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ارزیابی خودکار جویش‌گرهای ویدئویی حوزه وب فارسی بر اساس تجمیع آرا Automatic Evaluation of Video Search engines in Persian Web domain based on Majority Voting 1 1 امروزه رشد بسیار سریع اینترنت و نفوذ روزافزون آن در زندگی افراد باعث شده تا کاربران بسیاری برای رفع نیازهای روزمره خود به جویش‌گرها مراجعه کنند و این جویش‌گرها به توسعه و بهبود مستمر نیاز دارند. از‌این‌رو ارزیابی جویش‌گرها برای تعیین کارایی آنها اهمیت به‌سزایی دارد. در ایران نیز همانند سایر کشورها پژوهش‌های گسترده‌ای در زمینه ایجاد جویش‌گرهای خاص‌منظوره بومی انجام شده است. یکی از مهم‌ترین جویش‌گرهای خاص‌منظوره ایجاد‌شده، جویش‌گر ویدئویی است که وظیفه بازیابی ویدئوها از سطح وب را برعهده دارد. برای ارزیابی کیفیت این جویش‌گرها و بهبود مستمر آنها باید سطح خدمات‌دهی هر کدام از جویش‌گرها در مقایسه با دیگر جویش‌گرهای موجود مورد ارزیابی قرار گیرد. از آنجا که سرعت ارزیابی نقش مهمی در تعیین روند اصلاحات مورد نیاز دارد، بحث ارزیابی خودکار جویش‌گر‌ها بسیار پراهمیت خواهد شد. در این مقاله روشی مبتنی بر تجمیع آرا به‌منظور ارزیابی خودکار جویش‌گرهای ویدئویی ارائه شده است. تمرکز اصلی این روش بر روی حوزه وب فارسی بوده و با توسعه روشی نوین برای شباهت‌سنجی مبتنی بر محتوا براساس بردار‌های حرکت ویدئوها، سعی در ارزیابی جویش‌گرهای ویدئویی دارد. برای محک‌زدن روش معرفی‌شده، سازوکاری طراحی شد تا نتایج به‌دست‌آمده با نتایج حاصل از ارزیابی انسانی مورد مقایسه قرار گیرد. نتایج به‌دست‌آمده نشان‌دهنده میزان همبستگی بیش از 94% دو روش است که قابل اتکا‌بودن روش خودکار ارزیابی پیشنهادی را بیان می‌کند.   2 Today, the growth of the internet and its high influence in individuals’ life have caused many users to solve their daily needs by search engines and hence, the search engines need to be modified and continuously improved. Therefore, evaluating search engines to determine their performance is of paramount importance. In Iran, as well as other countries, extensive researches are being performed on search engines. To evaluate the quality of search engines and continually improve their performance, it is necessary to evaluate search engines and compare them to other existing ones. Since the speed plays an important role in the assessment of the performance, automatic search engine evaluation methods attracted grate attention. In this paper, a method based on the majority voting is proposed to assess the video search engines. We introduced a mechanism to assess the automatic evaluation method by comparing its results with the results obtained by human search engine evaluation. The results obtained, shows 94 % correlation of the two methods which indicate the reliability of automated approach. In general, the proposed method can be described in three steps. Step 1: Retrieve first k_retrieve results of n different video search engines and build the return result set for each written query. Step 2: Determine the level of relevance of each retrieved result from the search engines Step 3: Evaluating the search engines by computing different evaluation criteria based on decisions on relevance of the retrieved videos by each search engine  Clearly, the main part of any evaluation system with the goal of evaluating the accuracy of search engines is the second step. In this paper, we have tried to present a new solution based on the aggregation of votes in order to determine whether a result is relevant or not, as well as its level of relevance. For this purpose, for each query the return results from different search engines are compared with each other, and the result returned by more than m of the search engines (m ; and the result of which their URLs (after the normalization) are similar to the normalized URL from the m-1 of the other search engines, are considered as the relevant results. At the second level, the retrieved results will be compared in terms of content. In this way, after calculating the address-like similarity, all the results are transmitted to the motion vector extraction component to extract and store the motion vector. In the content based similarity algorithm, the set of motion vectors is initially considered as a sequence of motion vector. We, then, try to find the greatest similarity of the smaller sequence with the larger sequence. After this step, we will report the maximum similarity of the two videos. The process of finding the maximum similarity is that we consider a window with a smaller video sequence length. In this window we calculate and hold the similarity of two sequences. In the proposed method, after identifying the similarity between the return results of different search engines, their level is ranked at three different levels: "unrelated" (0), "quantitatively related" (1) and "related" (2). Since Google's search engine is currently the world’s largest and best-performing search engine, and most search engines have been compared to it, and are also trying to achieve the same function, the first five Google search engine results are get the minimum relevance, by default, "slightly related". Then the similarity module is used to evaluate the similarity of the retrieved n results of the tested search engines.   3 12 2017/12/6 1396/9/15 2018/09/15 1397/6/24 محمد مهدی یدالهی Mohammadmahdi Yadolahi مرکز تحقیقات مخابرات ایران ایران mm.yadollahi@itrc.ac.ir فرزاد زرگری Farzad Zargari مرکز تحقیقات مخابرات ایران ایران zargari@itrc.ac.ir مژگان فرهودی Mojgan Farhoodi مرکز تحقیقات مخابرات ایران ایران farhood@itrc.ac.ir Automatic assessment Video search engine Persian Web ارزیابی خودکار جویش‌گرهای ویدئویی وب فارسی [1] Moosavi Sobhan, Azimzadeh Masoumeh, Mahmo-udi Maryam, Yari Alireza, A Comprehens-ive and Effective Framework for the Assessment of Persian Search Engines, The 18th Annual National Conference of the Computer Society, Tehran, Esfand 1391.##[2] Azimzadeh Masoumeh, Somoori Shahriar, Yari Alireza, Qualitative and qualitative comparison of search engines in the Persian Web domain, 18th National Computer Society Conference, Tehran, Esfand 1391.##[3] R. Badie, M. Azimzadeh, M.S. Zahedi, S. Samuri, "Automatic evaluation of search engines: Using webpages' content, web graph link structure and websites' popularity" Seventh International Symposium on Telecommunica-tions ( IST2014), September 09-11, 2014.##[4] Maryam Mahmoudy, Mohammad Sadegh zahedi, Masomeh Azimzadeh, "Evalua-ting the retrieval effectiveness of search engines using Persian navigational queries", Seventh Interna-tional Symposium on Telecommunica-tions (IST2014), September 09-11, 2014.##[5] I. Soboroff, C. Nicholas, and P. Cahan, "Ranking retrieval systems without relevance judgments," in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 66-73.##[6] S. P. Harter, "Variations in relevance assessm-ents and the measurement of retrieval effective-ness," JASIS, vol. 47, pp. 37-49, 1996.##https://doi.org/10.1002/(SICI)1097-4571(199601)47:1<37::AID-ASI4>3.0.CO;2-3##[7] A. Spink and H. Greisdorf, "Regions and levels: measuring and mapping users' relevance judgments," Journal of the American Society for Information science and Technology, vol. 52, pp. 161-173, 2001.##https://doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1564>3.0.CO;2-L##[8] S. Wu and F. Crestani, "Methods for ranking information retrieval systems without relevance judgments," in Proceedings of the 2003 ACM symposium on Applied computing, 2003, pp. 811-816.##[9] J. Callan, M. Connell, and A. Du, "Automatic discovery of language models for text databases," in ACM SIGMOD Record, 1999, pp. 479-490.##[10] A. Chowdhury and I. Soboroff, "Automatic evaluation of world wide web search services," in Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002, pp. 421-422.##[11] S. M. Beitzel, E. C. Jensen, A. Chowdhury, and D. Grossman, "Using titles and category names from editor-driven taxonomies for automatic evaluation," in Proceedings of the twelfth international conference on Information and knowledge management, 2003, pp. 17-23.##[12] F. Can, R. Nuray, and A. B. Sevdik, "Automatic performance evaluation of Web search engines," Information processing & management, vol. 40, pp. 495-514, 2004.##[13] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickth-rough data as implicit feedback," in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 154-161.##[14] Y. Liu, Y. Fu, M. Zhang, S. Ma, and L. Ru, "Automatic search engine performance evaluation with click-through data analysis," in Proceedings of the 16th international conference on World Wide Web, 2007, pp. 1133-1134.##[15] Y. Liu, M. Zhang, L. Ru, and S. Ma, "Automatic query type identification based on click through information," in Information Retrieval Techno-logy, ed: Springer, 2006, pp. 593-600.##[16] T. Joachims, "Evaluating Retrieval Performance Using Clickthrough Data," ed: Citeseer, 2003.##[17] G. Mood, "Boes, Introduction to the theory of statistics," McCraw-Hill Statistics Series, 1974.##[18] H. Sharma and B. J. Jansen, "Automated evaluation of search engine performance via implicit user feedback," in Proceedings of the 28th annual international ACM SIGIR con-ference on Research and development in information retrieval, 2005, pp. 649-650.##[19] R. Ali and M. S. Beg, "Automatic performance evaluation of web search systems using rough set based rank aggregation," in Proceedings of the First International Conference on Intelligent Human Computer Interaction, 2009, pp. 344-358.##[20] R. Nuray and F. Can, "Automatic ranking of information retrieval systems using data fusion," Information processing & management, vol. 42, pp. 595-614, 2006.##[21] H. Sadeghi. "Automatic Performance Evaluation of Web search Engines using judgements of Meta search Engines", Online Information Review, ISSN:1468-4527,Emerald Publishing Limited, pp.957-971. (2011).##[22] Tawileh W, Griesbaum J, Mandl T. Evaluation of five web search engines in Arabic language. Proceedings of LWA. (2010).##[23] Lewandowski, Dirk. Evaluating the retrieval effectiveness of Web search engines using a representative query sample. Journal of the Association for Information Science and Technology (2015).##[24] Bar-Ilan J, Levene M. A method to assess search engine results. Online Information Review 35(6), 854-868. (2011).##[25] Keyvanpour M, alamdar F. Effective browsing of image search results via diversified visual summarization by Clustering. JSDP. 2012; 8 (2) :57-74##[1] موسوی سبحان، عظیم‌زاده معصومه، محمودی مریم، یاری علیرضا، ارائه چارچوبی جامع و کارا برای ارزیابی موتورهای جستجوی فارسی، هجدهمین کنفرانس ملی سالیانه انجمن کامپیوتر، تهران، اسفند 1391.##[2] عظیم‌زاده معصومه، سموری شهریار، یاری علیرضا، بررسی و مقایسه کیفی موتورهای جستجو در حوزه وب فارسی،‌ هجدهمین کنفرانس ملی سالیانه انجمن کامپیوتر، تهران، اسفند 1391.##[1] Moosavi Sobhan, Azimzadeh Masoumeh, Mahmo-udi Maryam, Yari Alireza, A Comprehens-ive and Effective Framework for the Assessment of Persian Search Engines, The 18th Annual National Conference of the Computer Society, Tehran, Esfand 1391.##[2] Azimzadeh Masoumeh, Somoori Shahriar, Yari Alireza, Qualitative and qualitative comparison of search engines in the Persian Web domain, 18th National Computer Society Conference, Tehran, Esfand 1391.##[3] R. Badie, M. Azimzadeh, M.S. Zahedi, S. Samuri, "Automatic evaluation of search engines: Using webpages' content, web graph link structure and websites' popularity" Seventh International Symposium on Telecommunica-tions ( IST2014), September 09-11, 2014.##[4] Maryam Mahmoudy, Mohammad Sadegh zahedi, Masomeh Azimzadeh, "Evalua-ting the retrieval effectiveness of search engines using Persian navigational queries", Seventh Interna-tional Symposium on Telecommunica-tions (IST2014), September 09-11, 2014.##[5] I. Soboroff, C. Nicholas, and P. Cahan, "Ranking retrieval systems without relevance judgments," in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 66-73.##[6] S. P. Harter, "Variations in relevance assessm-ents and the measurement of retrieval effective-ness," JASIS, vol. 47, pp. 37-49, 1996.##https://doi.org/10.1002/(SICI)1097-4571(199601)47:1<37::AID-ASI4>3.0.CO;2-3##[7] A. Spink and H. Greisdorf, "Regions and levels: measuring and mapping users' relevance judgments," Journal of the American Society for Information science and Technology, vol. 52, pp. 161-173, 2001.##https://doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1564>3.0.CO;2-L##[8] S. Wu and F. Crestani, "Methods for ranking information retrieval systems without relevance judgments," in Proceedings of the 2003 ACM symposium on Applied computing, 2003, pp. 811-816.##[9] J. Callan, M. Connell, and A. Du, "Automatic discovery of language models for text databases," in ACM SIGMOD Record, 1999, pp. 479-490.##[10] A. Chowdhury and I. Soboroff, "Automatic evaluation of world wide web search services," in Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002, pp. 421-422.##[11] S. M. Beitzel, E. C. Jensen, A. Chowdhury, and D. Grossman, "Using titles and category names from editor-driven taxonomies for automatic evaluation," in Proceedings of the twelfth international conference on Information and knowledge management, 2003, pp. 17-23.