national to some institutional policies, planning for motivation and provision of incentives to innovators is not stated in any of the institutional policies and this is a key to effective technology integration.

  • 6.
    Byungura, Jean Claude
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Hansson, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Mazimpaka, Olivier
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Exploring Teacher Adoption and Use of an Upgraded eLearning Platform for ICT Capacity Building at University of Rwanda2016Ingår i: IST-Africa 2016 Conference: Proceedings / [ed] Paul Cunningham, Miriam Cunningham, IEEE Computer Society, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Integrating technology in pedagogy is a step for ICT capacity building for higher education to meet its current demands. Therefore, the integration of eLearning systems has been problematic, albeit huge investments in ICT infrastructure. This study investigates teacher adoption of a new upgraded eLearning platform being integrated at University of Rwanda. A six-constructs model related to technology adoption was used to design questionnaire and interviews. Closed and open-ended questions seeking perceptions on the UR eLearning environment were used on 87 respondents who were purposively selected. Findings indicate that although participants find the system useful, easy and trustworthy, the intention for adopting and using it is very low due managerial support and technical support. Gaps in policy synergy, incentives, basic infrastructure, managerial and technical support were among the identified bottlenecks contributing negatively to the low degree of teacher intention. The study concludes by proposing some remedies to address the above challenges.

  • 7.
    Karunaratne, Thashmee
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Is Frequent Pattern Mining useful in building predictive models?2011Ingår i: ECML/PKDD 2011: Workshop of Collective Learning and Inference on Structured Data, 2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    The recent studies of pattern mining have given more attention to discovering patterns that are interesting, significant, discriminative and so forth, than simply frequent. Does this imply that the frequent patterns are not useful anymore? In this paper we carry out a survey of frequent pattern mining and, using an empirical study, show how far the frequent pattern mining is useful in building predictive models.

  • 8.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Can frequent itemset mining be efficiently and effectively used for learning from graph data?2012Ingår i: 11th International Conference on Machine Learning and Applications (ICMLA) / [ed] Juan E. Guerrero, IEEE Computer Society, 2012, 409-414 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Standard graph learning approaches are often challenged by the computational cost involved when learning from very large sets of graph data. One approach to overcome this problem is to transform the graphs into less complex structures that can be more efficiently handled. One obvious potential drawback of this approach is that it may degrade predictive performance due to loss of information caused by the transformations. An investigation of the tradeoff between efficiency and effectiveness of graph learning methods is presented, in which state-of-the-art graph mining approaches are compared to representing graphs by itemsets, using frequent itemset mining to discover features to use in prediction models. An empirical evaluation on 18 medicinal chemistry datasets is presented, showing that employing frequent itemset mining results in significant speedups, without sacrificing predictive performance for both classification and regression.

  • 9.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    DIFFER: A Propositionalization Approach for Learning from Structured Data2006Ingår i: Proceedings of World Academy of Science, Engineering and Technology, ISSN 2070-3740, Vol. 15, 49-51 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

  • 10.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Graph propositionalization for random forests2009Ingår i: The Eighth International Conference on Machine Learning and Applications: Proceedings, IEEE Computer Society, 2009, 196-201 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Graph propositionalization methods transform structured and relational data into a fixed-length feature vector format that can be used by standard machine learning methods. However, the choice of propositionalization method may have a significant impact on the performance of the resulting classifier. Six different propositionalization methods are evaluated when used in conjunction with random forests. The empirical evaluation shows that the choice of propositionalization method has a significant impact on the resulting accuracy for structured data sets. The results furthermore show that the maximum frequent itemset approach and a combination of this approach and maximal common substructures turn out to be the most successful propositionalization methods for structured data, each significantly outperforming the four other considered methods.

  • 11.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Learning from Structured Data by Finger Printing2006Ingår i: Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), Helsinki: Finnish Artificial Intelligence society FAIS , 2006, 120-126 s.Konferensbidrag (Refereegranskat)
  • 12.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Learning to Classify Structured Data by Graph Propositionalization2006Ingår i: Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006Konferensbidrag (Refereegranskat)
    Abstract [en]

    Existing methods for learning from structured data are limited with respect to handling large or isolated substructures and also impose constraints on search depth and induced structure length. An approach to learning from structured data using a graph based propositionalization method, called finger printing, is introduced that addresses the limitations of current methods. The method is implemented in a system called DIFFER, which is demonstrated to compare favorable to existing state-of-art methods on some benchmark data sets. It is shown that further improvements can be obtained by combining the features generated by finger printing with features generated by previous methods.

