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ارائه روشی برای بخش‌بندی و برون‌سپاری اجرای کاربردهای مبتنی بر خدمات وب در سامانه‌های سیار با محدودیت تبادل داده Design and Evaluation of a Method for Partitioning and Offloading Web-based Applications in Mobile Systems with Bandwidth Constraints 1 1 برون‌سپاری اجرا به‌عنوان یک راه حل مهم برای اجرای برنامه‌های کاربردی سنگین روی سامانه‌های تلفن همراه است. در سامانه‌های تلفن همراه تغییرات پهنای‌باند در دسترس، به‌طور معمول اتفاق می‌افتد که روی بخش‌بندی بهینه تأثیر می‌گذارد. به‌منظور اجتناب از تکرار این فرایند سنگین بخش‌بندی باید به‌صورت تطبیقی و یک بار انجام شده و با تغییرات پهنای‌باند سازگار باشد. در این مقاله با در‌نظر‌گرفتن تغییرات پهنای‌­باند و محدودیت تبادل ­داده، مسأله بخش‌بندی تطبیقی و برون‌سپاری اجرای کاربردهای مبتنی بر خدمات وب به‌صورت سه مدل جداگانه با اهداف متفاوت شامل بهینه‌­سازی زمان اجرا، بهینه‌سازی مصرف انرژی و بهینه‎سازی ترکیب وزن­دار زمان اجرا و مصرف انرژی، فرموله شده و روشی ابتکاری مبتنی بر الگوریتم ژنتیک برای حل هر مسأله بهینه‌سازی در زمان معقول ارائه شده است. نتایج ‌شبیه‌­سازی و ارزیابی الگوریتم پیشنهادی نشان می­دهدکه در مقابل تغییرات پهنای­باند در دسترس سامانه سیار، عملکرد الگوریتم ارائه‌شده به‌نحو قابل ملاحظه‌­ای بهتر از کارهای مشابه است. 2 Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoided. Currently, a wide range of mobile applications are based on web services, which in turn influences the process of offloading and partitioning. As a result, mobile users are prone to face difficulties in data communications due to cost of preferences or connection quality. Taking into account the fluctuations of mobile connection bandwidth and thereby data rate constraints, the current paper proposes a method of adaptive partitioning and computation offloading in three forms. Accordingly, an optimization problem is primarily formulated to each of three main objectives under the investigation. These objectives include run time, energy consumption and the weighted composition of run time and energy consumption. Next, taking into consideration the time complexity of the optimization problems, a heuristic partitioning method based on Genetic Algorithm (GABP) is proposed to solve each of the three objectives and with the capability of acceptable performance maintenance in both dynamic and static partitionings. In order to evaluate and analyze the performance of the proposed approach, a simulation framework was built to run for random graphs of different sizes with the capability of setting specific bandwidth limits as target. The simulation results evidence improved performance against bandwidth fluctuations when compared to similar approaches. Moreover, it was also seen that once the problem circumstances are modified, the offloading can take place in the vicinity of the target node. Furthermore, we implemented the proposed method in form of an application on Android platform to conduct experiments on real applications. The experiments prove that those partitions of the applications requiring higher processing reqources rather than data rate are the best candidates for offloading.   22 3 2017/12/15 1396/9/24 2019/06/19 1398/3/29 سیاوش زاهدی Siawash Zahedi دانشکده مهندسی فناوری اطلاعات، دانشگاه صنعتی ارومیه ایران syavash.36@gmail.com صالح یوسفی Saleh Yousefi دانشکده فنی و مهندسی، دانشگاه ارومیه ایران s.yousefi@urmia.ac.ir وحید سلوک Vahid Solouk دانشکده مهندسی فناوری اطلاعات، دانشگاه صنعتی ارومیه ایران v.solouk@it.uut.ac.ir Computation offloading partitioning bandwidth adaptation web service data rate limitation برون‌سپاری اجرا بخش‌بندی تطبیق با پهنای‌باند خدمات وب محدودیت تبادل داده [1] K.Kumar and et al., "A survey of computation offloading for mobile systems", Mobile Networks and Applications, vol. 18(1), p p. 129-140, 2013.##[2] X.Ma and et al., "When mobile terminals meet the cloud: computation offloading as the bridge", IEEE Network, vol. 27(5), pp. 28-33, 2013.##[3] A.R.Khan and et al., "A survey of mobile cloud computing application models", Communications Surveys & Tutorials, IEEE, vol. 16(1), pp. 393-413, 2014.##[4] J.Niu, W. Song, and M. Atiquzzaman, "Band-width-adaptive partitioning for distributed execu-tion optimization of mobile applications", Journal of Network and Computer Applications, vol.37, pp. 334-347. 2014.##[5] K.Kumar and Y.-H. Lu, "Cloud computing for mobile users: Can offloading computation save energy?" Computer,vol. 43(4), pp. 51-56. 2010.##[6] B.-G.Chun and et al, "Clonecloud: elastic execution between mobile device and cloud", in Proceedings of the sixth conference on Computer systems, ACM, 2011.##[7] P. Di Lorenzo, S. Barbarossa, and S. Sardellitti, "Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing", arXiv preprint arXiv:1307, 2013, pp3835.##[8] F.Xia and et al, "Phone2Cloud: Exploiting computation offloading for energy saving on smartphones in mobile cloud computing", Information Systems Frontiers, vol. 16(1), pp. 95-111. 2014##[9] M.H. Tandel and V.S. Venkitachalam, "Cloud Computing in Smartphone: Is offloading a better-bet?", CS837-F12-MW-04A Wichita State Uni-versity, 2013.##[10] T.Verbelen and et al, "Graph partitioning algorithms for optimizing software deployment in mobile cloud computing", Future Generation Computer Systems, vol. 29(2), pp. 451-459. 2013.##[11] E.Cuervo and et al , "MAUI: making smart-phones last longer with code offload", in Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 2010.##[12] Y.Zhang and et al, "Refactoring android java code for on-demand computation offloading", in ACM SIGPLAN Notices .ACM, 2012.##[13] R.Kemp and et al, "Cuckoo: a computation offloading framework for smartphones", in Mobile Computing, Applications, and Services, Springer, pp. 59-79, 2012.##[14] D.Kovachev, T. Yu, and R. Klamma, "Adaptive computation offloading from mobile devices into the cloud", in Parallel and Distributed Pro-cessing with Applications (ISPA), IEEE 10th International Symposium on, 2012.##[15] X.Wei and et al, "MVR: An Architecture for Computation Offloading in Mobile Edge Computing", in Edge Computing (EDGE), IEEE International Conference on. 2017.##[16] http://developer.android.com/guide/components/services.html ( last accessed 12 Apr 2016).##[17] https://msdn.microsoft.com/enus/library/dd203052.aspx.##[18] h.sadeghi and A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks", Signal and Data Processing, vol.14(4), p p. 19-30. 2018.##[1] K.Kumar and et al., "A survey of computation offloading for mobile systems", Mobile Networks and Applications, vol. 18(1), p p. 129-140, 2013.##[2] X.Ma and et al., "When mobile terminals meet the cloud: computation offloading as the bridge", IEEE Network, vol. 27(5), pp. 28-33, 2013.##[3] A.R.Khan and et al., "A survey of mobile cloud computing application models", Communications Surveys & Tutorials, IEEE, vol. 16(1), pp. 393-413, 2014.##[4] J.Niu, W. Song, and M. Atiquzzaman, "Band-width-adaptive partitioning for distributed execu-tion optimization of mobile applications", Journal of Network and Computer Applications, vol.37, pp. 334-347. 2014.##[5] K.Kumar and Y.-H. Lu, "Cloud computing for mobile users: Can offloading computation save energy?" Computer,vol. 43(4), pp. 51-56. 2010.##[6] B.-G.Chun and et al, "Clonecloud: elastic execution between mobile device and cloud", in Proceedings of the sixth conference on Computer systems, ACM, 2011.##[7] P. Di Lorenzo, S. Barbarossa, and S. Sardellitti, "Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing", arXiv preprint arXiv:1307, 2013, pp3835.##[8] F.Xia and et al, "Phone2Cloud: Exploiting computation offloading for energy saving on smartphones in mobile cloud computing", Information Systems Frontiers, vol. 16(1), pp. 95-111. 2014##[9] M.H. Tandel and V.S. Venkitachalam, "Cloud Computing in Smartphone: Is offloading a better-bet?", CS837-F12-MW-04A Wichita State Uni-versity, 2013.##[10] T.Verbelen and et al, "Graph partitioning algorithms for optimizing software deployment in mobile cloud computing", Future Generation Computer Systems, vol. 29(2), pp. 451-459. 2013.##[11] E.Cuervo and et al , "MAUI: making smart-phones last longer with code offload", in Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 2010.##[12] Y.Zhang and et al, "Refactoring android java code for on-demand computation offloading", in ACM SIGPLAN Notices .ACM, 2012.##[13] R.Kemp and et al, "Cuckoo: a computation offloading framework for smartphones", in Mobile Computing, Applications, and Services, Springer, pp. 59-79, 2012.##[14] D.Kovachev, T. Yu, and R. Klamma, "Adaptive computation offloading from mobile devices into the cloud", in Parallel and Distributed Pro-cessing with Applications (ISPA), IEEE 10th International Symposium on, 2012.##[15] X.Wei and et al, "MVR: An Architecture for Computation Offloading in Mobile Edge Computing", in Edge Computing (EDGE), IEEE International Conference on. 2017.##[16] http://developer.android.com/guide/components/services.html ( last accessed 12 Apr 2016).##[17] https://msdn.microsoft.com/enus/library/dd203052.aspx.##[18] h.sadeghi and A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks", Signal and Data Processing, vol.14(4), p p. 19-30. 2018.## ##