##[12] F. Can, R. Nuray, and A. B. Sevdik, "Automatic performance evaluation of Web search engines," Information processing & management, vol. 40, pp. 495-514, 2004.##[13] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickth-rough data as implicit feedback," in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 154-161.##[14] Y. Liu, Y. Fu, M. Zhang, S. Ma, and L. Ru, "Automatic search engine performance evaluation with click-through data analysis," in Proceedings of the 16th international conference on World Wide Web, 2007, pp. 1133-1134.##[15] Y. Liu, M. Zhang, L. Ru, and S. Ma, "Automatic query type identification based on click through information," in Information Retrieval Techno-logy, ed: Springer, 2006, pp. 593-600.##[16] T. Joachims, "Evaluating Retrieval Performance Using Clickthrough Data," ed: Citeseer, 2003.##[17] G. Mood, "Boes, Introduction to the theory of statistics," McCraw-Hill Statistics Series, 1974.##[18] H. Sharma and B. J. Jansen, "Automated evaluation of search engine performance via implicit user feedback," in Proceedings of the 28th annual international ACM SIGIR con-ference on Research and development in information retrieval, 2005, pp. 649-650.##[19] R. Ali and M. S. Beg, "Automatic performance evaluation of web search systems using rough set based rank aggregation," in Proceedings of the First International Conference on Intelligent Human Computer Interaction, 2009, pp. 344-358.##[20] R. Nuray and F. Can, "Automatic ranking of information retrieval systems using data fusion," Information processing & management, vol. 42, pp. 595-614, 2006.##[21] H. Sadeghi. "Automatic Performance Evaluation of Web search Engines using judgements of Meta search Engines", Online Information Review, ISSN:1468-4527,Emerald Publishing Limited, pp.957-971. (2011).##[22] Tawileh W, Griesbaum J, Mandl T. Evaluation of five web search engines in Arabic language. Proceedings of LWA. (2010).##[23] Lewandowski, Dirk. Evaluating the retrieval effectiveness of Web search engines using a representative query sample. Journal of the Association for Information Science and Technology (2015).##[24] Bar-Ilan J, Levene M. A method to assess search engine results. Online Information Review 35(6), 854-868. (2011).##[25] Keyvanpour M, alamdar F. Effective browsing of image search results via diversified visual summarization by Clustering. JSDP. 2012; 8 (2) :57-74## ##

شبکه عصبی پیچشی با پنجره‌های قابل تطبیق برای بازشناسی گفتار Adaptive Windows Convolutional Neural Network for Speech Recognition 1 1 در حالی‌که سامانه‌های بازشناسی گفتار به‌طور پیوسته در حال ارتقا می‌باشند و شاهد استفاده گسترده از آن‌ها می‌باشیم، اما دقت این سامانه‌ها فاصله زیادی نسبت به توان بازشناسی انسان دارد و در شرایط ناسازگار این فاصله افزایش می‌یابد. یکی از علل اصلی این مسئله تغییرات زیاد سیگنال گفتار است. در سال‌های اخیر، استفاده از شبکه‌های عصبی عمیق در ترکیب با مدل مخفی مارکف، موفقیت‌های قابل توجهی در حوزه پردازش گفتار داشته ‌است. این مقاله به‌دنبال مدل‌کردن بهتر گفتار با استفاده از تغییر ساختار در شبکه عصبی پیچشی عمیق است؛ به‌نحوی که با تنوعاتِ بیان گویندگان در سیگنال گفتار  منطبق‌تر شود. در این راه، مدل‌های موجود و انجام استنتاج بر روی آن‌ها را بهبود و گسترش خواهیم داد. در این مقاله با ارائه شبکه پیچشی عمیق با پنجره­های قابل تطبیق سامانه بازشناسی گفتار را نسبت به تفاوت بیان در بین گویندگان و تفاوت در بیان‌های یک گوینده مقاوم خواهیم کرد. تحلیل­ها و نتایج آزمایش‌های صورت‌گرفته بر روی دادگان گفتار فارس­دات و TIMIT نشان داد که روش پیشنهادی خطای مطلق بازشناسی واج را نسبت به شبکه پیچشی عمیق به­ترتیب به میزان 2/1 و 1/1 درصد کاهش می‌دهد که این مقدار در مسئله بازشناسی گفتار مقدار قابل توجهی است.  2 Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov model (HMM) leads to considerable performance achievement in speech recognition problem because deep networks model complex correlations between features. The main aim of this paper is to achieve a better acoustic modeling by changing the structure of deep Convolutional Neural Network (CNN) in order to adapt speaking variations. In this way, existing models and corresponding inference task have been improved and extended. Here, we propose adaptive windows convolutional neural network (AWCNN) to analyze joint temporal-spectral features variation. AWCNN changes the structure of CNN and estimates the probabilities of HMM states. We propose adaptive windows convolutional neural network in order to make the model more robust against the speech signal variations for both single speaker and among various speakers. This model can better model speech signals. The AWCNN method applies to the speech spectrogram and models time-frequency varieties. This network handles speaker feature variations, speech signal varieties, and variations in phone duration. The obtained results and analysis on FARSDAT and TIMIT datasets show that, for phone recognition task, the proposed structure achieves 1.2%, 1.1% absolute error reduction with respect to CNN models respectively, which is a considerable improvement in this problem. Based on the results obtained by the conducted experiments, we conclude that the use of speaker information is very beneficial for recognition accuracy. 13 30 2017/12/62018/04/4 1397/1/15 2018/09/152018/12/17 1397/9/26 تکتم ذوقی Toktam Zoughi دانشگاه صنعتی امیرکبیر ایران TOKTAM.ZOUGHI@GMAIL.COM محمد مهدی همایون پور Mohammad Mehdi Homayounpour دانشگاه صنعتی امیرکبیر ایران homayoun@aut.ac.ir Speech recognition deep neural network Convolutional neural network Adaptive windows convolutional neural network بازشناسی گفتار شبکه عصبی عمیق شبکه عصبی پیچشی پنجره‌های قابل تطبیق [1] Bi Jen Khan, J. Sheykhzadegan, "Persian speech dataset", in Machine Translation in Persian, 2006, pp. 261-247.##[2] B. BabaAli, "A state-of-the-art and efficient framework for Persian speech recognition", Signal and Data Processing, Vol. 13, No. 3, pp. 1-13, 2015.##[3] S. Z. Seyyedsalehi, and A. Seyyedsalehi, "Improving the nonlinear manifold separator model to the face recognition by a single image of per person." 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Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, 2012.##[21] A. R. Mohamed, G. Hinton and G. Penn, "Understanding how deep belief networks perform acoustic modelling," in IEEE Interna-tional Conference on Acoustics, Speech and Signal Processing, 2012, pp. 4273–4276.##[22] R. Salakhutdinov and G. Hinton, "An Efficient Learning Procedure for Deep Boltzmann Machines," Neural Computation, vol. 24, no. 8, pp. 1967–2006, 2012.##[23] R. Salakhutdinov, "Learning deep generative models‎," PHD thesis, Toronto, Ont., Canada‎, 2009.##[24] G. E. Hinton, "Training products of experts by minimizing contrastive divergence," Neural Computation, vol. 14, no. 8, pp. 1771–1800, 2002.##[25] G. E. Hinton, S. Osindero, and Y. W. 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Kingsbury, "Audio-visual deep learning for noise robust speech recognition," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7596–7599.##[31] A. Maas and Q. Le, "Recurrent Neural Networks for Noise Reduction in Robust ASR.," in Interspeech, 2012, pp. 3–6.##[32] H. Bourlard and N. Morgan, "Continuous speech recognition by connectionist statistical methods," IEEE Transactions on Neural Net-works, vol. 4, no. 6, pp. 893–909, 1993.##[33] A. J. Robinson, G. D. Cook, D. P. W. Ellis, E. Fosler-Lussier, S. J. Renals, and D. A. G. Williams, "Connectionist speech recognition of Broadcast News," Speech Communication, vol. 37, no. 1–2, pp. 27–45, 2002.##[34] Y. H. Sung and D. Jurafsky, "Hidden conditional random fields for phone recognition," in IEEE Workshop on Automatic Speech Recognition and Understanding, 2009, pp. 107–112.##[35] T. N. Sainath, A. R. Mohamed, B. Kingsbury and B. Ramabhadran, "Deep convolutional neural networks for LVCSR," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 8614–8618.##[36] T. N. Sainath, B. Kingsbury, G. Saon, H. Soltau, A.-R. Mohamed, G. Dahl, and B. Ramabhadran, "Deep Convolutional Neural Networks for Large-scale Speech Tasks.," Neural networks, vol. 64, pp. 39–48, Sep. 2014.##[37] T. N. Sainath, B. Kingsbury, H. Soltau and B. Ramabhadran, "Optimization techniques to improve training speed of deep neural networks for large speech tasks," IEEE Transactions on Audio, Speech and Language Processing, vol. 21, no. 11, pp. 2267–2276, 2013.##[38] H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical represent-tations," in Proceedings of the 26th Annual International Conference on Machine Learning, 2009, vol. 2008, pp. 1–8.##[39] O. Abdel-Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. 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ارایه یک روش جدید انتشار داده‌ها با حفظ محرمانگی با هدف بهبود دقّت طبقه‌‌بندی روی داده‌های گمنام A New Privacy Preserving Data Publishing Technique Conserving Accuracy of Classification on Anonymized Data 1 1 با توسعه روزافزون خدمات دولت الکترونیکی، اطلاعات شخصی افراد در قالب پایگاه‌های داده در دستگاه‌ها و ارگان‌های دولتی و خصوصی ذخیره شده است. در بسیاری از موارد برای پردازش و استخراج دانش از این منابع داده بزرگ و با‌ارزش، نیاز به انتشار منابع داده و در‌اختیار‌گذاشتن اطلاعات به سایر نهادها و شرکت‌ها پدید می‌آید که این امر موجب ایجاد چالش‌‌های امنیتی در نقض حریم خصوصی افراد می‌شود. در این مقاله ضمن بررسی کامل پیشینه پژوهش، حفظ محرمانگی در انتشار داده‌ها، یک روش کارآمد برای گمنام‌سازی ارائه می‌شود که هدف آن حفظ دقت طبقه‌بندی روی داده‌های گمنام است. این روش با بهره‌گیری از درخت تصمیم از انتشار اطلاعاتی که تأثیر کمی بر سودمندی داده‌های خروجی دارد و حذف آن‌ها موجب تأمین محرمانگی می‌شود، جلوگیری می‌کند. یکی از چالش‌های طرح‌‌هایی که از عمل‌گر گمنام‌سازی عمومی‌سازی استفاده می‌کنند، نیازمندی به ساخت درخت طبقه‌‌بندی برای هر شبه‌شناسه است که بیش‌تر به‌صورت خودکار صورت می‌گرفت. در طرح پیشنهادی نیازی به ساخت درخت طبقه‌‌بندی نیست. نتایج شبیه‌‌سازی و ارزیابی‌های انجام‌‌شده نشان می‌دهد، میان دقت الگوریتم‌های طبقه‌‌بندی که روی مجموعه‌داده استاندارد گمنام‌شده توسط این روش و مجموعه‌داده اولیه آموزش دیده‌اند، تفاوت اندکی وجود دارد.   2 Data collection and storage has been facilitated by the growth in electronic services, and has led to recording vast amounts of personal information in public and private organizations databases. These records often include sensitive personal information (such as income and diseases) and must be covered from others access. But in some cases, mining the data and extraction of knowledge from these valuable sources, creates the need for sharing them with other organizations. This would bring security challenges in user’s privacy. The concept of privacy is described as sharing of information in a controlled way. In other words, it decides what type of personal information should be shared and which group or person can access and use it. “Privacy preserving data publishing” is a solution to ensure secrecy of sensitive information in a data set, after publishing it in a hostile environment. This process aimed to hide sensitive information and keep published data suitable for knowledge discovery techniques. Grouping data set records is a broad approach to data anonymization. This technique prevents access to sensitive attributes of a specific record by eliminating the distinction between a number of data set records. So far a large number of data publishing models and techniques have been proposed but their utility is of concern when a high privacy requirement is needed. The main goal of this paper to present a technique to improve the privacy and performance data publishing techniques. In this work first we review previous techniques of privacy preserving data publishing and then we present an efficient anonymization method which its goal is to conserve accuracy of classification on anonymized data. The attack model of this work is based on an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. Our privacy model and technique uses a decision tree to prevent publishing of information that removing them provides privacy and has little effect on utility of output data. The presented idea of this paper is an extension of the work presented in [20]. Experimental results show that classifiers trained on the transformed data set achieving similar accuracy as the ones trained on the original data set. 31 46 2017/12/62018/04/42017/12/12 1396/9/21 2018/09/152018/12/172018/07/25 1397/5/3 رضا ابراهیمی آتانی Reza Ebrahimi Atani دانشگاه گیلان ایران rebrahimi@guilan.ac.ir مهدی صادق پور Mehdi Sadeghpour دانشگاه گیلان ایران mehdi.sadeghpour@live.com Privacy preservation Data sharing Anonymization Classification Decision tree Suppression حفظ محرمانگی طبقه‌‌بندی گمنام‌سازی درخت تصمیم عمل‌گر فرونشانی [1] B. C. M. Fung, K. Wang, A. Wai-Chee Fu and P. S. Yu, (2010), Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques, Chapman and Hall/CRC.##[2] J. Bennett and S. Lanning, (2007), "The Netflix Prize", Proceedings of the KDD Cup Workshop, pp. 3-6.##[3] D. Nettleton, (2014), "Data Privacy and Privacy-Preserving Data Publishing," in Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects, Morgan Kaufmann, pp. 266-277.##[4] B. Fung, K. Wang and P. Yu, (2010), "Privacy-Preserving Data Publishing: A Survey of Recent Developments", ACM Computing Surveys, vol. 42, no. 4,##[5] L. Sweeney, (2002), "k-Anonymity: A Model for Protecting Privacy", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570.##https://doi.org/10.1142/S0218488502001648##[6] K. S. Babu, (2013), Utility-Based Privacy Preserving Data Publishing, PhD thesis, National Institute of Technology Rourkela.##[7] N. Mohammed, B. C. M. Fung, P. C. K. Hung and C. K. Lee, (2009), "Healthcare Data: A Case Study on the Blood Transfusion Service", Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1285-1294.##[8] D. Dou and S. Coulondre, (2012), "Detecting Privacy Violations in Multiple Views Publishing," in Database and Expert Systems Applications, Springer-Verlag Berlin Heidelberg, 506–513.##[9] A. Anjum and G. Raschia, (2013), "Anonymizing Sequential Releases under Arbitrary Updates", Proceedings of the Joint EDBT/ICDT 2013 Workshops, pp. 145-154.##[10] B. Fung, K. Wang and P. Yu, (2007), "Anonymizing Classification Data for Privacy Preservation", IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 5, pp. 711-725.##[11] V. S. Susan and T. Christopher, (2014), "A Survey on Privacy Preservation in Data Publishing", International Journal of Computer Science and Mobile Computing, vol. 3, no. 3, pp. 188-193.##[12] T. Dalenius, (1977), "Towards a Methodology for Statistical Disclosure Control", Statistik Tidskrift, vol. 15, 429–222.##[13] C. Dwork, (2006), "Differential Privacy," in Automata, Languages and Programming, Springer Berlin Heidelberg, pp. 1-12.##[14] K. Wang and B. C. M. Fung, (2006), "Anonymizing sequential releases", Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 414-423.##[15] A. Machanavajjhala, D. Kifer, J. Gehrke and M. Venkitasubramaniam, (2007), "l-diversity: Privacy beyond k-anonymity", ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, article 3.##[16] N. Li, T. Li and S. Venkatasubramanian, (2007), "t-Closeness: Privacy Beyond k-Anonymity and l-Diversity", IEEE 23rd International Conference on Data Engineering, pp. 106 - 115.##[17] Y. Rubner, C. Tomasi and L. J. Guibas, (2000), "The Earth Mover's Distance as a Metric for Image Retrieval", International Journal of Computer Vision, vol. 40, no. 2, pp. 99 - 121.##[18] N. Li, T. Li and S. Venkatasubramanian, (2010), "Closeness A New Privacy Measure for Data Publishing", IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 943-956.##[19] M. E. Nergiz, M. Atzori and C. Clifton, (2007), "Hiding the Presence of Individuals from Shared Databases", InProc. of ACM International Conference on Management of Data, pp. 665-676.##[20] A. S. Sattar, J. Li, X. Ding, J. Liu and M. Vincent, (2013), "A general framework for privacy preserving data publishing", Knowledge-Based Systems, vol. 54, 276–287.##[21] K. Wang, P. Yu and S. Chakraborty, (2004), "Bottom-Up Generalization: A Data Mining Solution to Privacy Protection", Fourth IEEE International Conference on Data Mining, pp. 249 - 256.##[22] B. Fung, K. Wang and Y. P.S, (2005), "Top-Down Specialization for Information and Privacy Preservation", Proc. 21st International Conference on Data Engineering, pp. 205-216.##[23] S. Kisilevich, L. Rokach, Y. Elovici and B. Shapira, (2010), "Efficient Multidimensional Suppression for K-Anonymity", IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 3, pp. 334 - 347.##[24] A. Hussien, N. Hamza and A. Hefny, (2013), "Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing", Journal of Information Security, vol. 4, no. 2, pp. 101-112.##[25] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, (2009), "The WEKA Data Mining Software: An Update", ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18.##[26] "Taxonomy trees of the Adult data set", [Online]. Available: http://ddm.cs.sfu.ca/dmsoft/Privacy/products/adultHierarchy.txt. [Accessed 8 May 2016].##[27] "UCI Machine Learning Repository: Adult Data Set", [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Adult. [Accessed 8 May 2016].##[28] M. Nergiz, C. Clifton and A. Nergiz, (2009), "Multirelational k-Anonymity", IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 8, pp. 1104-1117.##[29] Mehdi Sadeghpour, "Privacy Preserving Data Publishing using Group Based Anonymization", MSc thesis in Software engineering, University of Guilan, 2015.##[1] B. C. M. Fung, K. Wang, A. Wai-Chee Fu and P. S. Yu, (2010), Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques, Chapman and Hall/CRC.##[2] J. Bennett and S. Lanning, (2007), "The Netflix Prize", Proceedings of the KDD Cup Workshop, pp. 3-6.##[3] D. Nettleton, (2014), "Data Privacy and Privacy-Preserving Data Publishing," in Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects, Morgan Kaufmann, pp. 266-277.##[4] B. Fung, K. Wang and P. Yu, (2010), "Privacy-Preserving Data Publishing: A Survey of Recent Developments", ACM Computing Surveys, vol. 42, no. 4,##[5] L. Sweeney, (2002), "k-Anonymity: A Model for Protecting Privacy", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570.##https://doi.org/10.1142/S0218488502001648##[6] K. S. Babu, (2013), Utility-Based Privacy Preserving Data Publishing, PhD thesis, National Institute of Technology Rourkela.##[7] N. Mohammed, B. C. M. Fung, P. C. K. Hung and C. K. Lee, (2009), "Healthcare Data: A Case Study on the Blood Transfusion Service", Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1285-1294.##[8] D. Dou and S. Coulondre, (2012), "Detecting Privacy Violations in Multiple Views Publishing," in Database and Expert Systems Applications, Springer-Verlag Berlin Heidelberg, 506–513.##[9] A. Anjum and G. Raschia, (2013), "Anonymizing Sequential Releases under Arbitrary Updates", Proceedings of the Joint EDBT/ICDT 2013 Workshops, pp. 145-154.##[10] B. Fung, K. Wang and P. Yu, (2007), "Anonymizing Classification Data for Privacy Preservation", IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 5, pp. 711-725.##[11] V. S. Susan and T. Christopher, (2014), "A Survey on Privacy Preservation in Data Publishing", International Journal of Computer Science and Mobile Computing, vol. 3, no. 3, pp. 188-193.##[12] T. Dalenius, (1977), "Towards a Methodology for Statistical Disclosure Control", Statistik Tidskrift, vol. 15, 429–222.##[13] C. Dwork, (2006), "Differential Privacy," in Automata, Languages and Programming, Springer Berlin Heidelberg, pp. 1-12.##[14] K. Wang and B. C. M. Fung, (2006), "Anonymizing sequential releases", Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 414-423.##[15] A. Machanavajjhala, D. Kifer, J. Gehrke and M. Venkitasubramaniam, (2007), "l-diversity: Privacy beyond k-anonymity", ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, article 3.##[16] N. Li, T. Li and S. Venkatasubramanian, (2007), "t-Closeness: Privacy Beyond k-Anonymity and l-Diversity", IEEE 23rd International Conference on Data Engineering, pp. 106 - 115.##[17] Y. Rubner, C. Tomasi and L. J. Guibas, (2000), "The Earth Mover's Distance as a Metric for Image Retrieval", International Journal of Computer Vision, vol. 40, no. 2, pp. 99 - 121.##[18] N. Li, T. Li and S. Venkatasubramanian, (2010), "Closeness A New Privacy Measure for Data Publishing", IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 943-956.##[19] M. E. Nergiz, M. Atzori and C. Clifton, (2007), "Hiding the Presence of Individuals from Shared Databases", InProc. of ACM International Conference on Management of Data, pp. 665-676.##[20] A. S. Sattar, J. Li, X. Ding, J. Liu and M. Vincent, (2013), "A general framework for privacy preserving data publishing", Knowledge-Based Systems, vol. 54, 276–287.##[21] K. Wang, P. Yu and S. Chakraborty, (2004), "Bottom-Up Generalization: A Data Mining Solution to Privacy Protection", Fourth IEEE International Conference on Data Mining, pp. 249 - 256.##[22] B. Fung, K. Wang and Y. P.S, (2005), "Top-Down Specialization for Information and Privacy Preservation", Proc. 21st International Conference on Data Engineering, pp. 205-216.##[23] S. Kisilevich, L. Rokach, Y. Elovici and B. Shapira, (2010), "Efficient Multidimensional Suppression for K-Anonymity", IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 3, pp. 334 - 347.##[24] A. Hussien, N. Hamza and A. Hefny, (2013), "Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing", Journal of Information Security, vol. 4, no. 2, pp. 101-112.##[25] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, (2009), "The WEKA Data Mining Software: An Update", ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18.##[26] "Taxonomy trees of the Adult data set", [Online]. Available: http://ddm.cs.sfu.ca/dmsoft/Privacy/products/adultHierarchy.txt. [Accessed 8 May 2016].##[27] "UCI Machine Learning Repository: Adult Data Set", [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Adult. [Accessed 8 May 2016].##[28] M. Nergiz, C. Clifton and A. Nergiz, (2009), "Multirelational k-Anonymity", IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 8, pp. 1104-1117.##[29] مهدی صادق پور، " حفظ محرمانگی در انتشار داده ها به وسیله گم نام سازی دسته‌ای"، پایان نامه کارشناسی ارشد مهندسی کامپیوتر – نرم افزار، دانشگاه گیلان، 1394.##[29] Mehdi Sadeghpour, "Privacy Preserving Data Publishing using Group Based Anonymization", MSc thesis in Software engineering, University of Guilan, 2015.## ##

امید ریاضی نرخ پوشش برای ماتریس‌های هلمن Expected coverage rate for the Hellman matrices 1 1 مصالحه حافظه‌زمان یک الگوریتم احتمالاتی برای وارون‌کردن توابع یک‌طرفه، با استفاده از داده‌های از پیش محاسبه‌شده است. هلمن در سال ۱۹۸۰ این روش را معرفی کرد و یک کران پایین برای احتمال موفقیت آن به‌دست آورد. پس از آن نیز تحلیل‌های پژوهش‌گران برای بررسی احتمال موفقیت، بر اساس همین کران پایین بوده است. در این مقاله، ابتدا به بررسی امید ریاضی نرخ پوشش یک ماتریس هلمن می‌پردازیم؛ سپس، این نرخ را برای ماتریس‌های هلمنی که فقط از یک زنجیره تشکیل شده‌اند، محاسبه کرده‌ایم. نشان داده‌ایم که امید ریاضی نرخ پوشش برای چنین ماتریس‌هایی بیشینه و برابر با ۸۵/۰ است. در ادامه، روش‌هایی برای تخمین دقیق‌تر این نرخ ارائه، و با روش هلمن مقایسه و در‌نهایت، ماتریس‌های هلمنی را معرفی کرده‌ایم که فقط از یک زنجیره تشکیل شده‌اند؛ ولی طول این زنجیره مقداری ثابت نیست و تا رسیدن به نخستین تکرار ادامه می‌یابد. به‌صورت نظری و عملی نشان داده‌ایم که احتمال موفقیت برای چنین ماتریس‌هایی بیشتر از روش هلمن است.   2 Hellman’s time-memory trade-off is a probabilistic method for inverting one-way functions, using pre-computed data. Hellman introduced this method in 1980 and obtained a lower bound for the success probability of his algorithm.  After that, all further analyses of researchers are based on this lower bound. In this paper, we first studied the expected coverage rate (ECR) of the Hellman matrices, which are constructed by a single chain. We showed that the ECR of such matrices is maximum and equal to 0.85. In this process, we find out that there exists a gap between the Hellman’s lower bound and experimental coverage rate of a Hellman matrix. Specifically, this gap is larger, when considering the Hellman matrices constructed with one single chain. So, we are investigated to obtain an accurate formula for the ECR of a Hellman matrix. Subsequently, we presented a new formula that estimate the ECR of a Hellman matrix more accurately than the Hellman’s lower bound. We showed that the given formula is closely match experimental data. In the last, we introduced a new method to construct matrices which have much more ECR than Hellman matrices. In fact, each matrix in this new method is constructed with one single chain, which is non-repeating trajectory from a random point. So, this approach result in a number of matrices that each one contains a chain with variable length. The main advantage of this method is that we have more probability of success than Hellman method, however online time and memory requirements are increased. We have also verified theory of this new method with experimental results.   47 58 2017/12/62018/04/42017/12/122017/12/7 1396/9/16 2018/09/152018/12/172018/07/252018/07/25 1397/5/3 ناصرحسین غروی Naser Hosein Gharavi دانشگاه جامع امام حسین (ع) ایران hgharavi@ihu.ac.ir عبدالرسول میرقدری Abdorasool Mirqadri دانشگاه جامع امام حسین (ع) ایران amrghdri@ihu.ac.ir محمد عبدالهی ازگمی Mohammad Abdollahi azgomi دانشگاه علم و صنعت ایران ایران azgomi@iust.ac.ir سید احمد موسوی Sayyed Ahmad Mousavi دانشگاه شهید باهنر کرمان ایران s.a.mousavi@math.uk.ac.ir Time-Memory Trade-off one way function Hellman matrix Expected coverage rate مصالحه حافظه‌زمان تابع یک‌طرفه ماتریس هلمن امید ریاضی نرخ پوشش [1] E. Barkan, E. Biham and A. Shamir, "Rigorous bounds on cryptanalytic time/memory tradeoffs", in Advances in Cryptology: Proceedings of Crypto, LNCS, 2006, 4117, pp. 1–21.##[2] J. Borst, "Block Ciphers: Design, Analysis and Side-Channel Analysis", PhD thesis, Katholieke Universiteit Leuven, 2001.##[3] D.E. Denning, Cryptography and data security, Addison-Wesley, 1982.##[4] P. Flajolet, and A. Odlyzko, "Random mapping statistics", Advances in Cryptology, Proceedings of Eurocrypt'89, LNCS, 1990, 434, pp. 329–354.##[5] P. Flajolet, and R. Sedgewick, An introduction to analysis of algorithms, Addison-Wesley, 2013.##[6] B. Harris, "Probability distributions related to random mappings", The Annals of Mathematical Statistics, pp. 1045–1062, 1960.##[7] M. Hellman, "A cryptanalytic time-memory trade-off", IEEE Transactions on Information Theory IT, vol. 26, pp. 401–406, 1980.##[8] J. Hong, and S. Moon, "A comparison of cryptanalytic tradeoff algorithms", Journal of cryptology, vol. 26 (4), pp. 559-637, 2013.##[9] J. Hong and B.I. Kim, "Performance comparison of cryptanalytic time memory data tradeoff methods", Bull. Korean Math. Soc., vol. 53(5), pp. 1439-1446, 2016.##[10] V. Kolchin, "Random mapp-ings", Translations series in mathematics and engineering, Optimization Software, Inc., Publications Division, 1986.##[11] K. Kusuda, and T. Matsumoto, "Optimization of time-memory trade-off cryptanalysis and is application to DES", FEAL-32, and Skipjack, E-79A, pp. 35–48, 1996.##[12] G. W. Lee, and J. Hong, "Comparison of perfect table cryptanalytic tradeoff algorithms", Designs, Codes and Cryptography, pp. 473-523, 2016.##[13] R.C. Phan, "Mini advanced encryption standard (mini-AES): a testbed for cryptanalysis students", Cryptologia, vol. 26(4), pp. 283-306, 2002.##[14] Ph. Oechslin, "Making a faster cryptanaly-tic time-memory trade-off", Annual International Cryptology Conference, Springer Berlin Heidelberg, 2003.##[1] E. Barkan, E. Biham and A. Shamir, "Rigorous bounds on cryptanalytic time/memory tradeoffs", in Advances in Cryptology: Proceedings of Crypto, LNCS, 2006, 4117, pp. 1–21.##[2] J. Borst, "Block Ciphers: Design, Analysis and Side-Channel Analysis", PhD thesis, Katholieke Universiteit Leuven, 2001.##[3] D.E. Denning, Cryptography and data security, Addison-Wesley, 1982.##[4] P. Flajolet, and A. Odlyzko, "Random mapping statistics", Advances in Cryptology, Proceedings of Eurocrypt'89, LNCS, 1990, 434, pp. 329–354.##[5] P. Flajolet, and R. Sedgewick, An introduction to analysis of algorithms, Addison-Wesley, 2013.##[6] B. Harris, "Probability distributions related to random mappings", The Annals of Mathematical Statistics, pp. 1045–1062, 1960.##[7] M. Hellman, "A cryptanalytic time-memory trade-off", IEEE Transactions on Information Theory IT, vol. 26, pp. 401–406, 1980.##[8] J. Hong, and S. Moon, "A comparison of cryptanalytic tradeoff algorithms", Journal of cryptology, vol. 26 (4), pp. 559-637, 2013.##[9] J. Hong and B.I. Kim, "Performance comparison of cryptanalytic time memory data tradeoff methods", Bull. Korean Math. Soc., vol. 53(5), pp. 1439-1446, 2016.##[10] V. Kolchin, "Random mapp-ings", Translations series in mathematics and engineering, Optimization Software, Inc., Publications Division, 1986.##[11] K. Kusuda, and T. Matsumoto, "Optimization of time-memory trade-off cryptanalysis and is application to DES", FEAL-32, and Skipjack, E-79A, pp. 35–48, 1996.##[12] G. W. Lee, and J. Hong, "Comparison of perfect table cryptanalytic tradeoff algorithms", Designs, Codes and Cryptography, pp. 473-523, 2016.##[13] R.C. Phan, "Mini advanced encryption standard (mini-AES): a testbed for cryptanalysis students", Cryptologia, vol. 26(4), pp. 283-306, 2002.##[14] Ph. Oechslin, "Making a faster cryptanaly-tic time-memory trade-off", Annual International Cryptology Conference, Springer Berlin Heidelberg, 2003.## ##

توسعه سامانه مکاترونیکی بلادرنگ سنجش استرس، مبتنی بر سیگنال‌های حیاتی Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals 1 1 کمتر کسی در دنیای پرتلاطم و پرتنش امروز، با واژه استرس بیگانه است؛ به‎طوری‎که در برخی مواقع، استرس تبدیل به بخشی از زندگی انسان شده‌است. استرسِ بیش از حد، باعث بروز مشکلاتی می‌شود که علاوه­بر اثرات روانی، پیامدهای جسمی بی‌شماری از جمله سکته‌های مغزی، قلبی، فشارخون و غیره را دارد و هیچ عضو یا ارگانی از بدن انسان از تأثیرات آن در امان نیست. هدف این پژوهش، طرّاحی و ساخت دستگاهی است که با استفاده از سیگنال‎های هدایت الکتریکی پوست (GSR) و فتوپلتیسموگرافی (PPG) بتواند میزان استرس فرد را به‌صورت شاخص پیوسته بیان کند. سخت‎افزار این دستگاه مبتنی بر پردازنده ARM و رابط کاربری آن با زبان C++ برنامه‌نویسی شده‌است. به‌منظور سنجش میزان استرس، الگو‎سازی با استفاده از شبکه عصبی مصنوعی MLP و شبکه فازی-عصبی تطبیقی (ANFIS) انجام، که در بهترین حالت در الگو‎سازی با ANFIS، دقت %91/92، و میانگین خطای 007/0 حاصل شده‌است.   2 Stress has affected human’s lives in many areas, today. Stress can adversely affect human’s health to such a degree as to either cause death or indicate a major contributor to death. Therefore, in recent years, some researchers have focused to developing systems to detect stress and then presenting viable solutions to manage this issue. Generally, stress can be identified through three different methods including (1) Psychological Evaluation, (2) Behavioral Responses and finally (3) Physiological Signals. Physiological signals are internal signs of functioning the body, and therefore nowadays are commonly used in various medical and non-medical applications. Since these signals are correlated with the stress, they have been commonly used in detection of the stress in humans. Photoplethysmography (PPG) and Galvanic Skin Response (GSR) are two of the most common signals which have been widely used in many stress related studies. PPG is a noninvasive method to measure the blood volume changes in blood vessels and GSR refers to changes in sweat gland activity that are reflective of the intensity of human emotional state. Design and fabrication of a real-time handheld system in order to detect and display the stress level is the main aim of this paper. The fabricated stress monitoring device is completely compatible with both wired and wireless sensor devices. The GSR and PPG signals are used in the developed system. The mentioned signals are acquired using appropriate sensors and are displayed to the user after initial signal processing operation. The main processor of the developed system is ARM-cortex A8 and its graphical user interface (GUI) is based on C++ programming language. Artificial Neural Networks such as MLP and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to modeling and estimation of the stress index. The results show that ANFIS model have a good accuracy with a coefficient of determination values of 0.9291 and average relative error of 0.007.   59 74 2017/12/62018/04/42017/12/122017/12/72017/12/20 1396/9/29 2018/09/152018/12/172018/07/252018/07/252018/07/25 1397/5/3 علیرضا گل‌گونه Alireza Golgouneh آزمایشگاه ربات‌های خدمت‌رسان پیشرفته، دانشکده علوم و فنون نوین، دانشگاه تهران ایران golgouneh@ut.ac.ir بهرام تارویردی‌زاده Bahram Tarvirdizadeh آزمایشگاه ربات‌های خدمت‌رسان پیشرفته، دانشکده علوم و فنون نوین، دانشگاه تهران ایران bahram@ut.ac.ir Stress Physiological signals GSR PPG Artificial Neural Networks ANFIS سیگنال‌های حیاتی فتوپلتیسموگرافی هدایت الکتریکی پوست شبکه‌ عصبی مصنوعی شبکه فازی-عصبی تطبیقی [1] J. Cacioppo, L. G. Tassinary, and G. G. Berntson, The Handbook of Psychophysiology, vol. 44., Cambridge University Press, 2007.##[2] H. Kurniawan, A. V Maslov, and M. Pechenizkiy, "Stress detection from speech and Galvanic Skin Response signals," Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. pp. 209–214, 2013.##[3] A. Kaklauskas et al., "Web-based biometric computer mouse advisory system to analyze a user's emotions and work productivity," Eng. Appl. Artif. Intell., vol. 24, no. 6, pp. 928–945, Sep. 2011.##[4] "Physiology," 2015. [Online]. Available: http://www.oxforddictionaries.com/definition/english/physiology.##[5] E. Risk and O. Report, OSH in figures: stress at work — facts and figures. Luxembourg,: Euro-pean Agency for Safety and Health at Work, 2009.z##[6] M. Bickford, "Stress in the workplace: A general overview of the causes, the effects, and the solutions," Canadian Mental Health Association Newfoundland and Labrador Division, pp. 1–3, 2005.##[7] W. Liao, W. Zhang, Z. Zhu, and Q. Ji, "A real-time human stress monitoring system using dynamic bayesian network," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops. p. 70, 2005.##[8] D. Carneiro, J. C. Castillo, P. Novais, A. Fernández-Caballero, and J. Neves, "Multimodal behavioral analysis for non-invasive stress detection," Expert Systems with Applications, vol. 39, no. 18, pp. 13376–13389, 2012.##[9] T. Hayashi, Y. Mizuno-Matsumoto, E. Okamoto, M. Kato, and T. Murata, "An fMRI study of brain processing related to stress states," World Automation Congress (WAC), pp. 1–6, 2012##[10] H. Lu et al., "Stresssense: Detecting stress in unconstrained acoustic environments using smartphones," Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 351-360. ACM, 2012.##[11] A. Muaremi, B. Arnrich, and G. Tröster, "Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep," Bionanoscience, vol. 3, no. 2, pp. 172–183, 2013.##[12] C. Epp, M. Lippold, and R. L. Mandryk, "Identifying emotional states using keystroke dynamics," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 715–724, 2011.##[13] D. F. Dinges et al., "Optical computer recognition of facial expressions associated with stress induced by performance demands.," Aviat. Space. Environ. Med., vol. 76, no. 6 Suppl, pp. B172-82, 2005.##[14] A. Alberdi, A. Aztiria, and A. Basarab, "Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review," J. Biomed. Inform., vol. 59, pp. 49–75, 2016.##[15] P. Zimmermann, S. Guttormsen, B. Danuser, and P. Gomez, "Affective computing—A rationale for measuring mood with mouse and keyboard," Int. J. Occup. Saf. Ergon., vol. 9, no. 4, pp. 539–551, 2003.##[16] M. Garbarino, M. Lai, D. Bender, R. W. Picard, and S. Tognetti, "Empatica E3—A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition," in Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on, pp. 39–42, 2014,.##[17] A. Golgouneh, A. Bamshad, B. Tarvirdizadeh, and F. Tajdari, "Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface," In Artificial Intelligence and Robotics (IRANOPEN), pp. 103-108. IEEE, 2016##[18] B. Cinaz, B. Arnrich, R. La Marca, and G. Tröster, "Monitoring of mental workload levels during an everyday life office-work scenario," Pers. ubiquitous Comput., vol. 17, no. 2, pp. 229–239, 2013.##[19] R.A. Shirvan, M.A. Khalilzadeh, S.E. Tahami, and V. Saadatian, "Comparision of linear and nonlinear feaaturs of heart rate variability signal to quantification of stress level using genetic algorithem and neural network," Signal and Data Processing, vol. 5, no. 2, p.p. 29-40, 2009.##[20] C. 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استفاده از نمایش پراکنده و همکاری دوربین‌ها برای کاربردهای نظارت بینایی Application of Sparse Representation and Camera Collaboration in Visual Surveillance Systems 1 1 مسأله آشکارسازی رفتارهای غیرمعمول براساس ویدئو، در‌‌حقیقت شناسایی الگوها در داده‌هایی است که با رفتارهای مورد انتظار مطابقت ندارند. درهمین‌اواخر برای خوشه­بندی رفتارها و آشکارسازی رفتارهای غیرمعمول، از روش‌های بازسازی پراکنده استفاده می‌شود. در این مقاله از نمایش پراکنده ویژگی‌ها و همکاری دوربین‌ها، برای شناسایی رفتارها و آشکارسازی رفتارهای غیرمعمول استفاده می‌شود. ابتدا برای هر محل در فریم تصویر، یک ویژگی که دارای استقلال هندسی است، استخراج می‌شود؛ سپس برای یک دوربین، ماتریس دیکشنری A محاسبه و به‌عنوان یک مجموعه از مدل رفتاری در نظر گرفته می‌شود. حال، تحت عنوان مسأله همکاری دوربین‌ها، ماتریس دیکشنری یادگرفته‌شده به دوربین دیگر منتقل می‌شود و در دوربین جدید برای آشکارسازی غیرمعمول‌ها مورد استفاده قرار می‌گیرد. برای یادگیری ماتریس دیکشنری، یک روش سلسله‌مراتبی با استفاده از خوشه­بندی طیفی پیشنهاد و یک معیار اندازه‌گیری با استفاده از نمایش پراکنده برای آشکارسازی رفتارهای غیرمعمول ارائه می‌شود. نتایج آزمایشی، مؤثر‌بودن ره‌یافت پیشنهادی در استفاده از همکاری دوربین­ها برای آشکارسازی رفتارهای غیرمعمول را نشان  می­دهد. 2 With the growth of demand for security and safety, video-based surveillance systems have been employed in a large number of rural and urban areas. The problem of such systems lies in the detection of patterns of behaviors in a dataset that do not conform to normal behaviors. Recently, for behavior classification and abnormal behavior detection, the sparse representation approach is used. In this paper, feature sparse representation in a multi-view network is used for the purpose of behavior classification and abnormal behavior detection. To serve this purpose, a geometrically independent feature is first extracted for each location in the image. Then, for each camera view, the matrix for the dictionary A is calculated, which is considered as a set of behavior models. In order to share information and make use of the trained models, the learned dictionary matrix from the experienced camera is transferred to inexperienced cameras. The transferred matrix in the new camera is subsequently used to detect abnormal behaviors. A hierarchical method on the basis of spectral clustering is proposed for learning the dictionary matrix. After sparse feature representation, a measurement criterion, which makes use of the representation, is presented for abnormal behavior detection. The merit of the method proposed in this paper is that the method does not require correspondence across cameras. The direct use of the dictionary matrix and transfer of the learned dictionary matrix from the experienced camera to inexperienced ones, are tested on several real-world video datasets. In both cases, desirable improvements in abnormal behavior detection are obtained. The experimental results point to the efficacy of the proposed method for camera cooperation in order to detect abnormal behaviors.   75 88 2017/12/62018/04/42017/12/122017/12/72017/12/202017/09/28 1396/7/6 2018/09/152018/12/172018/07/252018/07/252018/07/252018/08/6 1397/5/15 اصغر فیضی Asghar Feizi دانشگاه دامغان ایران a.feizi@du.ac.ir Visual Surveillance Sparse Representation Behavior Recognition Abnormal Behavior Camera Collaboration نظارت بینایی نمایش پراکنده شناسایی رفتارها رفتارهای غیرمعمول اشتراک اطلاعات چند دوربین [1] C. Chi-Hung, H. Jun-Wei, T. Luo-Wei, C. Sin-Yu, and F. Kuo-Chin, "Carried object detection using ratio histogram and its application to suspicious event analysis," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 6, pp. 911–916, 2009.##[2] T. Xiang and S. Gong, "Video behaviour profiling for anomaly detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 5, pp. 893–908, 2008.##[3] D. Xu, R. Song, X. Wu, N. Li, W. Feng, and H. Qian, "Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts," Neurocomputing, vol. 143, pp. 144–152, 2014.##[4] M. Sabokrou, M. Fayyaz, M. Fathy, Z. Moayed, and R. Klette, "Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes," Computer Vision and Image Understanding, no. February, Elsevier, pp. 0–1, 2018.##[5] Y. Yuan, D. Wang, and Q. Wang, "Anomaly Dete-ction in Traffic Scenes via Spatial-Aware Motion Reconstruction," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1198–1209, 2017.##[6] X. Gu, J. Cui, and Q. Zhu, "Abnormal crowd behavior detection by using the particle entropy," Optik (Stuttg)., vol. 125, no. 14, pp. 3428–3433, 2014.##[7] Y. Zhang, H. Lu, L. Zhang, and X. Ruan, "Combin-ing motion and appearance cues for anomaly detection," Pattern Recognit., vol. 51, pp. 443–452, 2016.##[8] B. Antić and B. Ommer, "Video parsing for ab-normality detection," Proc. IEEE Int. Conf. Com-put. Vis., pp. 2415–2422, 2011.##[9] S. Zhu, J. Hu, and Z. Shi, "Local abnormal behav-ior detection based on optical flow and spatio-temporal gradient," Multimed. Tools Appl., vol. 75, no. 15, pp. 9445–9459, 2016.##[10] J. Kim and K. Grauman, "Observe locally, infer globally: a space–time mrf for detecting ab-normal activities with incremental updates," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, no. June.##[11] A. L. Hou, J. L. Guo, C. J. Wang, L. Wu, and F. Li, "Abnormal behavior recognition based on trajectory feature and regional optical flow," in Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013, 2013, pp. 643–649.##[12] V. Saligrama, J. Konrad, and P. M. Jodoin, "Vi-deo anomaly identification," IEEE Signal Pro-cess. Mag., vol. 27, no. 5, pp. 18–33, 2010.##[13] J. A. Rodríguez-Serrano and S. Singh, "Trajec-tory clustering in CCTV traffic videos using probability product kernels with hidden Markov models," Pattern Anal. Appl., vol. 15, no. 4, pp. 415–426, 2012.##[14] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Anomaly detection and localization in crowded scenes using connected component analysis," 2017.##[15] X. Wang, X. Ma, and E. Grimson, "Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Mod-els.pdf," vol. 31, no. 3, pp. 1–35, 2007.##[16] S. Li, C. Liu, and Y. Yang, "Anomaly Detection Based on Maximum a Posteriori," Pattern Reco-gnit. Lett., vol. 0, pp. 1–7, 2017.##[17] C. Simon, J. Meessen, and C. De Vleeschouwer, "Visual event recognition using decision trees," Multimed. Tools Appl., vol. 50, no. 1, pp. 95–121, 2010.##[18] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Abnormal event detection in crowded scenes us-ing one-class SVM," Signal, Image Video Pro-cess., 2018.##[19] B. D. Devarajan, Z. Cheng, and R. J. Radke, "Camera Networks," vol. 96, no. 10, pp. 1625–1639, 2008.##[20] M. Piccardi, "Background subtraction techn-i-ques: a review," Vision Res., pp. 3099–3104, 2004.##[21] H. Cheng, Sparse Representation , Modeling and Learning in Visual Recognition. .##[22] X. Mo, V. Monga, R. Bala, and Z. Fan, "A joint sparsity model for video anomaly detection," Conf. Rec. - Asilomar Conf. Signals, Syst. Com-put., pp. 1969–1973, 2012.##[23] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, 2009.##[24] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Toward a practical face re-cognition system: Robust alignment and illu-mination by sparse representation," IEEE Trans. Pa-ttern Anal. Mach. Intell., vol. 34, no. 2, pp. 372–386, 2012.##[25] M. Planck and U. Von Luxburg, "A Tutorial on Spectral Clustering A Tutorial on Spectral Clus-tering," Stat. Comput., vol. 17, no. March, pp. 395–416, 2006.##[26] L. Zelnik and P. Perona, "Self-Yuning Spectral Clustering," vol. 17, no. 4.##[27] R. Mehran, a. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," 2009 IEEE Conf. Comput. Vis. Pattern Recognit., no. 2, pp. 935–942, 2009.##[28] "PETS2006 dataset. Available: http://www.cv-g.rdg.ac.uk/PETS2006/data.html."##[1] C. Chi-Hung, H. Jun-Wei, T. Luo-Wei, C. Sin-Yu, and F. Kuo-Chin, "Carried object detection using ratio histogram and its application to suspicious event analysis," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 6, pp. 911–916, 2009.##[2] T. Xiang and S. Gong, "Video behaviour profiling for anomaly detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 5, pp. 893–908, 2008.##[3] D. Xu, R. Song, X. Wu, N. Li, W. Feng, and H. Qian, "Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts," Neurocomputing, vol. 143, pp. 144–152, 2014.##[4] M. Sabokrou, M. Fayyaz, M. Fathy, Z. Moayed, and R. Klette, "Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes," Computer Vision and Image Understanding, no. February, Elsevier, pp. 0–1, 2018.##[5] Y. Yuan, D. Wang, and Q. Wang, "Anomaly Dete-ction in Traffic Scenes via Spatial-Aware Motion Reconstruction," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1198–1209, 2017.##[6] X. Gu, J. Cui, and Q. Zhu, "Abnormal crowd behavior detection by using the particle entropy," Optik (Stuttg)., vol. 125, no. 14, pp. 3428–3433, 2014.##[7] Y. Zhang, H. Lu, L. Zhang, and X. Ruan, "Combin-ing motion and appearance cues for anomaly detection," Pattern Recognit., vol. 51, pp. 443–452, 2016.##[8] B. Antić and B. Ommer, "Video parsing for ab-normality detection," Proc. IEEE Int. Conf. Com-put. Vis., pp. 2415–2422, 2011.##[9] S. Zhu, J. Hu, and Z. Shi, "Local abnormal behav-ior detection based on optical flow and spatio-temporal gradient," Multimed. Tools Appl., vol. 75, no. 15, pp. 9445–9459, 2016.##[10] J. Kim and K. Grauman, "Observe locally, infer globally: a space–time mrf for detecting ab-normal activities with incremental updates," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, no. June.##[11] A. L. Hou, J. L. Guo, C. J. Wang, L. Wu, and F. Li, "Abnormal behavior recognition based on trajectory feature and regional optical flow," in Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013, 2013, pp. 643–649.##[12] V. Saligrama, J. Konrad, and P. M. Jodoin, "Vi-deo anomaly identification," IEEE Signal Pro-cess. Mag., vol. 27, no. 5, pp. 18–33, 2010.##[13] J. A. Rodríguez-Serrano and S. Singh, "Trajec-tory clustering in CCTV traffic videos using probability product kernels with hidden Markov models," Pattern Anal. Appl., vol. 15, no. 4, pp. 415–426, 2012.##[14] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Anomaly detection and localization in crowded scenes using connected component analysis," 2017.##[15] X. Wang, X. Ma, and E. Grimson, "Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Mod-els.pdf," vol. 31, no. 3, pp. 1–35, 2007.##[16] S. Li, C. Liu, and Y. Yang, "Anomaly Detection Based on Maximum a Posteriori," Pattern Reco-gnit. Lett., vol. 0, pp. 1–7, 2017.##[17] C. Simon, J. Meessen, and C. De Vleeschouwer, "Visual event recognition using decision trees," Multimed. Tools Appl., vol. 50, no. 1, pp. 95–121, 2010.##[18] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Abnormal event detection in crowded scenes us-ing one-class SVM," Signal, Image Video Pro-cess., 2018.##[19] B. D. Devarajan, Z. Cheng, and R. J. Radke, "Camera Networks," vol. 96, no. 10, pp. 1625–1639, 2008.##[20] M. Piccardi, "Background subtraction techn-i-ques: a review," Vision Res., pp. 3099–3104, 2004.##[21] H. Cheng, Sparse Representation , Modeling and Learning in Visual Recognition. .##[22] X. Mo, V. Monga, R. Bala, and Z. Fan, "A joint sparsity model for video anomaly detection," Conf. Rec. - Asilomar Conf. Signals, Syst. Com-put., pp. 1969–1973, 2012.##[23] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, 2009.##[24] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Toward a practical face re-cognition system: Robust alignment and illu-mination by sparse representation," IEEE Trans. Pa-ttern Anal. Mach. Intell., vol. 34, no. 2, pp. 372–386, 2012.##[25] M. Planck and U. Von Luxburg, "A Tutorial on Spectral Clustering A Tutorial on Spectral Clus-tering," Stat. Comput., vol. 17, no. March, pp. 395–416, 2006.##[26] L. Zelnik and P. Perona, "Self-Yuning Spectral Clustering," vol. 17, no. 4.##[27] R. Mehran, a. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," 2009 IEEE Conf. Comput. Vis. Pattern Recognit., no. 2, pp. 935–942, 2009.##[28] "PETS2006 dataset. Available: http://www.cv-g.rdg.ac.uk/PETS2006/data.html."## ##

یافتن الگوهای مکرّر در قرآن کریم به‌‌کمک روش‌‌های متن‌‌کاوی Finding Frequent Patterns in Holy Quran UsingText Mining 1 1 متن قرآن خصوصیات منحصر به فردی از نظر معنا، مفهوم و موضوع نسبت با سایر متون دارد. کشف الگوهای پنهان و ارزشمند از درون حجم وسیعی از داده‌های خام، به‌تازگی توجه بسیاری از پژوهش‌گران را به خود جلب کرده است. متن‌کاوی زمینه‌ای برای کشف اطلاعات از متون است که ما را در نیل به این هدف می‌تواند کمک کند. در سال‌های اخیر متن‌کاوی روی قرآن و کشف دانش نهفته از واژه‌های آن، چندی است که مورد توجه بسیاری از متخصصان قرار گرفته است. در این مقاله با در‌نظر‌گرفتن 6348 آیه قرآن، هر آیه به‌صورت یک سبد خرید در نظر گرفته شده و کلمات  هر آیه به‌عنوان اقلام هستند؛ سپس با استفاده از قوانین انجمنی، کلمات و آیات قرآن بررسی شده و  از میان 54226 قانون انجمنی استخراج‌شده از واژه‌های قرآن که با استفاده از معیارهایی مانند ضریب اطمینان، ضریب پشتیبان، معیارLift  و معیار Co-efficient ارزیابی شده‌اند، ده قانون برتر هر معیار تحلیل و بررسی شده و بدین ترتیب الگوهای استخراج‌شده از قوانین انجمنی، الگوهای پرتکرار یک‌تایی، دوتایی و سه‌تایی در قرآن به دست می‌آید. 2 Quran’s Text differs from any other texts in terms of its exceptional concepts, ideas and subjects. To recognize the valuable implicit patterns through a vast amount of data has lately captured the attention of so many researchers. Text Mining provides the grounds to extract information from texts and it can help us reach our objective in this regard. In recent years, Text Mining on Quran and extracting implicit knowledge from Quranic words have been the object of researchers’ focus. It is common that in Quranic experts’ arguments, different sides of the discussion present different intellectual, logical and some non-integrated minor evidence in order to prove their own theories. More often than not, every side of these arguments disapproves of the other’s hypothesis and in the end it is impossible for them to reach a state of consensus on the matter, the reason is that, they do not have a common basis for their arguments and they do not make use of scientific, logical methods to strongly support their theories. Therefore, using modern technological trends regarding Quranic arguments could lead to resolving so many of current discrepancies, caused by human errors, which exist among Quranic researchers. It can help providing a common ground for their arguments in order to reach a comprehensive understanding. The method used in this research implements frequent pattern mining algorithms, singular frequent patterns as well as dual and tripe frequent patterns in order to analyze Quranic text, in addition to this, Association rules have also been evaluated in the research. Out of 54226 extracted association rules for Quranic words which have been evaluated by the use of criteria such as confidence coefficient, support coefficient, lift criteria as well as Co-efficient criteria. Top 10 rules for each criterion have been analyzed and reviewed throughout the project. 89 100 2017/12/62018/04/42017/12/122017/12/72017/12/202017/09/282017/12/14 1396/9/23 2018/09/152018/12/172018/07/252018/07/252018/07/252018/08/62018/07/25 1397/5/3 اکرم اصلانی Akram Aslani دانشگاه پیام نور ایران pnuakaslani@yahoo.com مهدی اسماعیلی Mahdi Esmaeili دانشگاه آزاد کاشان ایران msxp@yahoo.com Data Mining Text Mining Association rules Holy Quran Frequent patterns داده‌کاوی متن‌کاوی قوانین انجمنی قرآن کریم الگوهای مکرّر [1] M. Esmaieli, Concepts and techniques of data min-ing, Kashan;Azad University of Kashan,2013.##[2] R. N. Y. Radfar.R, Nezafti.N, Yoosefi Asli.S. Classification of Bank Internet Customers Using Data Mining Algorithms. IT management, pp71-90, 2014.##[3] A. M. Aghakardan, Keihani Nejad. Provides a mo-del for extracting information from textual texts ba-sed on e-learning, IT management,pp 47-54,2012.##[4] M.Karami, Journal of Health Management,2008.##[5] E. K. G. Estiri, Kahani, Ghaemi, Creating and publishing semantic web infrastructure for the Holy Quran, iranian association of information and communication technology, 2013.##[6] chue, s., puteri nor, e. (2014). frequent pattern ex-traction in the tafseer of al-quran. factually of com-puter sience and information technology.##[7] S. M. A. Salehi Shahroodi, Minaie, Ashrafi,The text explores the computerized subject of the Holy Quran to discover the semantic connections bet-ween the verses based on the interpretation of al-Mizan. The Quran recognizes, pp 117-152,2013.##[8] K.Kharazi, Discover the root relationships of Quranic words with the data mining approach. Tehran: Khaje Naseerdin Tousi University, 2011.##[9] chua, s., nor ellyza biniti nohuddin, p. (2014). Fre-quent pattern extraction in the tafseer of al-quran. department of computer science.##[10] alhawarat, m., hegazi, m., hilal, a. (2015). processing the text of the holy quran: a text mining study. international journal of advanced science and applications, 262-26##[11] ali, i. (2012). application of a mining algorithm to finding frequent patterns in a text corous: a case study of the arabic. international journal of soft-ware engineering and its applications, 127-134.##[12] Nasreen, S., Awais Azam, M., Shehzad, K., Naeem, U., Ali Ghazanfar, M. (2014). Frequent pattern mining algorithms for finding associated frequent patterns for data streams: a survey. emerging ubiquitous systems and pervasive net-works, 109-116##[13] H. Hojati, Research in the history of the Holy Qur'an, Teh-ran: Publishing House of Islamic Culture, 2006.##[14] F. Z. Fakhr Ahmad, Zolghadri Jahrom, An Effective Method for Exploring Over-the-Counter Items in Cart Basket Analysis. The International Scientific Engineering Department of Iran University of Science and Technology, pp 65-75, 2009.##[1] اسماعیلی، مهدی، مفاهیم و تکنیک‌های داده‌کاوی، کاشان، دانشگاه آزاد اسلامی واحد کاشان،1392.##[1] M. Esmaieli, Concepts and techniques of data min-ing, Kashan;Azad University of Kashan,2013.##[2] R. N. Y. Radfar.R, Nezafti.N, Yoosefi Asli.S. Classification of Bank Internet Customers Using Data Mining Algorithms. IT management, pp71-90, 2014.##[2] رادفر،رضا، نظافتی، نوید، یوسفی اصلی، سعید، "طبقه بندی مشتریان اینترنت بانک با کمک الگوریتم‌های داده کاوی"، نشریه علمی= پژوهشی مدیریت و فناوری اطلاعات، شماره 1، صفحات 90-71، 1393.##[3] آقاکاردان، احمد، کیهانی نژاد، مینا،" ارائه مدلی برای استخراج اطلاعات ازمستندات متنی مبتنی بر متن‌کاوی در حوزه یادگیری الکترونیکی"، نشریه علمی=پژوهشی مدیریت و فناوری اطلاعات و ارتباطات ایران، شماره‌های 11و12،صفحات 47-54، 1391.##[3] A. M. Aghakardan, Keihani Nejad. Provides a mo-del for extracting information from textual texts ba-sed on e-learning, IT management,pp 47-54,2012.##[4] کرمی، مهتاب،" کاربرد ابزارهای تحلیلگر داده کاوی و متن کاوی درچابکی سازمان های مراقبت بهداشتی و درمانی"، فصل‌نامه علمی پژوهشی مدیریت سلامت، شماره 10، 1386.##[4] M.Karami, Journal of Health Management,2008.##[5] استیری، احمد، کاهانی، محسن، قائمی، هادی، "ایجاد و انتشار زیر ساخت وب معنایی برای قرآن کریم"، iranian association of information and communication technology، 2013.##[5] E. K. G. Estiri, Kahani, Ghaemi, Creating and publishing semantic web infrastructure for the Holy Quran, iranian association of information and communication technology, 2013.##[6] chue, s., puteri nor, e. (2014). frequent pattern ex-traction in the tafseer of al-quran. factually of com-puter sience and information technology.##[7] صالحی شهرودی، محمدحسین، مینایی، بهروز، اشرفی، امیررضا، "متن‌کاوی موضوعی رایانه‌ای قرآن کریم برای کشف ارتباطات معنایی میان آیات برمبنای تفسیر المیزان " قرآن شناخت، شماره 2، 1392.##[7] S. M. A. Salehi Shahroodi, Minaie, Ashrafi,The text explores the computerized subject of the Holy Quran to discover the semantic connections bet-ween the verses based on the interpretation of al-Mizan. The Quran recognizes, pp 117-152,2013.##[8] خرازی، مریم، "کشف روابط ریشه واژه‌های قرآنی با رویکرد داده‌کاوی"، دانشگاه خواجه نصیر‌الدین طوسی، تهران،1393.##[8] K.Kharazi, Discover the root relationships of Quranic words with the data mining approach. Tehran: Khaje Naseerdin Tousi University, 2011.##[9] chua, s., nor ellyza biniti nohuddin, p. (2014). Fre-quent pattern extraction in the tafseer of al-quran. department of computer science.##[10] alhawarat, m., hegazi, m., hilal, a. (2015). processing the text of the holy quran: a text mining study. international journal of advanced science and applications, 262-26##[11] ali, i. (2012). application of a mining algorithm to finding frequent patterns in a text corous: a case study of the arabic. international journal of soft-ware engineering and its applications, 127-134.##[12] Nasreen, S., Awais Azam, M., Shehzad, K., Naeem, U., Ali Ghazanfar, M. (2014). Frequent pattern mining algorithms for finding associated frequent patterns for data streams: a survey. emerging ubiquitous systems and pervasive net-works, 109-116##[13] حجتی، سیدمحمدباقر، پژوهشی در تاریخ قرآن کریم، تهران،دفتر نشر فرهنگ اسلامی،1386.##[13] H. Hojati, Research in the history of the Holy Qur'an, Teh-ran: Publishing House of Islamic Culture, 2006.##[14] فخراحمد، سیدمحمد،صدرالدینی، محمدهادی، ذوالقدری جهرمی، منصور، "روشی کارا برای کاوش مجموعه اقلام پرتکرار در تحلیل داده‌های سبد خرید"، نشریه بین‌المللی علوم مهندسی دانشگاه علم وصنعت ایران، شماره 7،صفحه 74-65، 1387.##[14] F. Z. Fakhr Ahmad, Zolghadri Jahrom, An Effective Method for Exploring Over-the-Counter Items in Cart Basket Analysis. The International Scientific Engineering Department of Iran University of Science and Technology, pp 65-75, 2009.## ##

روشی جدید برای عضویت‌دهی به داده‌ها و شناسایی نوفه و داده‌های پرت با استفاده از ماشین بردار پشتیبان فازی A New Method to Determine Data Membership and Find Noise and Outlier Data Using Fuzzy Support Vector Machine 1 1 در این مقاله روشی جدید را با استفاده از ماشین بردار پشتیبان فازی به منظور عضویت‎دهی داده‌های آموزشی، براساس فاصله از ابر صفحه جداکننده معرفی می‌شود. در این روش، با استفاده از طبقه‌بندی ماشین بردار پشتیبان و نیز عضویت نخستین داده‌های آموزشی، یک تابع عضویت فازی به‌کمک اعداد فازی مثلثی متقارن برای تمام فضا معرفی می‌شود. مبتنی بر این روش، مقدار تابع عضویت فازی هر داده جدیدی که می‌خواهد طبقه‌بندی شود، به­گونه‌ای انتخاب می‌شود که کمترین میزان اختلاف را با عضویت اولیه داده‌های آموزشی و بیشترین میزان فازی­سازی داشته باشد. نخست این مسأله به‌صورت یک مسأله بهینه‌سازی غیرخطی تعریف، سپس به‌کمک روش نقاط بحرانی، الگوریتمی کارا معرفی می‌شود و تابع عضویت نهایی داده‌های آموزشی به‌دست می­آید؛ همچنین، در ادامه با مقایسه مقدار عضویت‌های اولیه داده‌های آموزشی با توزیع عضویت نهایی به‌دست‌آمده از روش پیشنهادی، میزان نوفه‌ای‌بودن داده آموزشی بررسی می‌شود. در انتهای این مقاله نیز جهت فهم بهتر و نشان‌دادن کارایی الگوریتم، آزمایش‌هایی انجام و چگونگی رفتار الگوریتم پیشنهادی بر روی نمودار پیاده‌سازی و با یک روش پایه مقایسه می‌شود.   2 Support Vector Machine (SVM) is one of the important classification techniques, has been recently attracted by many of the researchers. However, there are some limitations for this approach. Determining the hyperplane that distinguishes classes with the maximum margin and calculating the position of each point (train data) in SVM linear classifier can be interpreted as computing a data membership with certainty. A question may be raised here: how much the level of the certainty of this classification, based on hyperplane, can be trusted. In the standard SVM classification, the significance of error for different train data is considered equal and every datum is assumed to belong to just one class. However, in many cases some of train data, including outlier and vague data with no defined model, cannot be strictly considered as a member of a certain class. That means, a train datum may does not exactly belong to one class and its features may show 90 percent membership of one class and 10 percent of another. In such cases, by using fuzzy SVM based on fuzzy logic, we can determine the significance of data in the train phase and finally determine relative class membership of data. The method proposed by Lin and Wang is a basic method that introduces a membership function for fuzzy support vector machine. Their membership function is based on the distance between a point and the center of its corresponding class. In this paper, we introduce a new method for giving membership to train data based on their distance from distinctive hyperplane. In this method, SVM classification together with primary train data membership are used to introduce a fuzzy membership function for the whole space using symmetrical triangular fuzzy numbers. Based on this method, fuzzy membership function value of new data is selected with minimum difference from primary membership of train data and with the maximum level of fuzzification. In the first step, we define the problem as a nonlinear optimization problem. Then we introduce an efficient algorithm using critical points and obtain final membership function of train data. According to the proposed algorithm, the more distant data from the hyperplane will have a higher membership degree. If a datum exists on the hyperplane, it belongs to both classes with the same membership degree. Moreover, by comparing the primary membership degree of train data and calculated final distribution, we compute the level of noise for train data. Finally, we give a numerical example for illustration the efficiency of the proposed method and comparing its results with the results of the Lin and Wang approach.   101 112 2017/12/62018/04/42017/12/122017/12/72017/12/202017/09/282017/12/142017/11/19 1396/8/28 2018/09/152018/12/172018/07/252018/07/252018/07/252018/08/62018/07/252018/08/18 1397/5/27 منا خداقلی Mona Khodagholi گروه ریاضی، دانشکده علوم پایه، دانشگاه شاهد ایران ma_khodagholy@yahoo.com اردشیر دولتی Ardeshir Dolati گروه ریاضی، دانشکده علوم پایه، دانشگاه شاهد ایران dolati@shahed.ac.ir علی حسین زاده Ali Hosseinzadeh گروه ریاضی، دانشکده علوم پایه، دانشگاه شاهد ایران hoseinzadeh1393@gmail.com خشایار شمس الکتابی khashayar Shamsolketabi دانشگاه تربیت مدرس ایران ali_newmath@yahoo.com Fuzzy Logic Machine learning Data mining Fuzzy support vector machine fuzzy membership function منطق فازی یادگیری ماشین داده‌کاوی بردار پشتیبان فازی تابع عضویت فازی [1] Mehralian M A, kazem fouladi K. the recognition of online handwritten persian characters based on their main bodies using svm. JSDP. 2012; 9 (1) :59-68##[2] Montazer G A, shayestehfar M. iranian license plate identification with fuzzy support vector ma-chine . JSDP. 2015; 12 (1) :47-56##[3] Abe S. Pattern Classification: Neuro-Fuzzy M-ethods And Their Comparison. springer-verlag, London, UK, 2001.##[4] Alpayden E. Introduction To Machine Learning. The MIT Press, 2010.##[5] Bezdek J. C. Fuzzy Mathematics In Pattern Classi-fication. Phd Dissertation, Cornell University, Ith-aca, NY, 1973.##[6] Bishop CM. Pattern Recognition And Machine Learning. springer; 2006.##[7] Burges J. C. a tutorial on support vector machine-es for pattern recognition. Data Mining and Know-ledge Discovery 1998; 2(2): 121-167.##[[8] Cortes C., Vapnik V. support-vector networks. Machine Learning 1995; 20(3): 273-297.##https://doi.org/10.1007/BF00994018##[9] Fisher RA. the use of multiple measurements in taxonomic problems. Annals of eugenics. 1936 Sep;7(2):179-88.##[10] Huang H. P., Liu Y. H. fuzzy support vector machine for pattern recognition and data mining. Int J Fuzzy Syst 2002; 4(3): 826–835.##[11] Inoue T., Abe S. fuzzy support vector machines for pattern classification. In Proceeding of IJCNN 2001; 2: 1449–1454.##[12] Jiang X. F., Yi, Z., Lv J. C. fuzzy svm with a new fuzzy membership function. Neural Compute 2006; 15(3-4): 268–276.##[13] Lee, G.H., Taur, J. S., Tao, C.W. a robust fuzzy support vector machine for two-class pattern classification. International Journal of Fuzzy Systems 2006; 8(2): 76-87.##[14] Li M. Q., Chen F. Z., Kou J. S. candidate vectors selection for training support vector machines. IEEE Computer Society, Third International Conference on Natural Computation (ICNC) 2007; 1: 538-542.##[15] Lin C. F., Wang S.D. fuzzy support vector ma-chines. IEEE Trans. on Neural Networks 2002; 13(2): 464-471.##[16] Mitchell T. Machine Learning. McGraw Hill, ISBN 0-07-042807-7, 1997.##[17] Nilsson N. J. Introduction To Machine Learning. Robotics Laboratory Department of Computer Science Stanford University Stanford, CA 94305, 2005.##[18] Pontil M., Verri A. properties of support vector machines. Neural Computation 1998; 10(4): 955-974.##[19] Schölkopf B., Burges J. C., Smola A. Advances In Kernel Methods: Support Vector Learning. Cambridge. MA: MIT Press, 1999.##[20] Shiry S., Sadatpoor S. S. Using Machine Learning Techniques In Homeopathy. Amir Kabir Uni-versity Press, 2010.##[21] Tang M. E. fuzzy svm with a new fuzzy membership function to solve the two-class pro-blems. Neural Processing Letters 2011; 34(3): 209-219.##[22] Trung L. e., Tran D., Wanli M. A., Sharma D. a new fuzzy membership computation method for fuzzy support vector machines, Communications and Electronics (ICCE), Third International Conference on 2010; 153 – 157.