Search (77 results, page 1 of 4)

0.244031 = product of:
  0.488062 = sum of:
    0.1220155 = product of:
      0.3660465 = sum of:
        0.3660465 = weight(_text_:3a in 1826) [ClassicSimilarity], result of:
          0.3660465 = score(doc=1826,freq=2.0), product of:
            0.3907844 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046093877 = queryNorm
            0.93669677 = fieldWeight in 1826, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.078125 = fieldNorm(doc=1826)
      0.33333334 = coord(1/3)
    0.3660465 = weight(_text_:2f in 1826) [ClassicSimilarity], result of:
      0.3660465 = score(doc=1826,freq=2.0), product of:
        0.3907844 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046093877 = queryNorm
        0.93669677 = fieldWeight in 1826, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.078125 = fieldNorm(doc=1826)
  0.5 = coord(2/4)

Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

0.1220155 = product of:
  0.244031 = sum of:
    0.06100775 = product of:
      0.18302324 = sum of:
        0.18302324 = weight(_text_:3a in 4388) [ClassicSimilarity], result of:
          0.18302324 = score(doc=4388,freq=2.0), product of:
            0.3907844 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046093877 = queryNorm
            0.46834838 = fieldWeight in 4388, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4388)
      0.33333334 = coord(1/3)
    0.18302324 = weight(_text_:2f in 4388) [ClassicSimilarity], result of:
      0.18302324 = score(doc=4388,freq=2.0), product of:
        0.3907844 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046093877 = queryNorm
        0.46834838 = fieldWeight in 4388, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4388)
  0.5 = coord(2/4)

Footnote

Vgl. unter: https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwizweHljdbcAhVS16QKHXcFD9QQFjABegQICRAB&url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F271200105_Die_Autonomie_des_Menschen_und_der_Maschine_-_gegenwartige_Definitionen_von_Autonomie_zwischen_philosophischem_Hintergrund_und_technologischer_Umsetzbarkeit_Redigierte_Version_der_Magisterarbeit_Karls&usg=AOvVaw06orrdJmFF2xbCCp_hL26q.

0.01898975 = product of:
  0.075959 = sum of:
    0.075959 = weight(_text_:communication in 5205) [ClassicSimilarity], result of:
      0.075959 = score(doc=5205,freq=2.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.3816502 = fieldWeight in 5205, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.0625 = fieldNorm(doc=5205)
  0.25 = coord(1/4)

Abstract

We provide a model for the reception of knowledge from externalized information sources. The model is based on a cognitive understanding of information processing and draws up ideas of an exchange of information in communication processes. Karl Popper's three-world theory with its orientation on falsifiable scientific knowledge is extended by John Searle's concept of collective intentionality. This allows a consistent description of externalization and reception of knowledge including scientific knowledge as well as everyday knowledge.

0.014800546 = product of:
  0.059202183 = sum of:
    0.059202183 = product of:
      0.118404366 = sum of:
        0.118404366 = weight(_text_:networks in 1557) [ClassicSimilarity], result of:
          0.118404366 = score(doc=1557,freq=6.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.5430886 = fieldWeight in 1557, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=1557)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.

0.014242312 = product of:
  0.056969248 = sum of:
    0.056969248 = weight(_text_:communication in 50) [ClassicSimilarity], result of:
      0.056969248 = score(doc=50,freq=2.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.28623766 = fieldWeight in 50, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046875 = fieldNorm(doc=50)
  0.25 = coord(1/4)

Abstract

Assistive technology, especially for persons with disabilities, increasingly relies on electronic communication among users, between users and their devices, and among these devices. Making such ICT accessible and inclusive often requires remedial programming, which tends to be costly or even impossible. We, therefore, aim at more interoperable devices, services accessing these devices, and content delivered by these services, at the levels of 1. data and metadata, 2. datamodels and data modelling methods and 3. metamodels as well as a meta ontology language. Even though ontologies are widely being used to enable content interoperability, there is currently no unified framework for ontology interoperability itself. This paper outlines the design considerations underlying OntoIOp (Ontology Integration and Interoperability), a new standardisation activity in ISO/TC 37/SC 3 to become an international standard, which aims at filling this gap.

0.013427781 = product of:
  0.053711124 = sum of:
    0.053711124 = weight(_text_:communication in 3109) [ClassicSimilarity], result of:
      0.053711124 = score(doc=3109,freq=4.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.26986745 = fieldWeight in 3109, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.03125 = fieldNorm(doc=3109)
  0.25 = coord(1/4)

Abstract

This proposal presents the methodology of indexing with images suggested by De Brito and Caribé (2015). The imagetic model is used as a compatible mechanism with FRSAD for a global information share and use of subject data, both within the library sector and beyond. The conceptual model of imagetic indexing shows how images are related to topics and 'key-images' are interpreted as nomens to implement the FRSAD model. Indexing with images consists of using images instead of key-words or descriptors, to represent and organize information. Implementing the imaged navigation in OPACs denotes multiple advantages derived from this rethinking the OPAC anew, since we are looking forward to sharing concepts within the subject authority data. Images, carrying linguistic objects, permeate inter-social and cultural concepts. In practice it includes translated metadata, symmetrical multilingual thesaurus, or any traditional indexing tools. iOPAC embodies efforts focused on conceptual levels as expected from librarians. Imaged interfaces are more intuitive since users do not need specific training for information retrieval, offering easier comprehension of indexing codes, larger conceptual portability of descriptors (as images), and a better interoperability between discourse codes and indexing competences affecting positively social and cultural interoperability. The imagetic methodology deploys R&D fields for more suitable interfaces taking into consideration users with specific needs such as deafness and illiteracy. This methodology arouse questions about the paradigms of the primacy of orality in information systems and pave the way to a legitimacy of multiple perspectives in document indexing by suggesting a more universal communication system based on images. Interdisciplinarity in neurosciences, linguistics and information sciences would be desirable competencies for further investigations about he nature of cognitive processes in information organization and classification while developing assistive KOS for individuals with communication problems, such autism and deafness.

0.012084596 = product of:
  0.048338383 = sum of:
    0.048338383 = product of:
      0.09667677 = sum of:
        0.09667677 = weight(_text_:networks in 1868) [ClassicSimilarity], result of:
          0.09667677 = score(doc=1868,freq=4.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.44343 = fieldWeight in 1868, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=1868)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.

0.011868593 = product of:
  0.047474373 = sum of:
    0.047474373 = weight(_text_:communication in 3863) [ClassicSimilarity], result of:
      0.047474373 = score(doc=3863,freq=2.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.23853138 = fieldWeight in 3863, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3863)
  0.25 = coord(1/4)

Abstract

Knowledge Organization (KO) theoretical foundations are still being developed in a continuous process of epistemological, theoretical and methodological consolidation. The remarkable growth of scientific records has stimulated the analysis of this production and the creation of instruments to evaluate the behavior of science became indispensable. We propose the Domain Analysis of KO in North America through the citation analysis of North American Symposium on Knowledge Organization (NASKO) proceedings (2007 - 2015). We present the citation, co-citation and bibliographic coupling analysis to visualize and recognize the researchers that influence the scholarly communication in this domain. The most prolific authors through NASKO conferences are Smiraglia, Tennis, Green, Dousa, Grant Campbell, Pimentel, Beak, La Barre, Kipp and Fox. Regarding their theoretical references, Hjørland, Olson, Smiraglia, and Ranganathan are the authors who most inspired the event's studies. The co-citation network shows the highest frequency is between Olson and Mai, followed by Hjørland and Mai and Beghtol and Mai, consolidating Mai and Hjørland as the central authors of the theoretical references in NASKO. The strongest theoretical proximity in author bibliographic coupling network occurs between Fox and Tennis, Dousa and Tennis, Tennis and Smiraglia, Dousa and Beak, and Pimentel and Tennis, highlighting Tennis as central author, that interconnects the others in relation to KO theoretical references in NASKO. The North American chapter has demonstrated a strong scientific production as well as a high level of concern with theoretical and epistemological questions, gathering researchers from different countries, universities and knowledge areas.

0.011039853 = product of:
  0.044159412 = sum of:
    0.044159412 = product of:
      0.088318825 = sum of:
        0.088318825 = weight(_text_:22 in 3582) [ClassicSimilarity], result of:
          0.088318825 = score(doc=3582,freq=4.0), product of:
            0.16141291 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046093877 = queryNorm
            0.54716086 = fieldWeight in 3582, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3582)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 4.2017 10:42:05
22. 4.2017 10:48:38

0.010928897 = product of:
  0.04371559 = sum of:
    0.04371559 = product of:
      0.08743118 = sum of:
        0.08743118 = weight(_text_:22 in 8365) [ClassicSimilarity], result of:
          0.08743118 = score(doc=8365,freq=2.0), product of:
            0.16141291 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046093877 = queryNorm
            0.5416616 = fieldWeight in 8365, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=8365)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 6.2015 16:08:38

0.010070836 = product of:
  0.040283345 = sum of:
    0.040283345 = weight(_text_:communication in 3380) [ClassicSimilarity], result of:
      0.040283345 = score(doc=3380,freq=4.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.2024006 = fieldWeight in 3380, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3380)
  0.25 = coord(1/4)

Abstract

In April 2010, Bill Gates gave a talk at MIT in which he asked: 'are the brightest minds working on the most important problems?' Gates meant improving the lives of the poorest; improving education, health, and nutrition. We could easily add improving peaceful interactions, human rights, environmental conditions, living standards and so on. Philosophy of Information (PI) proponents think that Gates has a point - but this doesn't mean we should all give up philosophy. Philosophy can be part of this project, because philosophy understood as conceptual design forges and refines the new ideas, theories, and perspectives that we need to understand and address these important problems that press us so urgently. Of course, this naturally invites us to wonder which ideas, theories, and perspectives philosophers should be designing now. In our global information society, many crucial challenges are linked to information and communication technologies: the constant search for novel solutions and improvements demands, in turn, changing conceptual resources to understand and cope with them. Rapid technological development now pervades communication, education, work, entertainment, industrial production and business, healthcare, social relations and armed conflicts. There is a rich mine of philosophical work to do on the new concepts created right here, right now.

0.010070496 = product of:
  0.040281985 = sum of:
    0.040281985 = product of:
      0.08056397 = sum of:
        0.08056397 = weight(_text_:networks in 1865) [ClassicSimilarity], result of:
          0.08056397 = score(doc=1865,freq=4.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.369525 = fieldWeight in 1865, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1865)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

There's something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you. By the way, together with this post I am also releasing code on Github (https://github.com/karpathy/char-rnn) that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. You can also use it to reproduce my experiments below. But we're getting ahead of ourselves; What are RNNs anyway?

0.010070496 = product of:
  0.040281985 = sum of:
    0.040281985 = product of:
      0.08056397 = sum of:
        0.08056397 = weight(_text_:networks in 1873) [ClassicSimilarity], result of:
          0.08056397 = score(doc=1873,freq=4.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.369525 = fieldWeight in 1873, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1873)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.

0.009969283 = product of:
  0.03987713 = sum of:
    0.03987713 = product of:
      0.07975426 = sum of:
        0.07975426 = weight(_text_:networks in 5798) [ClassicSimilarity], result of:
          0.07975426 = score(doc=5798,freq=2.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.36581108 = fieldWeight in 5798, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5798)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

0.009494875 = product of:
  0.0379795 = sum of:
    0.0379795 = weight(_text_:communication in 4395) [ClassicSimilarity], result of:
      0.0379795 = score(doc=4395,freq=2.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.1908251 = fieldWeight in 4395, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.03125 = fieldNorm(doc=4395)
  0.25 = coord(1/4)

Abstract

Today many libraries are using computers and allied information technologies to improve their work methods and services. Consequently, the libraries need such professional staff, or need to train the present one, who could face the challenges placed by the introduction of these technologies in the libraries. To meet the demand of such professional staff, the departments of Library and Information Science in India introduced new courses of studies to expose their students in the use and application of computers and other allied technologies. Some courses introduced are: Computer Application in Libraries; Systems Analysis and Design Technique; Design and Development of Computer-based Library Information Systems; Database Organisation and Design; Library Networking; Use and Application of Communication Technology, and so forth. It is felt that the computer and information technologies biased courses need to be restructured, revised, and more harmoniously blended with the traditional main stream courses of library and information science discipline. We must alter the strategy of teaching library techniques, such as classification, cataloguing, and library procedures, and the techniques of designing computer-based library information systems and services. The use and application of these techniques get interwoven when we shift from a manually operated library system's environment to computer-based library system's environment. As such, it becomes necessary that we must follow an integrative approach, when we teach these techniques to the students of library and information science or train library staff in the use and application of these techniques to design, develop and implement computer-based library information systems and services. In the following sections of this paper, we shall outline the likeness or correspondence between certain concepts and techniques formed by computer specialist and the one developed by the librarians, in their respective domains. We make use of these techniques (i.e. the techniques of both the domains) in the design and implementation of computer-based library information systems and services. As such, it is essential that lessons of study concerning the exposition of these supplementary and complementary techniques must be integrated.

0.009367626 = product of:
  0.037470505 = sum of:
    0.037470505 = product of:
      0.07494101 = sum of:
        0.07494101 = weight(_text_:22 in 1552) [ClassicSimilarity], result of:
          0.07494101 = score(doc=1552,freq=2.0), product of:
            0.16141291 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046093877 = queryNorm
            0.46428138 = fieldWeight in 1552, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=1552)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 6.2018 11:04:35

0.008831883 = product of:
  0.03532753 = sum of:
    0.03532753 = product of:
      0.07065506 = sum of:
        0.07065506 = weight(_text_:22 in 4313) [ClassicSimilarity], result of:
          0.07065506 = score(doc=4313,freq=4.0), product of:
            0.16141291 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046093877 = queryNorm
            0.4377287 = fieldWeight in 4313, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4313)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 6.2018 14:22:02

0.008545099 = product of:
  0.034180395 = sum of:
    0.034180395 = product of:
      0.06836079 = sum of:
        0.06836079 = weight(_text_:networks in 970) [ClassicSimilarity], result of:
          0.06836079 = score(doc=970,freq=2.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.31355235 = fieldWeight in 970, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=970)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

0.008308016 = product of:
  0.033232063 = sum of:
    0.033232063 = weight(_text_:communication in 3460) [ClassicSimilarity], result of:
      0.033232063 = score(doc=3460,freq=2.0), product of:
        0.19902779 = queryWeight, product of:
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.046093877 = queryNorm
        0.16697197 = fieldWeight in 3460, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.317879 = idf(docFreq=1601, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3460)
  0.25 = coord(1/4)

Content

Garfield wandte sich im Zusammenhang mit seinen Messgrößen gegen "Bibliographic Negligence" und "Citation Amnesia", Er schrieb 2002: "There will never be a perfect solution to the problem of acknowledging intellectual debts. But a beginning can be made if journal editors will demand a signed pledge from authors that they have searched Medline, Science Citation Index, or other appropriate print and electronic databases." Er warnte aber auch vor einen unsachgemäßen Umgang mit seinen Messgößen und vor übertriebenen Erwartungen an sie in Zusammenhang mit Karriereentscheidungen über Wissenschaftler und Überlebensentscheidungen für wissenschaftliche Einrichtungen. 1982 übernahm die Thomson Corporation ISI für 210 Millionen Dollar. In der heutigen Nachfolgeorganisation Clarivate Analytics sind mehr als 4000 Mitarbeitern in über hundert Ländern beschäftigt. Garfield gründete auch eine Zeitung für Wissenschaftler, speziell für Biowissenschaftler, "The Scientist", die weiterbesteht und als kostenfreier Pushdienst bezogen werden kann. In seinen Beiträgen zur Wissenschaftspolitik kritisierte er beispielsweise die Wissenschaftsberater von Präsident Reagen 1986 als "Advocats of the administration´s science policies, rather than as objective conduits for communication between the president and the science community." Seinen Beitrag, mit dem er darum warb, die Förderung von UNESCO-Forschungsprogrammen fortzusetzen, gab er den Titel: "Let´s stand up für Global Science". Das ist auch in Trump-Zeiten ein guter Titel, da die US-Regierung den Wahrheitsbegriff, auf der Wissenschaft basiert, als bedeutungslos verwirft und sich auf Nationalismus und Abschottung statt auf internationale Kommunikation, Kooperation und gemeinsame Ausschöpfung von Interessen fokussiert."

0.008056397 = product of:
  0.032225586 = sum of:
    0.032225586 = product of:
      0.06445117 = sum of:
        0.06445117 = weight(_text_:networks in 1) [ClassicSimilarity], result of:
          0.06445117 = score(doc=1,freq=4.0), product of:
            0.21802035 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046093877 = queryNorm
            0.29562 = fieldWeight in 1, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.03125 = fieldNorm(doc=1)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based on tags, as well as following the user-user connections formed in the social bookmarking community. However, the effectiveness of tag-based search is limited due to the lack of explicitly represented semantics in the tag space. In addition, social connections between users are underused for web content discovery because of the inadequate social functions. In this research, we propose a comprehensive framework to reorganize the flat tag space into a hierarchical faceted model. We also studied the structure and properties of various networks emerging from the tag space for the purpose of more efficient web content discovery. The major research approach used in this research is social network analysis (SNA), together with methodologies employed in design science research. The contribution of our research includes: (i) a faceted model to categorize social bookmarking tags; (ii) a relationship ontology to represent the semantics of relationships between tags; (iii) heuristics to reorganize the flat tag space into a hierarchical faceted model using analysis of tag-tag co-occurrence networks; (iv) an implemented prototype system as proof-of-concept to validate the feasibility of the reorganization approach; (v) a set of evaluations of the social functions of the current networking features of social bookmarking and a series of recommendations as to how to improve the social functions to facilitate web content discovery.