بررسی روش‌های مؤثر بر عملکرد تجزیه‌گر دستور مستقل از متن آماری زبان فارسی Studying impressive parameters on the performance of Persian probabilistic context free grammar parser 1 1 عدم دقّت در طراحی دستورهای مستقل از متن و استفاده از ساختارهای نامناسب مانند فرم نرمال چامسکی به خودی خود می­‌تواند عملکرد تجزیه‌‍­گرهای آماری مستقل از متن را تضعیف کند. در این پژوهش ساختار ترکیبات عطفی درخت‌بانک فارسی را مورد بررسی قرار دادیم. نتایج حاصل از این پژوهش نشان می‌­دهد که با اضافه‌کردن وابستگی­‌های ساختاری به دستورهای مستقل از متن و اصلاح قواعد اولیه، می‌‌توان از ترکیبات عطفی رفع ابهام کرد و صحت عملکرد تجزیه­‌گر دستور مستقل از متن آماری را افزایش داد. فرض استقلال ضعیف، یکی از مشکلات مربوط به دستورهای مستقل از متن است که سعی شده است تا با تزریق وابستگی­‌های ساختاری از طریق نشانه­‌گذاری گره‌­های والد و فرزند مرتفع شود. تأثیر ریزدانگی و درشت­دانگی برچسب‌­های اجزای واژگانی کلام و همین‌طور ادغام ناپایانه­‌ها بر تجزیه‌­گر دستور مستقل از متن آماری فارسی از جمله مواردِ مورد بررسی قرار گرفته‌شده در این پژوهش­ است. 2 In linguistics, a tree bank is a parsed text corpus that annotates syntactic or semantic sentence structure. The exploitation of tree bank data has been important ever since the first large-scale tree bank, The Penn Treebank, was published. However, although originating in computational linguistics, the value of tree bank is becoming more widely appreciated in linguistics research as a whole. For example, annotated tree bank data has been crucial in syntactic research to test linguistic theories of sentence structure against large quantities of naturally occurring examples. The natural language parser consists of two basic parts, POS tagger and the syntax parser. A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some languages and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'. A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use knowledge of language gained from hand-parsed sentences to try to produce the most likely analysis of new sentences. These statistical parsers still make some mistakes, but commonly work rather well. Inaccurate design of context-free grammars and using bad structures such as Chomsky normal form can reduce accuracy of probabilistic context-free grammar parser. Weak independence assumption is one of the problems related to CFG. We have tried to improve this problem with parent and child annotation, which copies the label of a parent node onto the labels of its children, and it can improve the performance of a PCFG. In grammar, a conjunction (conj) is a part of speech that connects words, phrases, or clauses that are called the conjuncts of the conjunctions. In this study, we examined the conjunction phrases in the Persian tree bank. The results of this study show that adding structural dependencies to grammars and modifying the basic rules can remove conjunction ambiguity and increase accuracy of probabilistic context-free grammar parser. When a part-of-speech (PoS) tagger assigns word class labels to tokens, it has to select from a set of possible labels whose size usually ranges from fifty to several hundred labels depending on the language. In this study, we have investigated the effect of fine and coarse grain POS tags and merging non-terminals on Persian PCFG parser. 36 23 2017/12/152018/04/25 1397/2/5 2019/06/192019/07/10 1398/4/19 محمدباقر صادق زاده mohammadbagher sadeghzadeh دانشگاه صنعتی امیرکبیر ایران mohammadbaghersadeghzadeh@gmail.com محمدرضا رزازی mohammadreza razzazi دانشگاه صنعتی امیرکبیر ایران mohammadbaghersadeghzadeh@gmail.com مسعود قیومی Masood ghayoomi پژوهشگاه علوم انسانی و مطالعات فرهنگی ایران mohammadbaghersadeghzadeh@gmail.com Probabilistic context free grammar parser tree bank conjunction phrases parent annotation child annotation part of speech tags دستور مستقل از متن آماری تجزیه‌گر ترکیبات عطفی نشانه‌گذاری قواعد برچسب اجزای واژگانی کلام [1] J. E. Hopcroft, R. Motwani, and J. D. Ullman, "Automata theory, languages, and computation," International Edition, vol. 24, 2006.##[2] N. Chomsky, Syntactic structures. Walter de Gruy-ter, 2002.##[3] E. Charniak and M. Johnson, "Coarse-to-fine n-best parsing and MaxEnt discriminative reranking," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics: Association for Computational Linguistics, pp. 173-180. 2005.##[4] S. Green and C. D. Manning, "Better Arabic parsing: Baselines, evaluations, and analysis," in Proceedings of the 23rd International Conference on Computational Linguistics: Association for Computational Linguistics, pp. 394-402, 2010.##[5] M. Ghayoomi, "From Grammar Rule Extraction to Treebanking: A Bootstrapping Approach," in LREC, 2012, pp. 1912-1919.##[6] M. Razzazi, "Independent research at Amirkabir University Of Technology", 2006.##[7] A. Astiri, M. Kahani, R. Saeidi ,and A. Asgariyan, "Designing a parser for persian language",Inter-national Conference on persian language pro-cessing, 2012.##[8] H. Feili ,and G. Ghassem-Sani, "Unsupervised grammar induction using history based approach," Computer Speech & Language, vol. 20, no. 4, pp. 644-658, 2006.##[9] K. Lari and S. J. Young, "The estimation of stochastic context-free grammars using the inside-outside algorithm," Computer Speech & Language, vol. 4, no. 1, pp. 35-56, 1990.##[10] Sh.A. Poor, M.H. Poor ,and M. BijanKhan, "Identifying the location of the excess in Persian using PCFG", In Procedings of 13th Conference of Computer Society of Iran, 2008.##[11] M. Ghayoomi, "Persian Treebank and Autoa-mtion Parser", In Procedings of Computational Lingustic of Iran, 2013.##[12] M. Ghayoomi, "Word clustering for Persian statistical parsing," in Advances in Natural Language Processing: Springer, 2012, pp. 126-137.##[13] P. F. Brown, P. V. Desouza, R. L. Mercer, V. J. D. Pietra, and J. C. Lai, "Class-based n-gram models of natural language," Computational linguistics, vol. 18, no. 4, pp. 467-479, 1992.##[14] D. Jurafsky and J. H. Martin, Speech and Language Processing. Prentice Hall, Pearson Education International, 2014.##[15] T. L. Booth, "Probabilistic representation of formal languages," in Switching and Automata Theory, 1969., IEEE Conference Record of 10th Annual Symposium on, pp. 74-81, 1969.##[16] C. D. Manning and H. Schütze, Foundations of statistical natural language processing. MIT press, 1999.##[17] A. Bies et al., "Bracketing guidelines for treebank II style Penn Treebank project. Philadelphia: Linguistic Data Consortium," ed, 2013.##[18] C. Pollard, Head-driven phrase structure grammar, University of Chicago Press, 1994.##[19] M. Sadeghzadeh, M.Razzazi and M. Ghayoomi, "Investigating effective factors on Persian Parser", In Proceedings of the 3th Conference on Computatinal Lingustics, Tehran, 2013.##[20] D. Klein and C. D. Manning, "A parsing: fast exact Viterbi parse selection," in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Association for Computational Linguistics,vol.1, pp. 40-47, 2003.##[21] S. Bird, "NLTK: the natural language toolkit," in Proceedings of the COLING/ACL on Interactive presentation sessions: Association for Computational Linguistics, pp. 69-72, 2006.##[22] S. Abney and et al., "Procedure for quantitatively comparing the syntactic coverage of English grammars," in Proceedings of the workshop on Speech and Natural Language: Association for Computational Linguistics, pp. 306-311, 1991.##[23] K. Megerdoomian, "Developing a Persian part of speech tagger," in Proceedings of the 1st Workshop on Persian Language and Computer, , pp. 99-105, 2004.