  • 13.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    University of Skövde, Sweden.
    The effect of background knowledge in graph-based learning in the chemoinformatics domain2008Ingår i: Trends in Intelligent Systems and Computer Engineering / [ed] Oscar Castillo, Li Xu, Sio-Iong Ao, Springer, 2008, 141-153 s.Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Typical machine learning systems often use a set of previous experiences (examples) to learn concepts, patterns, or relations hidden within the data [1]. Current machine learning approaches are challenged by the growing size of the data repositories and the growing complexity of those data [1, 2]. In order to accommodate the requirement of being able to learn from complex data, several methods have been introduced in the field of machine learning [2]. Based on the way the input and resulting hypotheses are represented, two main categories of such methods exist, namely, logic-based and graph-based methods [3]. The demarcation line between logic- and graph-based methods lies in the differences of their data representation methods, hypothesis formation, and testing as well as the form of the output produced.

    The main purpose of our study is to investigate the effect of incorporating background knowledge into graph learning methods. The ability of graph learning methods to obtain accurate theories with a minimum of background knowledge is of course a desirable property, but not being able to effectively utilize additional knowledge that is available and has been proven important is clearly a disadvantage. Therefore we examine how far additional, already available, background knowledge can be effectively used for increasing the performance of a graph learner. Another contribution of our study is that it establishes a neutral ground to compare classifi- cation accuracies of the two closely related approaches, making it possible to study whether graph learning methods actually would outperform ILP methods if the same background knowledge were utilized [9].

    The rest of this chapter is organized as follows. The next section discusses related work concerning the contribution of background knowledge when learning from complex data. Section 10.3 provides a description of the graph learning method that is used in our study. The experimental setup, empirical evaluation, and the results from the study are described in Sect. 10.4. Finally, Sect. 10.5 provides conclusions from the experiments and points out interesting extensions of the work reported in this study.

  • 14.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Using Background Knowledge for Graph Based Learning: a Case Study in Chemoinformatics2007Ingår i: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, Hong Kong: International Association of Engineers, 2007, 153-157 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Incorporating background knowledge in the learning process is proven beneficial for numerous applications of logic based learning methods. Yet the effect of background knowledge in graph based learning is not systematically explored. This paper describes and demonstrates the first step in this direction and elaborates on how additional relevant background knowledge could be used to improve the predictive performance of a graph learner. A case study in chemoinformatics is undertaken in this regard in which various types of background knowledge are encoded in graphs that are given as input to a graph learner. It is shown that the type of background knowledge encoded indeed has an effect on the predictive performance, and it is concluded that encoding appropriate background knowledge can be more important than the choice of the graph learning algorithm.

  • 15.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Norinder, Ulf
    Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling2013Ingår i: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 17, nr 2, 327-341 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods.

  • 16.
    Karunaratne, Thashmee
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Norinder, Ulf
    Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - a Case Study with Medicinal Chemistry Datasets2010Ingår i: Ninth International Conference on Machine Learning and Applications (ICMLA), 2010: Proceedings, IEEE Computer Society, 2010, 828-833 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Graph propositionalization methods can be used to transform structured and relational data into fixed-length feature vectors, enabling standard machine learning algorithms to be used for generating predictive models. It is however not clear how well different propositionalization methods work in conjunction with different standard machine learning algorithms. Three different graph propositionalization methods are investigated in conjunction with three standard learning algorithms: random forests, support vector machines and nearest neighbor classifiers. An experiment on 21 datasets from the domain of medicinal chemistry shows that the choice of propositionalization method may have a significant impact on the resulting accuracy. The empirical investigation further shows that for datasets from this domain, the use of the maximal frequent item set approach for propositionalization results in the most accurate classifiers, significantly outperforming the two other graph propositionalization methods considered in this study, SUBDUE and MOSS, for all three learning methods.

  • 17.
    Karunaratne, Thashmee M.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Learning predictive models from graph data using pattern mining2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.

1 - 17 av 17
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf