207 research outputs found

    Förteckning på böcker och skrifter, som finnas uti G. M. Meyer's lån- och läse-bibliothek i Helsingfors

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    Sivu 3 on virheellisesti sivu 2.Imprimatur: J. M. af Tengström.S. I-III: Kirjaston käyttösäännöt. Päiväys: Helsingfors 1836, G. Meyer

    Cross-Language Plagiarism Detection

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    Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.This work was partially supported by the TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 project and the CONACyT-Mexico 192021 grant.Potthast, M.; Barrón Cedeño, LA.; Stein, B.; Rosso, P. (2011). Cross-Language Plagiarism Detection. Language Resources and Evaluation. 45(1):45-62. https://doi.org/10.1007/s10579-009-9114-zS4562451Ballesteros, L. A. (2001). Resolving ambiguity for cross-language information retrieval: A dictionary approach. PhD thesis, University of Massachusetts Amherst, USA, Bruce Croft.Barrón-Cedeño, A., Rosso, P., Pinto, D., & Juan A. (2008). On cross-lingual plagiarism analysis using a statistical model. In S. Benno, S. Efstathios, & K. Moshe (Eds.), ECAI 2008 workshop on uncovering plagiarism, authorship, and social software misuse (PAN 08) (pp. 9–13). Patras, Greece.Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities, 3, 1–8.Berger, A., & Lafferty, J. (1999). Information retrieval as statistical translation. In SIGIR’99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (vol. 4629, pp. 222–229). Berkeley, California, United States: ACM.Brin, S., Davis, J., & Garcia-Molina, H. (1995). Copy detection mechanisms for digital documents. In SIGMOD ’95 (pp. 398–409). New York, NY, USA: ACM Press.Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.Ceska, Z., Toman, M., & Jezek, K. (2008). Multilingual plagiarism detection. In AIMSA’08: Proceedings of the 13th international conference on artificial intelligence (pp. 83–92). Berlin, Heidelberg: Springer.Clough, P. (2003). Old and new challenges in automatic plagiarism detection. National UK Plagiarism Advisory Service, http://www.ir.shef.ac.uk/cloughie/papers/pas_plagiarism.pdf .Dempster A. P., Laird N. M., Rubin D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Dumais, S. T., Letsche, T. A., Littman, M. L., & Landauer, T. K. (1997). Automatic cross-language retrieval using latent semantic indexing. In D. Hull & D. Oard (Eds.), AAAI-97 spring symposium series: Cross-language text and speech retrieval (pp. 18–24). Stanford University, American Association for Artificial Intelligence.Gabrilovich, E., & Markovitch, S. (2007). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th international joint conference for artificial intelligence, Hyderabad, India.Hoad T. C., & Zobel, J. (2003). Methods for identifying versioned and plagiarised documents. American Society for Information Science and Technology, 54(3), 203–215.Levow, G.-A., Oard, D. W., & Resnik, P. (2005). Dictionary-based techniques for cross-language information retrieval. Information Processing & Management, 41(3), 523–547.Littman, M., Dumais, S. T., & Landauer, T. K. (1998). Automatic cross-language information retrieval using latent semantic indexing. In Cross-language information retrieval, chap. 5 (pp. 51–62). Kluwer.Maurer, H., Kappe, F., & Zaka, B. (2006). Plagiarism—a survey. Journal of Universal Computer Science, 12(8), 1050–1084.McCabe, D. (2005). Research report of the Center for Academic Integrity. http://www.academicintegrity.org .Mcnamee, P., & Mayfield, J. (2004). Character N-gram tokenization for European language text retrieval. Information Retrieval, 7(1–2), 73–97.Meyer zu Eissen, S., & Stein, B. (2006). Intrinsic plagiarism detection. In M. Lalmas, A. MacFarlane, S. M. Rüger, A. Tombros, T. Tsikrika, & A. Yavlinsky (Eds.), Proceedings of the European conference on information retrieval (ECIR 2006), volume 3936 of Lecture Notes in Computer Science (pp. 565–569). Springer.Meyer zu Eissen, S., Stein, B., & Kulig, M. (2007). Plagiarism detection without reference collections. In R. Decker & H. J. Lenz (Eds.), Advances in data analysis (pp. 359–366), Springer.Och, F. J., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.Pinto, D., Juan, A., & Rosso, P. (2007). Using query-relevant documents pairs for cross-lingual information retrieval. In V. Matousek & P. Mautner (Eds.), Lecture Notes in Artificial Intelligence (pp. 630–637). Pilsen, Czech Republic.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to cross-lingual natural language tasks. Journal of Algorithms, 64(1), 51–60.Potthast, M. (2007). Wikipedia in the pocket-indexing technology for near-duplicate detection and high similarity search. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 909–909). ACM.Potthast, M., Stein, B., & Anderka, M. (2008). A Wikipedia-based multilingual retrieval model. In C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, & R. W. White (Eds.), 30th European conference on IR research, ECIR 2008, Glasgow , volume 4956 LNCS of Lecture Notes in Computer Science (pp. 522–530). Berlin: Springer.Pouliquen, B., Steinberger, R., & Ignat, C. (2003a). Automatic annotation of multilingual text collections with a conceptual thesaurus. In Proceedings of the workshop ’ontologies and information extraction’ at the Summer School ’The Semantic Web and Language Technology—its potential and practicalities’ (EUROLAN’2003) (pp. 9–28), Bucharest, Romania.Pouliquen, B., Steinberger, R., & Ignat, C. (2003b). Automatic identification of document translations in large multilingual document collections. In Proceedings of the international conference recent advances in natural language processing (RANLP’2003) (pp. 401–408). Borovets, Bulgaria.Stein, B. (2007). Principles of hash-based text retrieval. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 527–534). ACM.Stein, B. (2005). Fuzzy-fingerprints for text-based information retrieval. In K. Tochtermann & H. Maurer (Eds.), Proceedings of the 5th international conference on knowledge management (I-KNOW 05), Graz, Journal of Universal Computer Science. (pp. 572–579). Know-Center.Stein, B., & Anderka, M. (2009). Collection-relative representations: A unifying view to retrieval models. In A. M. Tjoa & R. R. Wagner (Eds.), 20th International conference on database and expert systems applications (DEXA 09) (pp. 383–387). IEEE.Stein, B., & Meyer zu Eissen, S. (2007). Intrinsic plagiarism analysis with meta learning. In B. Stein, M. Koppel, & E. Stamatatos (Eds.), SIGIR workshop on plagiarism analysis, authorship identification, and near-duplicate detection (PAN 07) (pp. 45–50). CEUR-WS.org.Stein, B., & Potthast, M. (2007). Construction of compact retrieval models. In S. Dominich & F. Kiss (Eds.), Studies in theory of information retrieval (pp. 85–93). Foundation for Information Society.Stein, B., Meyer zu Eissen, S., & Potthast, M. (2007). Strategies for retrieving plagiarized documents. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 825–826). ACM.Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., & Varga, D. (2006). The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. In Proceedings of the 5th international conference on language resources and evaluation (LREC’2006).Steinberger, R., Pouliquen, B., & Ignat, C. (2004). Exploiting multilingual nomenclatures and language-independent text features as an interlingua for cross-lingual text analysis applications. In Proceedings of the 4th Slovenian language technology conference. Information Society 2004 (IS’2004).Vinokourov, A., Shawe-Taylor, J., & Cristianini, N. (2003). Inferring a semantic representation of text via cross-language correlation analysis. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS-02: Advances in neural information processing systems (pp. 1473–1480). MIT Press.Yang, Y., Carbonell, J. G., Brown, R. D., & Frederking, R. E. (1998). Translingual information retrieval: Learning from bilingual corpora. Artificial Intelligence, 103(1–2), 323–345

    Cross-lingual C*ST*RD: English access to Hindi information

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    We present C*ST*RD, a cross-language information delivery system that supports cross-language information retrieval, information space visualization and navigation, machine translation, and text summarization of single documents and clusters of documents. C*ST*RD was assembled and trained within 1 month, in the context of DARPA’s Surprise Language Exercise, that selected as source a heretofore unstudied language, Hindi. Given the brief time, we could not create deep Hindi capabilities for all the modules, but instead experimented with combining shallow Hindi capabilities, or even English-only modules, into one integrated system. Various possible configurations, with different tradeoffs in processing speed and ease of use, enable the rapid deployment of C*ST*RD to new languages under various conditions

    Design, development and field evaluation of a Spanish into sign language translation system

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    This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE)

    Accuracy Bounds and Measurements of a Contactless Permittivity Sensor for Gases Using Synchronized Low-Cost mm-Wave Frequency Modulated Continuous Wave Radar Transceivers

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    A primary concern in a multitude of industrial processes is the precise monitoring of gaseous substances to ensure proper operating conditions. However, many traditional technologies are not suitable for operation under harsh environmental conditions. Radar-based time-of-flight permittivity measurements have been proposed as alternative but suffer from high cost and limited accuracy in highly cluttered industrial plants. This paper examines the performance limits of low-cost frequency-modulated continuous-wave (FMCW) radar sensors for permittivity measurements. First, the accuracy limits are investigated theoretically and the Cramér-Rao lower bounds for time-of-flight based permittivity and concentration measurements are derived. In addition, Monte-Carlo simulations are carried out to validate the analytical solutions. The capabilities of the measurement concept are then demonstrated with different binary gas mixtures of Helium and Carbon Dioxide in air. A low-cost time-of-flight sensor based on two synchronized fully-integrated millimeter-wave (MMW) radar transceivers is developed and evaluated. A method to compensate systematic deviations caused by the measurement setup is proposed and implemented. The theoretical discussion underlines the necessity of exploiting the information contained in the signal phase to achieve the desired accuracy. Results of various permittivity and gas concentration measurements are in good accordance to reference sensors and measurements with a commercial vector network analyzer (VNA). In conclusion, the proposed radar-based low-cost sensor solution shows promising performance for the intended use in demanding industrial applications

    No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data"

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    In a recently published PNAS article, we studied the identifiability of genomic samples using machine learning methods [Lippert et al., 2017]. In a response, Erlich [2017] argued that our work contained major flaws. The main technical critique of Erlich [2017] builds on a simulation experiment that shows that our proposed algorithm, which uses only a genomic sample for identification, performed no better than a strategy that uses demographic variables. Below, we show why this comparison is misleading and provide a detailed discussion of the key critical points in our analyses that have been brought up in Erlich [2017] and in the media. Further, not only faces may be derived from DNA, but a wide range of phenotypes and demographic variables. In this light, the main contribution of Lippert et al. [2017] is an algorithm that identifies genomes of individuals by combining multiple DNA-based predictive models for a myriad of traits

    The warm ionized medium in spiral galaxies

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    This article reviews observations and models of the diffuse ionized gas that permeates the disk and halo of our Galaxy and others. It was inspired by a series of invited talks presented during an afternoon scientific session of the 65th birthday celebration for Professor Carl Heiles held at Arecibo Observatory in August 2004. This review is in recognition of Carl's long standing interest in and advocacy for studies of the ionized as well as the neutral components of the interstellar medium.Comment: 29 pages, 19 figures; accepted by Reviews of Modern Physic

    Weaning practices in phenylketonuria vary between health professionals in Europe

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    Background: In phenylketonuria (PKU), weaning is considered more challenging when compared to feeding healthy infants. The primary aim of weaning is to gradually replace natural protein from breast milk or standard infant formula with solids containing equivalent phenylalanine (Phe). In addition, a Phe-free second stage L-amino acid supplement is usually recommended from around 6 months to replace Phe-free infant formula. Our aim was to assess different weaning approaches used by health professionals across Europe. Methods: A cross sectional questionnaire (survey monkey (R)) composed of 31 multiple and single choice questions was sent to European colleagues caring for inherited metabolic disorders (IMD). Centres were grouped into geographical regions for analysis. Results: Weaning started at 17-26 weeks in 85% (n=81/95) of centres, > 26 weeks in 12% (n=11/95) and 26 weeks. First solids were mainly low Phe vegetables (59%, n=56/95) and fruit (34%, n=32/95). A Phe exchange system to allocate dietary Phe was used by 52% (n=49/95) of centres predominantly from Northern and Southern Europe and 48% (n=46/95) calculated most Phe containing food sources (all centres in Eastern Europe and the majority from Germany and Austria). Some centres used a combination of both methods. A second stage Phe-free L-amino acid supplement containing a higher protein equivalent was introduced by 41% (n=39/95) of centres at infant age 26-36 weeks (mainly from Germany, Austria, Northern and Eastern Europe) and 37% (n=35/95) at infant age > 1y mainly from Southern Europe. 53% (n=50/95) of centres recommended a second stage Phe-free L-amino acid supplement in a spoonable or semi-solid form. Conclusions: Weaning strategies vary throughout European PKU centres. There is evidence to suggest that different infant weaning strategies may influence longer term adherence to the PKU diet or acceptance of Phe-free L-amino acid supplements; rendering prospective long-term studies important. It is essential to identify an effective weaning strategy that reduces caregiver burden but is associated with acceptable dietary adherence and optimal infant feeding development.Peer reviewe

    Early feeding practices in infants with phenylketonuria across Europe

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    Background: In infants with phenylketonuria (PKU), dietary management is based on lowering and titrating phenylalanine (Phe) intake from breast milk or standard infant formula in combination with a Phe-free infant formula in order to maintain blood Phe levels within target range. Professionals use different methods to feed infants with PKU and our survey aimed to document practices across Europe. Methods: We sent a cross sectional, survey monkey (R) questionnaire to European health professionals working in IMD. It contained 31 open and multiple-choice questions. The results were analysed according to different geographical regions. Results: Ninety-five centres from 21 countries responded. Over 60% of centres commenced diet in infants by age 10 days, with 58% of centres implementing newborn screening by day 3 post birth. At diagnosis, infant hospital admission occurred in 61% of metabolic centres, mainly in Eastern, Western and Southern Europe. Breastfeeding fell sharply following diagnosis with only 30% of women still breast feeding at 6 months. 53% of centres gave pre-measured Phe-free infant formula before each breast feed and 23% alternated breast feeds with Phe-free infant formula. With standard infant formula feeds, measured amounts were followed by Phe-free infant formula to satiety in 37% of centres (n = 35/95), whereas 44% (n = 42/95) advised mixing both formulas together. Weaning commenced between 17 and 26 weeks in 85% centres, >= 26 weeks in 12% and <17 weeks in 3%. Discussion: This is the largest European survey completed on PKU infant feeding practices. It is evident that practices varied widely across Europe, and the practicalities of infant feeding in PKU received little focus in the PKU European Guidelines (2017). There are few reports comparing different feeding techniques with blood Phe control, Phe fluctuations and growth. Controlled prospective studies are necessary to assess how different infant feeding practices may influence longer term feeding development.Peer reviewe

    Getting Past the Language Gap: Innovations in Machine Translation

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    In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT
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