##[24] E. Rahimtoroghi, H. Faili, and A. Shakery, "A structural rule-based stemmer for Persian," in Telecommunications (IST), 2010 5th Inter-national Symposium on, 2010: IEEE, pp. 574-578, 2010.##[25] M. Mohseni and B. Minaei-Bidgoli, "A Persian Part-Of-Speech Tagger Based on Morphological Analysis," in LREC, 2010.##[26] M. Johnson, "The effect of alternative tree representations on tree bank grammars," in Proceedings of the Joint Conferences on New Methods in Language Processing and Computa-tional Natural Language Learning: Association for Computational Linguis-tics, pp. 39-48, 1998.##[27] M. Sadeghzadeh, M. Razzazi ,and H. Mahmoodi, " Injecting Structural Dependency into Persian PCFG", In Proceedings of 20th Conference of Computer Society of Iran, 2013.##[28] M. Collins, "Head-driven statistical models for natural language parsing," Computational linguistics, vol. 29, no. 4, pp. 589-637, 2003.##[1] J. E. Hopcroft, R. Motwani, and J. D. Ullman, "Automata theory, languages, and computation," International Edition, vol. 24, 2006.##[2] N. Chomsky, Syntactic structures. Walter de Gruy-ter, 2002.##[3] E. Charniak and M. Johnson, "Coarse-to-fine n-best parsing and MaxEnt discriminative reranking," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics: Association for Computational Linguistics, pp. 173-180. 2005.##[4] S. Green and C. D. Manning, "Better Arabic parsing: Baselines, evaluations, and analysis," in Proceedings of the 23rd International Conference on Computational Linguistics: Association for Computational Linguistics, pp. 394-402, 2010.##[5] M. Ghayoomi, "From Grammar Rule Extraction to Treebanking: A Bootstrapping Approach," in LREC, 2012, pp. 1912-1919.##[6] م. رزازی, "پژوهش مستقل دانشگاه صنعتی امیرکبیر," 1385.##[6] M. Razzazi, "Independent research at Amirkabir University Of Technology", 2006.##[7] ا. استیری, م. کاهانی, ر. سعیدی و ا. عسگریان, "طراحی ابزار پارسر زبان فارسی," کنفرانس بین المللی پردازش خط و زبان فارسی, 1391.##[7] A. Astiri, M. Kahani, R. Saeidi ,and A. Asgariyan, "Designing a parser for persian language",Inter-national Conference on persian language pro-cessing, 2012.##[8] H. Feili ,and G. Ghassem-Sani, "Unsupervised grammar induction using history based approach," Computer Speech & Language, vol. 20, no. 4, pp. 644-658, 2006.##[9] K. Lari and S. J. Young, "The estimation of stochastic context-free grammars using the inside-outside algorithm," Computer Speech & Language, vol. 4, no. 1, pp. 35-56, 1990.##[10] ش. ع. پور, م. ه. پور و م. ب. ج. خان, "شناسایی محل کسره اضافه در زبان فارسی با استفاده از گرامر مستقل از متن احتمالاتی"، ارائه شده در سیزدهمین کنفرانس سالانه انجمن کامپیوتر ایران 1386.##[10] Sh.A. Poor, M.H. Poor ,and M. BijanKhan, "Identifying the location of the excess in Persian using PCFG", In Procedings of 13th Conference of Computer Society of Iran, 2008.##[11] م. قیومی, "معرفی دادگان درختی و تجزیه‌گر خودکار فارسی," ارائه شده در هشتمین همایش زبان‌شناسی ایران, تهران، دانشگاه علامه‌طباطبایی, ۱۳۹۲.##[11] M. Ghayoomi, "Persian Treebank and Autoa-mtion Parser", In Procedings of Computational Lingustic of Iran, 2013.##[12] M. Ghayoomi, "Word clustering for Persian statistical parsing," in Advances in Natural Language Processing: Springer, 2012, pp. 126-137.##[13] P. F. Brown, P. V. Desouza, R. L. Mercer, V. J. D. Pietra, and J. C. Lai, "Class-based n-gram models of natural language," Computational linguistics, vol. 18, no. 4, pp. 467-479, 1992.##[14] D. Jurafsky and J. H. Martin, Speech and Language Processing. Prentice Hall, Pearson Education International, 2014.##[15] T. L. Booth, "Probabilistic representation of formal languages," in Switching and Automata Theory, 1969., IEEE Conference Record of 10th Annual Symposium on, pp. 74-81, 1969.##[16] C. D. Manning and H. Schütze, Foundations of statistical natural language processing. MIT press, 1999.##[17] A. Bies et al., "Bracketing guidelines for treebank II style Penn Treebank project. Philadelphia: Linguistic Data Consortium," ed, 2013.##[18] C. Pollard, Head-driven phrase structure grammar, University of Chicago Press, 1994.##[19] م. صادق‌زاده, م. رزازی و م. قیومی, "بررسی عوامل مؤثر بر عملکرد تجزیه گر آمارسی"، ارائه شده در سومین همایش زبانشناسی رایانشی, تهران, 1393.##[19] M. Sadeghzadeh, M.Razzazi and M. Ghayoomi, "Investigating effective factors on Persian Parser", In Proceedings of the 3th Conference on Computatinal Lingustics, Tehran, 2013.##[20] D. Klein and C. D. Manning, "A parsing: fast exact Viterbi parse selection," in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Association for Computational Linguistics,vol.1, pp. 40-47, 2003.##[21] S. Bird, "NLTK: the natural language toolkit," in Proceedings of the COLING/ACL on Interactive presentation sessions: Association for Computational Linguistics, pp. 69-72, 2006.##[22] S. Abney and et al., "Procedure for quantitatively comparing the syntactic coverage of English grammars," in Proceedings of the workshop on Speech and Natural Language: Association for Computational Linguistics, pp. 306-311, 1991.##[23] K. Megerdoomian, "Developing a Persian part of speech tagger," in Proceedings of the 1st Workshop on Persian Language and Computer, , pp. 99-105, 2004.##[24] E. Rahimtoroghi, H. Faili, and A. Shakery, "A structural rule-based stemmer for Persian," in Telecommunications (IST), 2010 5th Inter-national Symposium on, 2010: IEEE, pp. 574-578, 2010.##[25] M. Mohseni and B. Minaei-Bidgoli, "A Persian Part-Of-Speech Tagger Based on Morphological Analysis," in LREC, 2010.##[26] M. Johnson, "The effect of alternative tree representations on tree bank grammars," in Proceedings of the Joint Conferences on New Methods in Language Processing and Computa-tional Natural Language Learning: Association for Computational Linguis-tics, pp. 39-48, 1998.##[27] م. صادقزاده, م. رزازی و ح. محمودی, "تزریق وابستگی ساختاری به دستورهای مستقل از متن آماری زبان فارسی از طریق نشانه‌گذاری قواعد," ارائه شده در بیستمین کنفرانس ملی سالانه انجمن کامپیوتر ایران, مشهد, 1393.##[27] M. Sadeghzadeh, M. Razzazi ,and H. Mahmoodi, " Injecting Structural Dependency into Persian PCFG", In Proceedings of 20th Conference of Computer Society of Iran, 2013.##[28] M. Collins, "Head-driven statistical models for natural language parsing," Computational linguistics, vol. 29, no. 4, pp. 589-637, 2003.## ##

یک مدل آماری جهت ارزیابی سامانه‌های پرسش و پاسخ تعاملی با استفاده از رگرسیون A New Statistical Model for Evaluation Interactive Question Answering Systems Using Regression 1 1 همانند بسیاری از زمینه‌­های دیگر زبان‌­شناسی محاسباتی، ارزیابی نقش مهمی در سامانه‌­های پرسش و پاسخ تعاملی ایفا می‌کند. با این وجود، در زمینه ارزیابی سامانه‌های پرسش و پاسخ تعاملی به‌طورتقریبی هیچ روش خاصی وجود ندارد که به ارزیابی کلی این سامانه‌­ها پرداخته و همواره انسان باید در فرآیند ارزیابی مشارکت داشته باشد. ارائه مدلی که بتواند جایگزین انسان در فرآیند ارزیابی شود، یکی از موضوعات مورد توجه در این حوزه است. در این مقاله، یک مدل آماری مناسب برای ارزیابی سامانه‌های پرسش و پاسخ تعاملی جهت جایگزین‌کردن به جای انسان توسط مجموعه‌ای از ویژگی‌های جدید و رگرسیون ارائه شده است. با استفاده از چهار سامانه تعاملی موجود پایگاه داده‌ای مناسب ایجاد شد. تعداد 540 نمونه به‌عنوان داده مناسب در نظر گرفته شد تا مجموعه آزمون و آموزش بر اساس آن تشکیل شود. ابتدا پیش‌پردازش بر روی مکالمات صورت پذیرفت و بر اساس روابط تعریف‌شده، ویژگی‌های آماری از متن مکالمه‌ها استخراج و بر اساس آن ماتریس ویژگی تشکیل و سپس با استفاده از انواع رگرسیون سعی شد تا بهترین مدل استخراج شود که در‌نهایت رگرسیون غیرخطی سری توانی با RMSE به میزان 13/0 بهترین مدل را ارائه کرد. 2 The development of computer systems and extensive use of information technology in the everyday life of people have just made it more and more important for them to make quick access to information that has received great importance. Increasing the volume of information makes it difficult to manage or control. Thus, some instruments need to be provided to use this information. The QA system is an automated system for obtaining the correct answers to questions posed by the human in the natural language. In these systems, if the response is found, and if it is not the user's expected response or if it needs more information, there is no possibility of exchanging information between the system and the user to ask more questions and get answers related to it. To solve this problem, interactive Question answering (IQA) systems were created. Interactive question answering (IQA) systems are associated with linguistic ambiguous structures, so these systems are more accurate than QA systems. Regarding the probability of ambiguity (ambiguity in the user question or ambiguity in the answer provided by the system), the repetition is possible in these systems to obtain the clarity. No standard methods have been developed on IQA systems evaluation, and the existing evaluation methods have been developed based on the methods used in QA and dialogue systems. In evaluating IQA systems, in addition to quantitative evaluation, a qualitative evaluation is used. It requires users’ participation in the evaluation process to determine the success level of interaction between the system and the user. Evaluation plays an important role in the IQA systems. In the context of evaluating IQA systems, there is partially no specific methodology for evaluating these systems in general. The main problem with designing an assessment method for IQA systems lies in the rare possibility to predict the interaction part. To this end, human needs to be involved in the evaluation process. In this paper, an appropriate model is presented by introducing a set of built-in features for evaluating IQA systems. To conduct the evaluation process, four IQA systems were considered based on the conversation exchanged between users and systems. Moreover, 540 samples were considered as suitable data to create a test and training set. The statistical characteristics of each conversation were extracted after performing the preprocessing on them. Then a feature matrix was formed based on the obtained characteristics. Finally, using linear and nonlinear regression, human thinking was predicted. As a result, the nonlinear power regression with 0.13 Root Mean Square Error (RMSE) was the best model. 48 37 2017/12/152018/04/252017/11/24 1396/9/3 2019/06/192019/07/102019/01/9 1397/10/19 محمدمهدی حسینی mohammad mehdi hosseini دانشکده مهندسی برق و کامپیوتر، دانشگاه آزاد اسلامی، واحد شاهرود ایران hosseini_mm@shahroodut.ac.ir مرتضی زاهدی Morteza zahedi دانشکده مهندسی کامپیوتر و فن‌آوری اطلاعات ایران zahedi@ganjineh.co.ir حمید حسن پور hamid Hassanpour دانشکده مهندسی کامپیوتر و فن‌آوری اطلاعات ایران h.hassanpour@shahroodut.ac.ir Evaluation Interactive Question Answering Systems Nonlinear Regression Feature Extraction ارزیابی سامانه پرسش و پاسخ تعاملی رگرسیون غیرخطی استخراج ویژگی [1] S. Shahriini, S. Zahedi, "Interactive Question answering System Using Artificial Intelligence Techniques", Senior Thesis, Shahrood University of Technology, Faculty of Computer and Information Technology, 2015.##[2] M.M. Hosseini, M. Zahedi, "Improvement of the response provided in interactive question answering systems using neural network", Eighth International Conference on Information and Knowledge Technology, pp. 84-91, 2016.##[3] Bouziane, Abdelghani, Bouchiha, Doumi, and Malki, “Question Answering Systems: Survey and Trends”, Procedia Computer Science, pp. 366-375, 2015.##[4] Bao, Junwei, Nan Duan, Ming Zhou, and Tiejun Zhao, "Knowledge-based question answering as machine translation," Cell 2, no. 6, 2014.##[5] C. Guinaudeau, M. Strube, “Graph-based Local Coherence Modeling”, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 93–103, 2013.##[6] Hartawan, Andrei, and Derwin Suhartono, “Using Vector Space Model in Question Answering System”, Procedia Computer Science, pp. 305-311, 2015.##[7] Höffner, Konrad, S. Walter, E. Marx, R. Usbeck, J. Lehmann, and A. Ngomo, “Survey on challenges of question answering in the semantic web”, Semantic Web 8, no. 6, pp.895-920, 2017.##[8] Kelly, Diane, P. B. Kantor, E. L. Morse, J. Scholtz, and Y. Sun, “Questionnaires for eliciting evaluation data from users of interactive question answering systems,” Natural Language Engineering 15, no. 1, , pp. 119-141, 2009.##[9] L. C. Yew, “Rouge: A package for automatic evaluation of summaries,” In Text summarization branches out: Proceedings of the ACL-04 workshop, vol. 8, 2004.##[10] L. Vanessa, V. Uren, M. Sabou and E. Motta. “Is question answering fit for the semantic web? A survey,” Semantic Web 2, no. 2, pp.125-155, 2011.##[11] M. Amit, and S. K. Jain, “A survey on question answering systems with classification,” Journal of King Saud University-Computer and Information Sciences 28, no. 3, pp. 345-361,2016.##[12] M. Mansoori, and H. Hassanpour, “Boosting passage retrieval through reuse in question answering,” International Journal of Engineering 25, no. 3, pp.187-196, 2012.##[13] Y. Boreshban, H. Yousefinasab, S. A. Mirroshandel, “Providing a Religious Corpus of Question Answering System in Persian”, Journal of Signal and Data Processing (JSDP),Vol 15, no 1, pp.87-102, 2018.##[14] N. Wacholder, S. G. Small, B. Bai, D. Kelly, R. trittman, S. Ryan, R. Salkin, “Designing a Realistic Evaluation of an End-to-end Interactive Question Answering System.” In LREC. 2004.##[15] Quarteroni, Silvia and S. Manandhar, “Designing an interactive open-domain question answering system,” Natural Language Engineering 15, no. 1, pp. 73-95,2009.##[16] S. Ying, P. B. Kantor and E. L. Morse, “Using cross-evaluation to evaluate interactive QA systems.” Journal of the Association for Information Science and Technology 62, no. 9, pp. 1653-1665, 2011.##[1] شهرآیینی، سلیمه، زاهدی، مرتضی،”سیستم پاسخگوی تعاملی با استفاده از تکنیک‌های هوش مصنوعی”، دانشگاه صنعتی شاهرود، دانشکده کامپیوتر و فناوری اطلاعات، پایان نامه ارشد، 1394.##[1] S. Shahriini, S. Zahedi, "Interactive Question answering System Using Artificial Intelligence Techniques", Senior Thesis, Shahrood University of Technology, Faculty of Computer and Information Technology, 2015.##[2] حسینی، محمدمهدی، زاهدی، مرتضی، “بهبود پاسخ ارائه‌شده در سیستم‌های پرسش و پاسخ تعاملی با استفاده از شبکه عصبی”، هشتمین کنفرانس بین‌المللی فناوری اطلاعات و دانش، صفحات 84-91، 1395.##[2] M.M. Hosseini, M. Zahedi, "Improvement of the response provided in interactive question answering systems using neural network", Eighth International Conference on Information and Knowledge Technology, pp. 84-91, 2016.##[3] Bouziane, Abdelghani, Bouchiha, Doumi, and Malki, “Question Answering Systems: Survey and Trends”, Procedia Computer Science, pp. 366-375, 2015.##[4] Bao, Junwei, Nan Duan, Ming Zhou, and Tiejun Zhao, "Knowledge-based question answering as machine translation," Cell 2, no. 6, 2014.##[5] C. Guinaudeau, M. Strube, “Graph-based Local Coherence Modeling”, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 93–103, 2013.##[6] Hartawan, Andrei, and Derwin Suhartono, “Using Vector Space Model in Question Answering System”, Procedia Computer Science, pp. 305-311, 2015.##[7] Höffner, Konrad, S. Walter, E. Marx, R. Usbeck, J. Lehmann, and A. Ngomo, “Survey on challenges of question answering in the semantic web”, Semantic Web 8, no. 6, pp.895-920, 2017.##[8] Kelly, Diane, P. B. Kantor, E. L. Morse, J. Scholtz, and Y. Sun, “Questionnaires for eliciting evaluation data from users of interactive question answering systems,” Natural Language Engineering 15, no. 1, , pp. 119-141, 2009.##[9] L. C. Yew, “Rouge: A package for automatic evaluation of summaries,” In Text summarization branches out: Proceedings of the ACL-04 workshop, vol. 8, 2004.##[10] L. Vanessa, V. Uren, M. Sabou and E. Motta. “Is question answering fit for the semantic web? A survey,” Semantic Web 2, no. 2, pp.125-155, 2011.##[11] M. Amit, and S. K. Jain, “A survey on question answering systems with classification,” Journal of King Saud University-Computer and Information Sciences 28, no. 3, pp. 345-361,2016.##[12] M. Mansoori, and H. Hassanpour, “Boosting passage retrieval through reuse in question answering,” International Journal of Engineering 25, no. 3, pp.187-196, 2012.##[13] برشبان، یاسمن، یوسفی نسب، حامد، میرروشندل، سید ابوالقاسم " ارایه یک پیکره پرسش و پاسخ مذهبی در زبان فارسی"، فصل‌نامه پردازش علائم و داده‌ها، دوره 15، شماره 1، صفحات 87- 102، 1397.##[13] Y. Boreshban, H. Yousefinasab, S. A. Mirroshandel, “Providing a Religious Corpus of Question Answering System in Persian”, Journal of Signal and Data Processing (JSDP),Vol 15, no 1, pp.87-102, 2018.##[14] N. Wacholder, S. G. Small, B. Bai, D. Kelly, R. trittman, S. Ryan, R. Salkin, “Designing a Realistic Evaluation of an End-to-end Interactive Question Answering System.” In LREC. 2004.##[15] Quarteroni, Silvia and S. Manandhar, “Designing an interactive open-domain question answering system,” Natural Language Engineering 15, no. 1, pp. 73-95,2009.##[16] S. Ying, P. B. Kantor and E. L. Morse, “Using cross-evaluation to evaluate interactive QA systems.” Journal of the Association for Information Science and Technology 62, no. 9, pp. 1653-1665, 2011.## ##

الگوی حاشیه‌گذاری فارسی بر اساس نظریۀ گروه‌های خودگردان و وابستگی‌های جهانی An annotation scheme for Persian based on Autonomous Phrases Theory and Universal Dependencies 1 1 این مقاله فرایند طراحی الگویی را گزارش می‌­کند که برای حاشیه­‌گذاری ساخت وابستگی زبان فارسی به‌عنوان بخشی از پروژۀ تهیۀ درخت‌بانک وابستگی برای زبان فارسی تدوین شده است. این الگو بر توصیفی جامع از نحو زبان فارسی بنا بر نظریه­ای تحت عنوان نظریۀ گروه‌­های خودگردان پایه­‌گذاری شده است. تأکید عمده در نظریۀ بالا بر ضرورت توجه به اهمیت مفهوم گروه­ در تحلیل­‌های وابستگی به‌علت واقعیت شناختی آن و مفهوم ظرفیت نیز در آنجا فراتر از فعل گسترده شده است. بر آن مبنا، هر وابستۀ هر نوع هسته به‌‌عنوان متمم یا افزوده طبقه‌­بندی می­شود؛ به‌علاوه، برای اینکه الگوی حاصل آن‌طور‌که باید برای مخاطبان احتمالی مفهوم باشد، جدیدترین الگوی حاشیه‌­گذاری معیار موجود، موسوم به وابستگی­‌های جهانی، متناسب با نیازهای چهارچوب اتخاذی مطابقت یافته است. نتیجه، مجموعه برچسبی است متشکل از پنجاه و سه رابطۀ وابستگی، شامل پانزده برچسب جدید در کنار برچسب­‌هایی که از وابستگی‌­های جهانی وام گرفته شده است.   2 A treebank is a corpus with linguistic annotations above the level of the parts of speech. During the first half of the present decade, three treebanks have been developed for Persian either originally or subsequently based on dependency grammar: Persian Treebank (PerTreeBank), Persian Syntactic Dependency Treebank, and Uppsala Persian Dependency Treebank (UPDT). The syntactic analysis of a sentence in these corpora involves a series of relations introducing each word in the sentence as a dependent of another, referred to as the head. Examination of head-dependent pairs extracted from similar contexts in the above treebanks reveals frequent, apparently systematic inconsistencies observed particularly in the cases of nominal and adjectival heads. This can be explained in terms of the failure to postulate valency structures for nouns and adjectives as well as for verbs, taking for granted that tokens receive the proper labels regardless of such an assumption. When the notion of valency was borrowed from chemistry to refer to the number of controlled arguments, it was meant to apply only to verbs. Later developments of dependency grammar included the proposal of nominal and adjectival valency as well. The significance of the idea seems to have been underestimated, though. It has been highly improbable, therefore, for developers of dependency treebanks to design their annotation schemes otherwise. As far as Persian is concerned, Uppsala Persian Dependency Treebank and Dependency Persian Treebank (DepPerTreeBank, the dependency version of PerTreeBank) have used the Stanford Typed Dependencies. The later version of the former treebank, Persian Universal Dependency Treebank, has used the Universal Dependencies. These are standard annotation schemes that do not recognize valency for nouns and adjectives. Furthermore, Persian Syntactic Dependency Treebank has used its own set of dependency relations, where little attention has been paid to the idea. This paper reported the design process of a scheme for annotation of Persian dependency structure as part of an ongoing project of developing a dependency treebank for Persian. The scheme was based on a comprehensive description of Persian syntax according to a theory introduced as the Autonomous Phrases Theory. The main idea is that the significance of phrases should be appreciated in dependency analyses due to their cognitive reality, and the notion of valency is also extended beyond verbs, on which basis every dependent of whatever head type is classified as either a complement or an adjunct. Moreover, to make the resulting annotation scheme reasonably intelligible to the target audience, the latest standard available annotation scheme, Universal Dependencies (UD), was adapted to suit the requirements of the adopted framework. The outcome was a tag set of fifty-three dependency relations, including fifteen original labels and the rest borrowed from the universal dependencies. Although it provides more detailed annotation than UD does by making finer distinctions, our scheme does not involve too many tags more than UD does, mainly because a large number of the additional relations are shared by two or three head types.   60 49 2017/12/152018/04/252017/11/242018/06/22 1397/4/1 2019/06/192019/07/102019/01/92019/09/4 1398/6/13 نوید برادران همتی Navid Baradaran Hemmati مرکز ویراستاری انگلیسی دانشگاه کردستان ایران navidbh@gmail.com امید طبیب زاده Omid Tabibzadeh پژوهشکدۀ زبان‌شناسی، پژوهشگاه علوم انسانی و مطالعات فرهنگی ایران otabibzadeh@yahoo.com annotation Persian treebank Universal Dependencies valency حاشیه‌گذاری درخت‌بانک ظرفیت فارسی وابستگی‌های جهانی [1] J.Nivre, “Treebanks”, In A. Lüdeling & M. Kytö (Eds.), Corpus Linguistics: An International Handbook, Berlin, Germany: Mouton de Gruyter. 2008, pp. 223-255##[2] L.Tesnière, Éléments de Syntaxe Structurale (Elements of Structural Syntax), Paris, France: Klincksieck, 1959.##[3] V. Ágel and K. Fischer, Dependency grammar and valency theory. In B. Heine & H. Narrog (Eds.), The Oxford Handbook of Linguistic Analysis. Oxford, UK: Oxford University Press, 2010, pp. 223-255.##[4] M. Seraji, Morphosyntactic corpora and tools for Persian, (Doctoral Dissertation), Uppsala University, Uppsala, Sweden, 2015.##[5] M. Ghayoomi and J. Kuhn, “Converting an HPSG-based treebank into its parallel dependency-based Treebank”, In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, pp. 802-809, 2014.##[6] M.de Marneffe, B. MacCartney, and C. Manning, “Generating typed dependency parses from phrase structure parses”, In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy, 2006, pp. 449–454.##[7] M. de Marneffe, and C. D. Manning, “The Stanford Typed Dependencies representation”, In Proceedings of the COLING’08 Workshop on Cross-Framework and Cross-Domain Parser Evaluation , Manchester, UK, 2008, pp. 1–8.##[8] J. Nivre and et al, Universal Dependencies. Retrieved from http://www.universaldependenc-ies.org/u/dep/index.html (last accessed February 2016), 2014.##[9] M. Rasooli, M.Kouhestani, and A. Moloodi, “Development of a Persian syntactic dependency Treebank”, In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT). Atlanta, USA, 2013.##[10] O.Tabibzadeh,”dasture zabâne fârsi: bar asâse nazariyeye goruhhâye xodgardân dar dasture vâbastegi (Persian Grammar: A Theory of Autonomous Phrases Based on Dependency Grammar)”, Tehran, Iran: Nashr-e-Markaz Publishing Co, 2012.##[11] N. Baradaran Hemmati, “mas’aleye zarfiyate esm va sefat dar deraxtbânkhâye vâbastegiye nahviye fârsi (The issue of valency for nouns and adjectives in the syntactic dependency treebanks for Persian)”, In Proceedings of the 2nd National Conference on Applied Research in Computational Linguistics , Shiraz, Iran, pp. 31-47, 2019.##[12] M. de Marneffe, T. Dozat, N.Silveira, K. Haverinen, F. Ginter, J.Nivre, and C. D. Manning, “Universal Stanford Dependencies: A cross-linguistic typology”, In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14), Rey-kjavik, Iceland, pp. 4585–4592, 2014.##[13] O.Tabibzadeh and N. Baradaran Hemmati, “do now’ vâbasteye ezâfe’i dar zabâne fârsi: mozafon’elayhe esmi va mozâfon’elayhe vasfi (Two kinds of genitive dependents in Persian: Nominal genitives and attributive genitives)”, A Quarterly Journal of Persian Language and Literature (adabpažuhi), vol. 9, no. 32, pp. 151-172. 2015.##[14] R. Tsarfaty, “A unified morpho-syntactic scheme of Stanford Dependencies,” In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria. pp. 578–584, 2013.##[15] M. Rezaei Sharifabadi and P. Khosravizadeh Forushani, “barĉasbzaniye xodkâre naqšhâye ma’nâyi dar jomalâte fârsi be komake deraxthâye vâbastegi (Automatic semantic role labeling in Persian sentences using dependency treebanks)” Quarterly Journal of Signal and Data Processing, vol. 13, no. 2, pp. 27-38, 2016.##[1] J.Nivre, “Treebanks”, In A. Lüdeling & M. Kytö (Eds.), Corpus Linguistics: An International Handbook, Berlin, Germany: Mouton de Gruyter. 2008, pp. 223-255##[2] L.Tesnière, Éléments de Syntaxe Structurale (Elements of Structural Syntax), Paris, France: Klincksieck, 1959.##[3] V. Ágel and K. Fischer, Dependency grammar and valency theory. In B. Heine & H. Narrog (Eds.), The Oxford Handbook of Linguistic Analysis. Oxford, UK: Oxford University Press, 2010, pp. 223-255.##[4] M. Seraji, Morphosyntactic corpora and tools for Persian, (Doctoral Dissertation), Uppsala University, Uppsala, Sweden, 2015.##[5] M. Ghayoomi and J. Kuhn, “Converting an HPSG-based treebank into its parallel dependency-based Treebank”, In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, pp. 802-809, 2014.##[6] M.de Marneffe, B. MacCartney, and C. Manning, “Generating typed dependency parses from phrase structure parses”, In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy, 2006, pp. 449–454.##[7] M. de Marneffe, and C. D. Manning, “The Stanford Typed Dependencies representation”, In Proceedings of the COLING’08 Workshop on Cross-Framework and Cross-Domain Parser Evaluation , Manchester, UK, 2008, pp. 1–8.##[8] J. Nivre and et al, Universal Dependencies. Retrieved from http://www.universaldependenc-ies.org/u/dep/index.html (last accessed February 2016), 2014.##[9] M. Rasooli, M.Kouhestani, and A. Moloodi, “Development of a Persian syntactic dependency Treebank”, In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT). Atlanta, USA, 2013.##[10]طبیب‌زاده، ا.، دستور زبان فارسی: بر اساس نظریۀ گروه‌های خودگردان در دستور وابستگی، تهران، نشر مرکز، ١٣٩١.##[10] O.Tabibzadeh,”dasture zabâne fârsi: bar asâse nazariyeye goruhhâye xodgardân dar dasture vâbastegi (Persian Grammar: A Theory of Autonomous Phrases Based on Dependency Grammar)”, Tehran, Iran: Nashr-e-Markaz Publishing Co, 2012.##[11] برادران همتی، ن.، مسألۀ ظرفیت اسم و صفت در درخت‌بانک‌های وابستگی نحوی زبان فارسی، در مجموعه مقالات دومین کنفرانس ملی پژوهش‌های کاربردی در زبان‌شناسی رایانشی (صص. 31-47). شیراز، 1398.##[11] N. Baradaran Hemmati, “mas’aleye zarfiyate esm va sefat dar deraxtbânkhâye vâbastegiye nahviye fârsi (The issue of valency for nouns and adjectives in the syntactic dependency treebanks for Persian)”, In Proceedings of the 2nd National Conference on Applied Research in Computational Linguistics , Shiraz, Iran, pp. 31-47, 2019.##[12] M. de Marneffe, T. Dozat, N.Silveira, K. Haverinen, F. Ginter, J.Nivre, and C. D. 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آشکارسازی رخداد آتش به‌کمک تصاویر ویدئویی در محیط‌‌‌های سرباز شهری Fire detection using video sequences in urban out-door environment 1 1 سامانه‌های تشخیص حریق با توجه به قابلیت گسترش سریع و توانایی بالای آتش در تخریب، از اهمیت بالایی برخوردار هستند. روش‌های سنتی شناسایی آتش که بر مبنای اثرات ناشی از آتش مانند دود و حرارت هستند، در محیط‌های بزرگ و سرباز کارایی لازم را ندارند. هدف از این مقاله ارائه‌ روشی کارآمد برای تشخیص آتش به‌کمک تصاویر ویدئویی در محیط‌های سرباز شهری با تمرکز بر انبار شرکت گاز با هزینه محاسباتی پایین است. در روش پیشنهادی از مؤلفه رنگ و ویژگی‌های زمانی و مکانی که خاصیت تفکیک‌کنندگی بالایی دارند، استفاده شده است. ابتدا نواحی نامزد آتش با کمک مدل رنگی انتخاب و ﺳﭙﺲ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺗﻐﯿﯿﺮات ﻧﺎﻣﻨﻈﻢ ﭘﯿﻮﺳﺘﻪ ﺷﻌﻠﻪ آﺗﺶ در قاب‌های متوالی و ﭘﺮاﮐﻨﺪﮔﯽ ﺳﻄﻮح روﺷﻨﺎیﯽ ﮐﺎﻧﺎل ﻗﺮﻣﺰ، ﻧﻮاﺣﯽ آﺗﺶ در ﺗﺼﺎویﺮ اﺳﺘﺨﺮاج می‎‌شوند. آزمایش‌های انجام‌شده حاکی از آن است که روش پیشنهادی علاوه‌بر این‌که در نمونه ویدئوهای بررسی‌شده، قابلیت تشخیص صد‌درصدی وقوع آتش را در محیط‌های سرباز شهری دارد، زمان تشخیص را در شرایط  مشابه نسبت به روش مقاله پایه 35/87 درصد کاهش داده است. 2 Nowadays automated early warning systems are essential in human life. One of these systems is fire detection which plays an important role in surveillance and security systems because the fire can spread quickly and cause great damage to an area. Traditional fire detection methods usually are based on smoke and temperature detectors (sensors). These methods cannot work properly in large space and out-door environments. They have high false alarm rates, and to cover the entire area, many smoke or temperature fire detectors are required, that is expensive. Due to the rapid developments in CCTV (Closed Circuit Television) surveillance system in recent years and video processing techniques, there is a big trend to replace conventional fire detection techniques with computer vision-based systems. This new technology can provide more reliable information and can be more cost-effective. The main objective of fire detection systems is high detection accuracy, low error rate and reasonable time detect. The video fire detection technology uses CCD cameras to capture images of the observed scene, which provides abundant and intuitive information for fire detection using image processing algorithms. This paper presents an efficient fire detection system which detects fire areas by analyzing the videos that are acquired by surveillance cameras in urban out-door environment, especially in storage of Yazd Gas Company. Proposed method uses color, spatial and temporal information that makes a good distinction between the fire and objects which are similar to the fire.  The purpose is achieved using multi- filter. The first filter separates red color area as a primary fire candidate. The second filter operates based on the difference between fire candidate areas in the sequence of frames. In the last filter, variation of red channel in the candidate area is computed and compared with the threshold which is updating continuously. In the experiments, the performance of these filters are evaluated separately. The proposed final system is a combination of all filters. Experimental results show that the precision of the final proposed system in urban out-door environment is 100%, and our technique achieves the average detection rate of 87.35% which outperforms other base methods.  78 61 2017/12/152018/04/252017/11/242018/06/222016/11/4 1395/8/14 2019/06/192019/07/102019/01/92019/09/42019/06/19 1398/3/29 فائزه کریمی زارچی faeze karimi zarchi دانشکده مهندسی کامپیوتر، دانشگاه یزد ایران faeze.karimi@stu.yazd.ac.ir ولی درهمی vali derhami دانشکده مهندسی کامپیوتر، دانشگاه یزد ایران vderhami@yazd.ac.ir علی‌محمد لطیف Alimohammad Latif دانشکده مهندسی کامپیوتر، دانشگاه یزد ایران alatif@yazd.ac.ir علی ابراهیمی ali ebrahimi واحد فناوری اطلاعات و ارتباطات شرکت گاز استان یزد ایران Ebrahimi@nigc-yazd.ir Fire Detection Video Sequences Image Processing Detection Time تشخیص هوشمند وقوع آتش تصاویر ویدئویی پردازش تصویر زمان تشخیص [1] Kh. Rezaee, M. Ghezelbash, and J. hadadnia, ”Accurate and intelligent fire monitoring system in various environments using image processing,” 20th Iranian Conference on Electric, 2012.##[2] J. Chen, Y. He, and J. Wang, ”Multi-feature fusion based fast video flame detection, ”Building Environment Journal, vol. 45, no. 5, pp. 1113–1122, 2010.##[3] W. Wang, and H. Zhou, “Fire detection based on flame color and area,” International Conference on Computer Science and Automation Engineering, vol. 3, pp. 222–226,2012.##[4] Q. Zhou, X. Yang, and L. Bu, “Analysis of shape features of flame and interference image in video fire detection,” Chinese Automation Congress (CAC, pp. 633–637), 2015.##[5] A. E Cetin, et al,”Video fire detection - review,” Digital Signal Processes A Reveiw Journal, vol. 23, no.6, pp. 1827–1843, 2013.##[6] H. Chao, and K. Tzu-Hsin, “Real-time video-based fire smoke detection system,” International Conference on Advanced Inteliigent Mechatronics, pp.1845-1850, 2009.##[7] B. Ko, K. Cheong, and J, Nam,”Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks,” Fire Safety Journal, vol. 45, no.4, pp. 262-270, 2010.##[8] B. C. KO, K. H. Cheong, and J-. Nam,”Fire detection based on vision sensor and support vector machines,”Fire Safety Journal, vol. 44, no. 3, pp. 322–329, 2009.##[9] W. Phillips, and M. Shah, and N. D. V. Lob, “Flame recognition in video,” Pattern Recognition Letters, vol. 23, pp. 319-327, 2002.##[10] J. Seebamrungsat, S. Praising, and P. Riyamongkol,”Fire detection in the buildings using image processing,” Third ICT International Student Project Conference, pp. 95–98, 2014.##[11] G. F. Shidik, et al., “Multi color feature background subtraction and time frame selection for fire detection,” IEEE International Con-ference on Robotics, Biomimetics, and Inte-lligent Computational Systems (ROBIONETICS), pp. 115-120, 2013.##[12] T. Ceilik, H. Ozkaramanli, and H. Demirel, ”Fire pixel classification using fuzzy logic and statistical color model,” in IEEE Inter-national Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 1, pp. 1000–1205, 2007.##[13] B.-H. Cho, J.-W. Bae, and S.-H. Jung, ”Image processing-based fire detection system using statistic color model,” 8th International Conference on Advanced Language Processing and Web Information Technology, pp. 245–250, 2008.##[14] T-H. Chen, P-H. Wu, and Y. Chiou,”An early fire-detection method based on image processing,” in International Conference on Image Processing, (ICIP ’04), vol. 3, pp. 1707–1710, 2004.##[15] S. C. Pol, et al., "Fire detection using image processing and sensors," International Journal of Engineering Trends and Applications, vol. 3, pp. 85–87, 2016.##[16] B. U. Toreyin, Y. Dedeoglu, U. Gudukbay, and A. E. Cetin,”Computer vision based method for real-time fire and flame detection,” Pattern Recognition Letters, vol. 27, pp. 49–58, 2006.##[17] T. X. Truong, and J. Kim, ”Fire flame detection in video sequences using multi-stage pattern re-cognition techniques,” Engineering Applications of Artificial Intelligence, vol. 25, no. 7, pp. 1365-1372, 2012.##[18] Y. Zhao, and G. Tang, ”Fire video recognition based on flame and smoke characteristics,” in The 2nd International Conference on Systems and Informatics (ICSAI 2014),pp. 113–118, 2014.##[19] K. Dimitropolos, P. Barmpoutis, N. Grammalidis, “Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection,” IEEE transactions on circuits and systems for video technology, vol. 25, no. 2, pp.339-351, 2015.##[20] X-L. Zhou, et al.,”Early fire detection based on flame contours in video,” Information Tech-nology Journal, vol. 9, no. 5, pp. 899-908, 2010.##[21] M. Akarami, “Study and improvement of fire detection algorithm in video images”, M.S. Thesis, Faculty of Electrical and Computer Engineering, Yazd University, 2012.##[22] B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, ”Flame detection in video using hidden Markov models, ” in IEEE International Conference on Image Processing, vol. 2, pp. 1230–1233, 2005.##[23] J. Rong, et al.,”Fire flame detection based on GICA and target tracking,” Optics &. Laser Technology, vol. 47, pp. 283–291, 2013.##[24] H. SunJae. ,K. Byoungchul, and N. Jae, “Fire-flame detection based on fuzzy finite automation,” 20th International Conference on Pattern Recognition(ICPR), pp.3919-3922, 2010.##[25] J. Z, et al., ”SVM based forest fire detection using static and dynamic features, ” Computer Science Information Systems Journal, vol. 8, no. 3, pp. 821–841, 2011.##[26] J. Antony, and J. C. Prasad, “Real Time Fire and smoke detection using multi-expert system for video-surveillance,” international journal for innovative research in science & technology, vol. 3, 2016.##[27] K. Muhammad, J. Ahmad, and S. Wookbaik, “Early fire detection using convolutional neural networks duringnsurveillance for effective disaster management,” Neurocomputing, vol. 288, pp. 30-42, 2018.##[28] H. Xian-Feng, et al., “Video fire detection based on gaussian mixture model and multi-color features,” Signal, Image and Video Processing, vol. 11, no. 8, pp. 1419-1425, 2017.##[29] M. Mahdavi, H. Ahaki, and B, NaserSharif, “Design of a currency recognition system based on neural network using image texture and color ”, Signal and Date Processing Journal, Issue. 7, No. 2, 2011.##[30] A. Feizy, “Application of Sparse representation and camera collaboration in visual surveillance systems,” Signal and Date Processing Journal, Issue. 15, No. 3, 2018.##[1] Kh. Rezaee, M. Ghezelbash, and J. hadadnia, ”Accurate and intelligent fire monitoring system in various environments using image processing,” 20th Iranian Conference on Electric, 2012.##[1] رضایی، خسرو، قزلباش، محمد راسق، حدادنیا، جواد، سیستم دقیق و هوشمند پایش آتش در محیط‌های گوناگون با استفاده از پردازش تصویر، بیستمین کنفرانس مهندسی برق ایران، 1391.##[2] J. Chen, Y. He, and J. Wang, ”Multi-feature fusion based fast video flame detection, ”Building Environment Journal, vol. 45, no. 5, pp. 1113–1122, 2010.##[3] W. Wang, and H. Zhou, “Fire detection based on flame color and area,” International Conference on Computer Science and Automation Engineering, vol. 3, pp. 222–226,2012.##[4] Q. Zhou, X. Yang, and L. Bu, “Analysis of shape features of flame and interference image in video fire detection,” Chinese Automation Congress (CAC, pp. 633–637), 2015.##[5] A. E Cetin, et al,”Video fire detection - review,” Digital Signal Processes A Reveiw Journal, vol. 23, no.6, pp. 1827–1843, 2013.##[6] H. Chao, and K. Tzu-Hsin, “Real-time video-based fire smoke detection system,” International Conference on Advanced Inteliigent Mechatronics, pp.1845-1850, 2009.##[7] B. Ko, K. Cheong, and J, Nam,”Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks,” Fire Safety Journal, vol. 45, no.4, pp. 262-270, 2010.##[8] B. C. KO, K. H. Cheong, and J-. Nam,”Fire detection based on vision sensor and support vector machines,”Fire Safety Journal, vol. 44, no. 3, pp. 322–329, 2009.##[9] W. Phillips, and M. Shah, and N. D. V. Lob, “Flame recognition in video,” Pattern Recognition Letters, vol. 23, pp. 319-327, 2002.##[10] J. Seebamrungsat, S. Praising, and P. Riyamongkol,”Fire detection in the buildings using image processing,” Third ICT International Student Project Conference, pp. 95–98, 2014.##[11] G. F. Shidik, et al., “Multi color feature background subtraction and time frame selection for fire detection,” IEEE International Con-ference on Robotics, Biomimetics, and Inte-lligent Computational Systems (ROBIONETICS), pp. 115-120, 2013.##[12] T. Ceilik, H. Ozkaramanli, and H. Demirel, ”Fire pixel classification using fuzzy logic and statistical color model,” in IEEE Inter-national Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 1, pp. 1000–1205, 2007.##[13] B.-H. Cho, J.-W. Bae, and S.-H. Jung, ”Image processing-based fire detection system using statistic color model,” 8th International Conference on Advanced Language Processing and Web Information Technology, pp. 245–250, 2008.##[14] T-H. Chen, P-H. Wu, and Y. Chiou,”An early fire-detection method based on image processing,” in International Conference on Image Processing, (ICIP ’04), vol. 3, pp. 1707–1710, 2004.##[15] S. C. Pol, et al., "Fire detection using image processing and sensors," International Journal of Engineering Trends and Applications, vol. 3, pp. 85–87, 2016.##[16] B. U. Toreyin, Y. Dedeoglu, U. Gudukbay, and A. E. Cetin,”Computer vision based method for real-time fire and flame detection,” Pattern Recognition Letters, vol. 27, pp. 49–58, 2006.##[17] T. X. Truong, and J. Kim, ”Fire flame detection in video sequences using multi-stage pattern re-cognition techniques,” Engineering Applications of Artificial Intelligence, vol. 25, no. 7, pp. 1365-1372, 2012.##[18] Y. Zhao, and G. Tang, ”Fire video recognition based on flame and smoke characteristics, ” in The 2nd International Conference on Systems and Informatics (ICSAI 2014),pp. 113–118, 2014.##[19] K. Dimitropolos, P. Barmpoutis, N. Grammalidis, “Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection,” IEEE transactions on circuits and systems for video technology, vol. 25, no. 2, pp.339-351, 2015.##[20] X-L. Zhou, et al.,”Early fire detection based on flame contours in video,” Information Tech-nology Journal, vol. 9, no. 5, pp. 899-908, 2010.##[21] M. Akarami, “Study and improvement of fire detection algorithm in video images”, M.S. Thesis, Faculty of Electrical and Computer Engineering, Yazd University, 2012.##[21] اکرمی، محمد، مطالعه و بهبود الگوریتم‌های تشخیص آتش در تصاویر ویدئویی، پایان‌نامه کارشناسی ارشد، دانشکده مهندس برق و کامپیوتر، دانشگاه یزد، 1391.##[22] B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, ”Flame detection in video using hidden Markov models, ” in IEEE International Conference on Image Processing, vol. 2, pp. 1230–1233, 2005.