##[23] Vapnik V. N. Statistical Learning Theory. New York: Wiley, 1998.##[24] Vapnik V. N. The Nature Of Statistical Learning Theory. New York: Springer-Verlag, 1995.##[25] Zadeh L. A. Fuzzy Set A Basis For A Thory Of Possibility. In Fuzzy Set and System 1987; 1: 3-28.##[26] Zadeh L. A. fuzzy sets. information and control, 1965; 8(3): 338-353.##[27] Zhang X.G. using class-center vectors to build support vector machines. In: Proceeding of the IEEE signal processing society workshop 1999; 3–11.##[1] محمدامین، فولادی کاظم. بازشناسی برخط حروف مجزای دست‌نویس فارسی بر اساس تشخیص گروه بدنه اصلی با استفاده از ماشین بردار پشتیبان. پردازش علائم و داده‌ها. 1391; 9 (1) :59-68##[1] Mehralian M A, kazem fouladi K. the recognition of online handwritten persian characters based on their main bodies using svm. JSDP. 2012; 9 (1) :59-68##[2] منتظر غلامعلی، شایسته‌فر محمد. شناسایی پلاک خودروهای ایرانی با الگوریتم ماشین بردار پشتیبانی فازی. پردازش علائم و داده‌ها. ۱۳۹۴; ۱۲ (۱) :۴۷-۵۶.##[2] Montazer G A, shayestehfar M. iranian license plate identification with fuzzy support vector ma-chine . JSDP. 2015; 12 (1) :47-56##[3] Abe S. Pattern Classification: Neuro-Fuzzy M-ethods And Their Comparison. springer-verlag, London, UK, 2001.##[4] Alpayden E. Introduction To Machine Learning. The MIT Press, 2010.##[5] Bezdek J. C. Fuzzy Mathematics In Pattern Classi-fication. Phd Dissertation, Cornell University, Ith-aca, NY, 1973.##[6] Bishop CM. Pattern Recognition And Machine Learning. springer; 2006.##[7] Burges J. C. a tutorial on support vector machine-es for pattern recognition. Data Mining and Know-ledge Discovery 1998; 2(2): 121-167.##[[8] Cortes C., Vapnik V. support-vector networks. Machine Learning 1995; 20(3): 273-297.##https://doi.org/10.1007/BF00994018##[9] Fisher RA. the use of multiple measurements in taxonomic problems. Annals of eugenics. 1936 Sep;7(2):179-88.##[10] Huang H. P., Liu Y. H. fuzzy support vector machine for pattern recognition and data mining. Int J Fuzzy Syst 2002; 4(3): 826–835.##[11] Inoue T., Abe S. fuzzy support vector machines for pattern classification. In Proceeding of IJCNN 2001; 2: 1449–1454.##[12] Jiang X. F., Yi, Z., Lv J. C. fuzzy svm with a new fuzzy membership function. Neural Compute 2006; 15(3-4): 268–276.##[13] Lee, G.H., Taur, J. S., Tao, C.W. a robust fuzzy support vector machine for two-class pattern classification. International Journal of Fuzzy Systems 2006; 8(2): 76-87.##[14] Li M. Q., Chen F. Z., Kou J. S. candidate vectors selection for training support vector machines. IEEE Computer Society, Third International Conference on Natural Computation (ICNC) 2007; 1: 538-542.##[15] Lin C. F., Wang S.D. fuzzy support vector ma-chines. IEEE Trans. on Neural Networks 2002; 13(2): 464-471.##[16] Mitchell T. Machine Learning. McGraw Hill, ISBN 0-07-042807-7, 1997.##[17] Nilsson N. J. Introduction To Machine Learning. Robotics Laboratory Department of Computer Science Stanford University Stanford, CA 94305, 2005.##[18] Pontil M., Verri A. properties of support vector machines. Neural Computation 1998; 10(4): 955-974.##[19] Schölkopf B., Burges J. C., Smola A. Advances In Kernel Methods: Support Vector Learning. Cambridge. MA: MIT Press, 1999.##[20] Shiry S., Sadatpoor S. S. Using Machine Learning Techniques In Homeopathy. Amir Kabir Uni-versity Press, 2010.##[21] Tang M. E. fuzzy svm with a new fuzzy membership function to solve the two-class pro-blems. Neural Processing Letters 2011; 34(3): 209-219.##[22] Trung L. e., Tran D., Wanli M. A., Sharma D. a new fuzzy membership computation method for fuzzy support vector machines, Communications and Electronics (ICCE), Third International Conference on 2010; 153 – 157.##[23] Vapnik V. N. Statistical Learning Theory. New York: Wiley, 1998.##[24] Vapnik V. N. The Nature Of Statistical Learning Theory. New York: Springer-Verlag, 1995.##[25] Zadeh L. A. Fuzzy Set A Basis For A Thory Of Possibility. In Fuzzy Set and System 1987; 1: 3-28.##[26] Zadeh L. A. fuzzy sets. information and control, 1965; 8(3): 338-353.##[27] Zhang X.G. using class-center vectors to build support vector machines. In: Proceeding of the IEEE signal processing society workshop 1999; 3–11.## ##

نهان‌کاوی صوت مبتنی بر همبستگی بین فریم و کاهش بازگشتی ویژگی Audio Steganalysis based on Inter-frame correlation and recursive feature elimination 1 1 سیگنال­‌های صوتی دیجیتال، به‌دلیل این­که حاوی نرخ اطلاعات زیادی هستند، پوشش مناسبی برای روش‌­های نهان‌­نگاری محسوب می­‌شوند. روش‌­های متنوعی برای نهان‌­نگاری داده‌­های مختلف و به تبع آن نهان­‌کاوی داده­‌ها در سیگنال صوتی وجود دارد. در این میان روش‌­های نهان­‌کاوی فراگیر به‌دلیل عدم وابستگی به الگوریتم نهان­‌نگاری، کاربرد وسیع‌­تری دارند. در این مقاله روش جدیدی برای نهان­‌کاوی فراگیر ارائه شده که در آن با به‌کارگیری ضرایب مربوط به همبستگی بین فریم، دقت نهان­‌کاوی به مقدار قابل توجهی افزایش پیدا کرده است. همچنین عملکرد ماشین بردار پشتیبان با به‌کارگیری الگوریتم کاهش بازگشتی ویژگی­‌ها به‌همراه کاهش بایاس ناشی از همبستگی بین آن­ها بهبود یافته که منجر به افزایش پایداری نهان­‌کاوی و دقت بیشتر شده است.   2 Dramatic changes in digital communication and exchange of image, audio, video and text files result in a suitable field for interpersonal transfers of hidden information. Therefore, nowadays, preserving channel security and intellectual property and access to hidden information make new fields of researches naming steganography, watermarking and steganalysis. Steganalysis as a binary classification distinguish clean signals from stego signals. Features extracted from time and transform domain are proper for this classifier. Some of steganalysis methods are depended on a specific steganography algorithm and others are independent. The second group of methods are called Universal steganalysis. Universal steganalysis methods are widely used in applications because of their independency to steganography algorithms. These algorithms are based on characteristics such as distortion measurements, higher order statistics and other similar features. In this research we try to achieve more reliable and accurate results using analytical review of features, choose more effective of them and optimize SVM performance. In new researches Mel Frequency Cepstral Coefficient and Markov transition probability matrix coefficients are used to steganalysis design. In this paper we consider two facts. First, MFCC extract signal   features in transform domain similar to human hearing model, which is more sensitive to low frequency signals. As a result, in this method there is more hidden information mostly in higher frequency audio signals. Therefore, it is suggested to use reversed MFCC. Second, there is an interframe correlation in audio signals which is useful as an information hiding effect. For the first time, in this research, this features is used in steganalysis field. To have more accurate and stable results, we use recursive feature elimination with correlation bias reduction for SVM. To implement suggested algorithm, we use two different data sets from TIMIT and GRID. For each data sets,Steghide and LSB-Matching steganography methods implement with 20 and 50 percent capacity. In addition, one of the LIBSVM 3.2 toolboxes is sued for implementation. Finally, the results show accuracy of steganalysis, four to six percent increase in comparison with previous methods. The ROC of methods clearly shows this improvement.   113 122 2017/12/62018/04/42017/12/122017/12/72017/12/202017/09/282017/12/142017/11/192017/12/24 1396/10/3 2018/09/152018/12/172018/07/252018/07/252018/07/252018/08/62018/07/252018/08/182018/07/25 1397/5/3 فاطمه اشعری Fatemeh Ashari دانشکده فنی و مهندسی دانشگاه الزهرا(س) ایران ftashari@gmail.com نوشین ریاحی Nooshin Riahi دانشکده فنی و مهندسی دانشگاه الزهرا(س) ایران nriahi@alzahra.ac.ir steganalysis steganography Mel SVM-RFE+CBR نهان نگاری نهان‌کاوی همبستگی بین فریم کپستروم مل معکوس کاهش ویژگی بازگشتی [1] Ghasemzadeh, H. Tajikkhas, M. Khalilarjmandi, H. "Audio Steganalysis based on psychoacoustic model of human hearing". (2016) ELSEVIER, Signal Processing Letters,Vol. 51, Pages 664-672.##[2] Ren, Y. Xiong, Q. Wang, L. "Steganalysis of AAC using calibrated Markov model of adjacent codebook". (2016) IEEE International Conference on Acoustica, Speech and Signal Processing.##[3] Yan, Ke. Zhang, David. "Feature selection and analysis on correlated gas sensor data with-recursive feature elimination". (2015) Elsevier, Sensors and Actuators B 212. Pages 353-363.##[4] Ghasemzadeh, H. Khalil Arjmandi, M. "Reversed-Mel Cepstrum Based Audio Steganalysis". (2014) IEEE 4th International eConference on Computer and Knowledge Engineering.##[5] Yang, Y. et al. "An inter-frame correlation based error concealment of immittance spectral coeffi-cients for mobile speech and audio codecs". (2014) IEEE International Conference on High Per-formance Computing and Communications.##[6] Liu, Q. Sung, A. Qiao, M. "Derivative-Based Audio Steganalysis". (2011) ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 7, No. 3, Article 18.##[7] Tolosi, L. Lengauer, Th. "Classification with correlated features: unreliability of feature ranking and solu-tions". (2011) Data and text mining. Publ-ished by Oxford University Press. Vol. 27, Pages 1986–1994.##[8] Liu, Q. Sung, A. Qiao, M."Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Ste-ganalysis". (2009) IEEE Transaction on in-formation forensics and security, Vol. 4, No. 3.##[9] Liu, Q. et al. "Novel Stram Mining for Audio Steganalysis". (2009) ACM International Con-ference on Multimedia, Pages 95-104.##[10] Zeng, W. et al. "An Algorithm of Echo Steg-analysis based on Power Cepstrum and Pattern Classification". (2008) International Conference on Information and Automation, Pages 1667-1670.##[11] Liu, Y. el al. "A Novel Audio Steganalysis based on Higher-Order Statistics of Distortion Measure with Hausdroff Distence". (2008) Lecture Notes in Computer Science, Vol. 5222, Pages 487-501.##[12] Ismail Avcibas,"Audio Steganalysis With Content-Independent Dis-tortion Measures". (2006) IEEE Signal Processing Letters,Vol.13, No.2.##[13] Johnson, M.K. et al. "Steganalysis of Recorded Speech". (2005) Conference on Security, Stega-nography and Watermarking of Multi-media, Vol. 5681, Pages 664-672.##[14] Ozer, H. Activbas, I. et al. "Steganalysis of Audio based on Audio Quality Metrics". (2003) Conference on Security, Steganography and Watermarking of Multimedia, Vol. 5020, Pages 55-66.##[15] Harmsen, J.J. Pearlman, W.A. "Steganalysis of additive noise modelable information Hiding". (2006)##[16] Paliwal, K.K. "Use of temporal correlation between successive frames in a hidden markov-model based speech recognizer". (1993) IEEE international conference on Acoustics, speech, and signal processing##[17] I. Guyon, J. Weston, S. B, and V. V. (2002) "Gene selection for cancer classification using support vector machines". Mach. Learn., Vol. 46, No. 1–3, pp. 389–422.##[1] Ghasemzadeh, H. Tajikkhas, M. Khalilarjmandi, H. "Audio Steganalysis based on psychoacoustic model of human hearing". (2016) ELSEVIER, Signal Processing Letters,Vol. 51, Pages 664-672.##[2] Ren, Y. Xiong, Q. Wang, L. "Steganalysis of AAC using calibrated Markov model of adjacent codebook". (2016) IEEE International Conference on Acoustica, Speech and Signal Processing.##[3] Yan, Ke. Zhang, David. "Feature selection and analysis on correlated gas sensor data with-recursive feature elimination". (2015) Elsevier, Sensors and Actuators B 212. Pages 353-363.##[4] Ghasemzadeh, H. Khalil Arjmandi, M. "Reversed-Mel Cepstrum Based Audio Steganalysis". (2014) IEEE 4th International eConference on Computer and Knowledge Engineering.##[5] Yang, Y. et al. "An inter-frame correlation based error concealment of immittance spectral coeffi-cients for mobile speech and audio codecs". (2014) IEEE International Conference on High Per-formance Computing and Communications.##[6] Liu, Q. Sung, A. Qiao, M. "Derivative-Based Audio Steganalysis". (2011) ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 7, No. 3, Article 18.##[7] Tolosi, L. Lengauer, Th. "Classification with correlated features: unreliability of feature ranking and solu-tions". (2011) Data and text mining. Publ-ished by Oxford University Press. Vol. 27, Pages 1986–1994.##[8] Liu, Q. Sung, A. Qiao, M."Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Ste-ganalysis". (2009) IEEE Transaction on in-formation forensics and security, Vol. 4, No. 3.##[9] Liu, Q. et al. "Novel Stram Mining for Audio Steganalysis". (2009) ACM International Con-ference on Multimedia, Pages 95-104.##[10] Zeng, W. et al. "An Algorithm of Echo Steg-analysis based on Power Cepstrum and Pattern Classification". (2008) International Conference on Information and Automation, Pages 1667-1670.##[11] Liu, Y. el al. "A Novel Audio Steganalysis based on Higher-Order Statistics of Distortion Measure with Hausdroff Distence". 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بهینه‌سازی اجرا و پاسخ صفحات وب در فضای ابری با روش‌های پیش‌پردازش، مطالعه موردی سامانه‌های وارنیش و انجینکس Optimizing Web programs Response in Cloud Using Pre-processing, Case study Nginx, Varnish 1 1 سرعت پاسخ صفحات وب یکی از ضرورت‌های دنیای فناوری اطلاعات است. در سال‌های اخیر شرکت‌های مشهور از قبیل گوگل و دانشمندان علوم رایانه تمرکز خود را روی سرعت‌بخشیدن به وب قرار دادند و تلاش کردند تا راهی برای سریع‌تر‌کردن وب بیابند. دستاوردهایی از قبیل گوگل پیج اسپید، انجینکس و وارنیش نتیجه این پژوهش‌ها بوده است. درهمین‌اواخر روش‌ها و ابزارهای مؤثر و موفقی برای افزایش سرعت بارگذاری صفحات وب ارائه شده که بیش‌تر به دو رهیافت افزایش سرعت در سمت کاربر و افزایش سرعت در سمت سرور تقسیم می‌شود. پراکسی معکوس به‌عنوان مؤثرترین روش افزایش سرعت در سمت سرور است. در این مقاله ضمن معرفی پراکسی معکوس، کارایی سامانه‌های وب سرور چهارگانه آپاچی + وارنیش، انجینکس، انجینکس + وارنیش و آپاچی را، با دو نوع محتوای پویا و ایستا، از منظر سرعت پاسخ صفحات وب به‌عنوان معیار سنجش بررسی کرده‌ایم. نخست‌ این‌که نتایج به‌دست‌آمده نشان می‌دهند، استفاده از پراکسی معکوس موجب افزایش سرعت پاسخ می‌شود؛ دوم این‌که این افزایش سرعت نه‌تنها به وب سرور بلکه به نوع محتوای صفحات وب و تعداد تقاضاهای تکراری در صفحات وب مرتبط است. در پایان یک رتبه­بندی برای انتخاب وب­سرور و پراکسی معکوس مناسب برای محتوای وب ایستا یا چندرسانه­ای و محتوای وب پویا یا پردازشی ارائه شده است.   2 The response speed of Web pages is one of the necessities of information technology. In recent years, renowned companies such as Google and computer scientists focused on speeding up the web. Achievements such as Google Pagespeed, Nginx and varnish are the result of these researches. In Customer to Customer(C2C) business systems, such as chat systems, and in Business to Customer(B2C) systems, such as online stores and banks, the power and speed of the system’s response to the high volume of visitors are very effective in customer satisfaction and the efficiency of the business system. Increasing the speed of web pages from the origin of the advent of this technology, used from known and proven methods such as preprocessing, cookie, Ajax, cache and so on, to speed up the implementation of Internet applications, but it still needs to increase the speed of running and operating systems under the web. Recently, successful and effective methods and tools devised to increase the loading speed of Web pages, which consist mainly two approaches, increasing the speed on the client-side user and increasing the speed of the server-side. Research and technology on the performance and speed of Web technology on server side are divided into two categories of content enhancements, such as the Google Page Speed tool and Web server performance improvements such as Reverse Proxies. Reverse proxy is the most effective way to increase the speed on the server-side. Web server performance is measured by various metrics such as process load, memory usage and response speed to requests. Reverse proxy technology has been implemented in the Varna and Engineer systems. Implementing the reverse proxy in Varna has focused on caching processing content and on the engineering to cache static content. Our goal is to evaluate the performance of these two systems as reverse proxies to improve the response speed and loading of web pages in two types of dynamic (processing) and static (multimedia) content and provide a framework for the appropriate selection of a reverse proxy on web servers. In this paper, we introduce reverse proxy and analyze the performance of the four web servers, namely apache + varnish, nginx, nginx + varnish and apache, with both static and dynamic content, in terms of response speed of web pages as a measure of performance. First, our results show that, using a reverse proxy response speed is increased. Second, the resulted speed up is related not only to web server type but also to the content type of web pages requested repeatedly. Finally, a ranking is provided which helps to select the appropriate web server and reverse proxy when the web content type is static (multimedia) or dynamic (processed). 123 133 2017/12/62018/04/42017/12/122017/12/72017/12/202017/09/282017/12/142017/11/192017/12/242017/10/22 1396/7/30 2018/09/152018/12/172018/07/252018/07/252018/07/252018/08/62018/07/252018/08/182018/07/252018/07/25 1397/5/3 سنیه دیلمی Saniyeh Deylami دانشگاه قم ایران s.deylami1990@gmail.com یعقوب فرجامی Yaghoub Farjami دانشگاه قم ایران farjami@gmail.com reverse proxy varnish nginx accelerated web response time سرعت پاسخ پراکسی معکوس انجینکس آپاچی وارنیش [1] M. Firtman, High Performance Mobile Web: Best Practices for Optimizing Mobile Web Apps. O'Reilly Media, 2016.##[2] J. Wagner, Web Performance in Action: Building Faster Web Pages. Manning Publications, 2017.##[3] D. Aivaliotis, Mastering Nginx. Packt Publishing Ltd, 2016.##[4] T. Feryn, Getting Started with Varnish Cache: Acc-elerate Your Web Applications, O'Reilly Media, 2017.##[5] Apache Foundation, Apache HTTP Server 2.2 Official Documentation. Vol. I-III, Apache Sof-tware Foundation, 2010.##[6] S. Corona, Nginx: A Practical Guide to High Per-formance, O'Reilly Media, 2017.##[7] R. Moutinho, Instant Varnish Cache How-to. Packt Publishing Ltd.‌, New York, 2013.##[8] P. H. Kamp, You're doing it wrong. Communi-cations of the ACM vol. 53 no.7, pp. 55-59, 2010.##[9] Web Page Test, Test a website's performance, Available: http://www.webpagetest.org/, [Access-ed: Sept. 1, 2015].##[10] GTmetrix, Website Speed and Performance Optimization, Available: http://gtmetrix.com/, [Accessed: Sept. 1, 2015].##[11] Pingdom, Website & Performance Monitoring, Available: http://tools.pingdom.com/fpt/, [Acc-essed: Sept. 1, 2015].##[12] S. Bakhtiyari, Performance Evaluation of the Apache Traffic Server and Varnish Reverse Proxies, Master Thesis, Dept. of Informatics, Univ. of Oslo, Oslo, Norway, May 23, 2012.##[13] L. D. Tobias, Caching HTTP A comparative study of caching reverse proxies Varnish and Nginx, Master Thesis, School of Informatics, Univ. of Skövde, Skövde, Sweden, June 8, 2014.##[14] N. Mathieu, Magento Site Performance Optimi-zation, Packt Publishing, 2014.##[15] B. T. Teklehaimanot, Virtualization of Video Streaming Functions. Ph.D. diss., Dept. of Computer Sciences, Univ. of Saarlandes, Saarb-rücken, Germany, 2016.##[16] D. Kumar, V. Tyagi, and M. Vijay, With High Level Fragmentation of Dataset, Load Balancing in Cloud Computing. Int. Journal of Innovations & Advancement in Computer Science, Vol 3, Issue 5, 2014.##[17] R. DiCosmo, S. Zacchiroli and G. Zavattaro, Aeolus: A component model for the cloud. J. In-formation and Computation, Vol. 239, pp. 100-121, 2014.##[18] A. Grocevs, N. Prokofyeva, Modern approaches to reduce webpage load times, J. Env. Tech. Resources, 2015, Vol. 3, pp. 87-91, 2015.##[19] J. Andjarwirawan, I. Gunawan and E. Kusumo, Varnish Web Cache Application Evaluation. Proc. Intelligence in the Era of Big Data, 4th Int. Conf. on Soft Computing, Intelligent Systems, and Information Technology, pp. 404-410, Sprin-ger,‌ 2015.##[20]‌ A. Loechel and S. Schmid, Comparison of Different Caching Techniques for High-Per-formance Web Map Services. Int. Journal of Spatial Data Infrastructures Research, Vol. 8, pp. 43-73, 2013.##[21] J. Nogueira, D. Gonzalez, L. Guardalben and S. Sargento. Over-The-Top Catch-up TV content-aware caching. 2016 IEEE Symposium on Com-puters and Communication (ISCC), pp. 1012-1017. IEEE, 2016.##[22] P. A. Baeza, Integración de un proxy inverso Varnish Cache con un panel de control Virtualmin para crear una plataforma de servicios de hospedaje Web. Thesis, Dept. Tècnica Sup-erior d'Enginyeria Informàtica, Univ. Politècnica de València, Valencia, Spain, 2015.##[23] L. P. Petroski, J. Matos, and J. E. Bertotti. Analysis of an event oriented web server as a reverse proxy with flexible load distribution. Ibe-roamerican Journal of Applied Computing, Vol. 4, no. 2, 2016.‌##[24] A. Martínez-Álvarez, S. Cuenca-Asensi, A. Ortiz, J. Calvo-Zaragoza, and L. A. V. Tejuelo, Tuning compilations by multi-objective optimization: Application to apache web server, Journal of Applied Soft Computing, Vol. 29, pp. 461-470.‌ 2016.##[25] D. S. Berger, K. R. Sitaraman and M. Harchol-Balter. AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network. 14th USENIX Symposium on Networked Sys-tems Design and Implementation, pp. 483-498. 2017.##[26] R. Viscomi, A. Davies and M. Duran: Using WebPageTest, Web Performance Testing for Novices and Power Users. O'Reilly Media, Octo-ber 2015.##[27] L. Čegan, Intelligent preloading of websites resources based on clustering web user sessions. 5th International Conference on IT Convergence and Security, pp. 1-4. IEEE, 2015.##[28] L. Čegan, Performance Evaluation of Preloading Resources for Web Pages, Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, Vol. 362. Springer, 2016.##[1] M. Firtman, High Performance Mobile Web: Best Practices for Optimizing Mobile Web Apps. O'Reilly Media, 2016.##[2] J. Wagner, Web Performance in Action: Building Faster Web Pages. Manning Publications, 2017.##[3] D. Aivaliotis, Mastering Nginx. Packt Publishing Ltd, 2016.##[4] T. Feryn, Getting Started with Varnish Cache: Acc-elerate Your Web Applications, O'Reilly Media, 2017.##[5] Apache Foundation, Apache HTTP Server 2.2 Official Documentation. Vol. I-III, Apache Sof-tware Foundation, 2010.##[6] S. Corona, Nginx: A Practical Guide to High Per-formance, O'Reilly Media, 2017.##[7] R. Moutinho, Instant Varnish Cache How-to. Packt Publishing Ltd.‌, New York, 2013.##[8] P. H. Kamp, You're doing it wrong. Communi-cations of the ACM vol. 53 no.7, pp. 55-59, 2010.##[9] Web Page Test, Test a website's performance, Available: http://www.webpagetest.org/, [Access-ed: Sept. 1, 2015].##[10] GTmetrix, Website Speed and Performance Optimization, Available: http://gtmetrix.com/, [Accessed: Sept. 1, 2015].##[11] Pingdom, Website & Performance Monitoring, Available: http://tools.pingdom.com/fpt/, [Acc-essed: Sept. 1, 2015].##[12] S. Bakhtiyari, Performance Evaluation of the Apache Traffic Server and Varnish Reverse Proxies, Master Thesis, Dept. of Informatics, Univ. of Oslo, Oslo, Norway, May 23, 2012.##[13] L. D. Tobias, Caching HTTP A comparative study of caching reverse proxies Varnish and Nginx, Master Thesis, School of Informatics, Univ. of Skövde, Skövde, Sweden, June 8, 2014.##[14] N. Mathieu, Magento Site Performance Optimi-zation, Packt Publishing, 2014.##[15] B. T. Teklehaimanot, Virtualization of Video Streaming Functions. Ph.D. diss., Dept. of Computer Sciences, Univ. of Saarlandes, Saarb-rücken, Germany, 2016.##[16] D. Kumar, V. Tyagi, and M. Vijay, With High Level Fragmentation of Dataset, Load Balancing in Cloud Computing. Int. Journal of Innovations & Advancement in Computer Science, Vol 3, Issue 5, 2014.##[17] R. DiCosmo, S. Zacchiroli and G. Zavattaro, Aeolus: A component model for the cloud. J. In-formation and Computation, Vol. 239, pp. 100-121, 2014.##[18] A. Grocevs, N. Prokofyeva, Modern approaches to reduce webpage load times, J. Env. Tech. Resources, 2015, Vol. 3, pp. 87-91, 2015.##[19] J. Andjarwirawan, I. Gunawan and E. Kusumo, Varnish Web Cache Application Evaluation. Proc. Intelligence in the Era of Big Data, 4th Int. Conf. on Soft Computing, Intelligent Systems, and Information Technology, pp. 404-410, Sprin-ger,‌ 2015.##[20]‌ A. Loechel and S. Schmid, Comparison of Different Caching Techniques for High-Per-formance Web Map Services. Int. Journal of Spatial Data Infrastructures Research, Vol. 8, pp. 43-73, 2013.##[21] J. Nogueira, D. Gonzalez, L. Guardalben and S. Sargento. Over-The-Top Catch-up TV content-aware caching. 2016 IEEE Symposium on Com-puters and Communication (ISCC), pp. 1012-1017. IEEE, 2016.##[22] P. A. Baeza, Integración de un proxy inverso Varnish Cache con un panel de control Virtualmin para crear una plataforma de servicios de hospedaje Web. Thesis, Dept. Tècnica Sup-erior d'Enginyeria Informàtica, Univ. Politècnica de València, Valencia, Spain, 2015.##[23] L. P. Petroski, J. Matos, and J. E. Bertotti. Analysis of an event oriented web server as a reverse proxy with flexible load distribution. Ibe-roamerican Journal of Applied Computing, Vol. 4, no. 2, 2016.‌##[24] A. Martínez-Álvarez, S. Cuenca-Asensi, A. Ortiz, J. Calvo-Zaragoza, and L. A. V. Tejuelo, Tuning compilations by multi-objective optimization: Application to apache web server, Journal of Applied Soft Computing, Vol. 29, pp. 461-470.‌ 2016.##[25] D. S. Berger, K. R. Sitaraman and M. Harchol-Balter. AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network. 14th USENIX Symposium on Networked Sys-tems Design and Implementation, pp. 483-498. 2017.##[26] R. Viscomi, A. Davies and M. Duran: Using WebPageTest, Web Performance Testing for Novices and Power Users. O'Reilly Media, Octo-ber 2015.##[27] L. Čegan, Intelligent preloading of websites resources based on clustering web user sessions. 5th International Conference on IT Convergence and Security, pp. 1-4. IEEE, 2015.##[28] L. Čegan, Performance Evaluation of Preloading Resources for Web Pages, Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, Vol. 362. Springer, 2016.## ##