“As such, adolescents may experience heightened emotional sensitivity to the communicative and interactive nature of social media.” “Frequent social media use may be associated with distinct changes in the developing brain in the amygdala (important for emotional learning and behavior) and the prefrontal cortex (important for impulse control, emotional regulation, and moderating social behavior), and could increase sensitivity to social rewards and punishments,” Murthy warned. Social media can severely affect that development.
That age is also where children’s sense of self-worth begins to manifest. Adolescents between the ages of 10 and 19, it says, are in a period where risk-taking behaviors reach their highest levels and when depression and other mental health issues often emerge. Of particular concern is the effect of social media on brain development. Okazaki, "Classias: a collection of machinelearning algorithms for classification," 2009.
Srebro, "Pegasos: Primal Estimated subGrAdient SOlver for SVM," in ICML '07: Proceedings of the 24th international conference on Machine learning. Kudo, "Mecab: Yet another partofspeech and morphological analyzer,". Pfeiffer, "Information extraction from mathematical texts by means of natural language processing techniques," in ACM Multimedia EMME Workshop, 2007, pp.
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Franke, "Mbase: Representing knowledge and context for the integration of mathematical software systems," Journal of Symbolic Computation, vol. Aizawa, "An approach to similarity search for mathematical expressions using MathML," in Towards digital mathematics library (DML), 2009, pp. Khiyal, "Math GO! prototype of a content based mathematical formula search engine," Journal of Theoretical and Applied Information Technology, vol.
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Misutka, "Indexing mathematical content using full text search engine," in WDS' 08 Proceedings of Contributed Papers: Part I Mathematics and Computer Sciences, 2008, pp. Miner, "Mathfind: a mathaware search engine," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, ser. Yamaguchi, "An integrated ocr software for mathematical documents and its output with accessibility," in Computers Helping People with Special Needs, ser. World Wide Web Consortium, "Mathematical markup language (mathml) version 2.0 (second edition),". "Information Processing Society of Japan,". Key words: Natural language processing, mathematical expressions, pattern matching, machine learning. The effectiveness of the proposed methods is shown through experiments by using the reference set. We then propose the use of two methods, pattern matching and machine learning based ones for the extraction task. We first define an extraction task and constructed a reference dataset of 100 Japanese scientific papers by hand.
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We present how to extract a natural language description, such as variable names or function definitions that refer to mathematical expressions with various experimental results. Since the superficial information of mathematical expressions is ambiguous, considering not only mathematical expressions but also the texts around them is necessary. We found a way to use mathematical search to provide better navigation for reading papers on computers. Manuscript accepted for publication January 10, 2011. Keisuke Yokoi 1, MinhQuoc Nghiem 2, Yuichiroh Matsubayashi 3, and Akiko Aizawa 4ġ Department of Computer Science, University of Tokyo, Hongo 7≣≡, Bunkyoku, Tokyo, Japan (email: Department of Informatics, The Graduate University for Advanced Studies, Tokyo, Japan (email: National Institute of Informatics, Tokyo, Japan (email: Department of Computer Science, University of Tokyo, Hongo 7≣≡, Bunkyoku, Tokyo, Japan and with National Institute of Informatics, Tokyo, Japan (email: received November 12, 2010. Contextual Analysis of Mathematical Expressions for Advanced Mathematical Search
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