##[23] J. Rong, et al.,”Fire flame detection based on GICA and target tracking,” Optics &. Laser Technology, vol. 47, pp. 283–291, 2013.##[24] H. SunJae. ,K. Byoungchul, and N. Jae, “Fire-flame detection based on fuzzy finite automation,” 20th International Conference on Pattern Recognition(ICPR), pp.3919-3922, 2010.##[25] J. Z, et al., ”SVM based forest fire detection using static and dynamic features, ” Computer Science Information Systems Journal, vol. 8, no. 3, pp. 821–841, 2011.##[26] J. Antony, and J. C. Prasad, “Real Time Fire and smoke detection using multi-expert system for video-surveillance,” international journal for innovative research in science & technology, vol. 3, 2016.##[27] K. Muhammad, J. Ahmad, and S. Wookbaik, “Early fire detection using convolutional neural networks duringnsurveillance for effective disaster management,” Neurocomputing, vol. 288, pp. 30-42, 2018.##[28] H. Xian-Feng, et al., “Video fire detection based on gaussian mixture model and multi-color features,” Signal, Image and Video Processing, vol. 11, no. 8, pp. 1419-1425, 2017.##[29] M. 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استخراج ویژگی‌ و بررسی کارآیی روش‌های کاهش بُعد در زمینه تحلیل احساس Feature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context 1 1 امروزه با فراگیر‌شدن دسترسی به اینترنت و به‌خصوص شبکه‌های اجتماعی، امکان به‌اشتراک‎گذاری عقاید و نظرات کاربران فراهم شده است. از سوی دیگر تحلیل احساس و عقاید افراد می‌تواند نقش به‌سزایی در تصمیم‌گیری سازمان‌ها و تولیدکنندگان داشته باشد. از‌این‌‌رو وظیفه تحلیل احساس و یا عقیده‎کاوی به زمینه پژوهشی مهمی در حوزه پردازش زبان طبیعی تبدیل شده است. یکی از چالش‌های استفاده از شیوه‎های یادگیری ماشینی در حوزه پردازش زبان طبیعی، انتخاب و استخراج ویژگی‌های مناسب از میان تعداد زیاد ویژگی‌های اولیه برای دست‌یابی به مدلی با صحت مطلوب است. در این پژوهش دو روش فشرده‌سازی براساس تجزیه‌های ماتریسی SVD و   NMF و یک روش بر اساس شبکه‌های عصبی برای استخراج ویژگی‌های مؤثرتر و با تعداد کمتر در زمینه تحلیل احساس در مجموعه‌داده نظرات به زبان فارسی مورد استفاده و تأثیر سطح فشرده‌سازی و اندازه مجموعه‌داده در صحت مدل‎های ایجاد‌شده مورد ارزیابی قرارگرفته شده است. بررسی‌ها نشان می‌دهد که فشرده‌سازی نه‌‌‌تنها از بار محاسباتی و زمانی ایجاد مدل کم می‌کند، بلکه می‌تواند صحت مدل را نیز افزایش دهد. بر طبق نتایج پیاده‌سازی، فشرده‌سازی ویژگی‌ها از 7700 ویژگی اولیه به دوهزار ویژگی با استفاده از شبکه عصبی، نه‌‌تنها باعث کاهش هزینه محاسسباتی و فضای ذخیره‌سازی می‎شود، بلکه می‌تواند صحت مدل را از % 05/77 به % 85/77 افزایش دهد.  از سوی دیگر در مجموعه داده کوچک با استفاده از روش SVD نتایج بهتری به‌دست می‎‌آید و با تعداد ویژگی دوهزار می‌توان به صحت % 92/63 در مقابل % 57/63 دست پیدا کرد؛ هم‌چنین آزمایش‌ها حاکی از آن است که فشرده‌سازی با استفاده از شبکه عصبی در صورت بزرگی مجموعه‌داده برای ابعاد پایین مجموعه ویژگی،‌ بسیار بهتر از سایر روش‌ها عمل می‌کند. به‌طوری‌که تنها با یکصد ویژگی استخراج‌شده با استفاده از فشرده‌ساز شبکه عصبی از 7700 ویژگی اولیه می‌توان به صحت قابل قبول % 46/74 در مقابل صحت اولیه % 05/77 با 7700 ویژگی دست یافت.   2 Nowadays, users can share their ideas and opinions with widespread access to the Internet and especially social networks. On the other hand, the analysis of people's feelings and ideas can play a significant role in the decision making of organizations and producers. Hence, sentiment analysis or opinion mining is an important field in natural language processing. One of the most common ways to solve such problems is machine learning methods, which creates a model for mapping features to the desired output. One challenge of using machine learning methods in NLP fields is feature selection and extraction among a large number of early features to achieve models with high accuracy. In fact, the high number of features not only cause computational and temporal problems but also have undesirable effects on model accuracy. Studies show that different methods have been used for feature extraction or selection. Some of these methods are based on selecting important features from feature sets such as Principal Component Analysis (PCA) based methods. Some other methods map original features to new ones with less dimensions but with the same semantic relations like neural networks. For example, sparse feature vectors can be converted to dense embedding vectors using neural network-based methods. Some others use feature set clustering methods and extract less dimension features set like NMF based methods. In this paper, we compare the performance of three methods from these different classes in different dataset sizes. In this study, we use two compression methods using Singular Value Decomposition (SVD) that is based on selecting more important attributes and non-Negative Matrix Factorization (NMF) that is based on clustering early features and one Auto-Encoder based method which convert early features to new feature set with the same semantic relations. We compare these methods performance in extracting more effective and fewer features on sentiment analysis task in the Persian dataset. Also, the impact of the compression level and dataset size on the accuracy of the model has been evaluated. Studies show that compression not only reduces computational and time costs but can also increase the accuracy of the model. For experimental analysis, we use the Sentipers dataset that contains more than 19000 samples of user opinions about digital products and sample representation is done with bag-of-words vectors. The size of bag-of-words vectors or feature vectors is very large because it is the same as vocabulary size. We set up our experiment with 4 sub-datasets with different sizes and show the effect of different compression performance on various compression levels (feature count) based on the size of dataset size.  According to experiment results of classification with SVM, feature compression using the neural network from 7700 to 2000 features not only increases the speed of processing and reduces storage costs but also increases the accuracy of the model from 77.05% to 77.85% in the largest dataset contains about 19000 samples. Also in the small dataset, the SVD approach can generate better results and by 2000 features from 7700 original features can obtain 63.92 % accuracy compared to 63.57 % early accuracy. Furthermore, the results indicate that compression based on neural network in large dataset with low dimension feature sets is much better than other approaches, so that with only 100 features extracted by neural network-based auto-encoder, the system achieves acceptable 74.46% accuracy against SVD accuracy 67.15% and NMF accuracy 64.09% and the base model accuracy 77.05% with 7700 features.   88 79 2017/12/152018/04/252017/11/242018/06/222016/11/42017/07/22 1396/4/31 2019/06/192019/07/102019/01/92019/09/42019/06/192019/06/19 1398/3/29 راضیه برادران Razieh Baradaran گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه قم ایران r_baradaran_stu@yahoo.com عفت گلپر رابوکی Effat Golpar-Raboki گروه ریاضی، دانشگاه قم ایران g.raboky@qom.ac.ir Natural Language Processing Sentiment Analysis Opinion Mining Auto-Encoder Singular Value Decomposition Nonnegative Matrix Factorization پردازش زبان طبیعی تحلیل احساس کدگذار خودکار تجزیه مقدار تکین تجزیه نامنفی ماتریس [1] P. Hosseini, A. Ahmadian-Ramaki, H. Maleki, M. Anvari and A. Mirroshandel, "Sentipers: A sentiment analysis corpus for Persian", in 3th National Conference on Linguistics, Tehran: Sharif University of Technology, 2015.##[2] H. Ghassemian, and H. R. Shahdoosti. "Multispectral and Panchromatic image fusion using Spatial PCA", in Signal and Data Processing, vol. 10, pp. 78-69, 2013.##[3] E. Asgarian, M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet", Signal and Data Processing, vol. 15, pp. 71-86, 2018.##[4] M. Najafzadeh, S. Rahati Quchani, R. Ghaemi, "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing, vol. 15, pp. 89-102, 2018.##[5] S. Noferesti, and M. Shamsfard. “Automatic building a corpus and exploiting it for polarity classification of indirect opinions about drugs”, Signal and Data Processing, vol 2, pp. 35-42, 2017.##[6] D. Ankitkumar, R. Badre, and M. Kinikar, “A Survey on Sentiment Analysis and Opinion Mining”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 11, November 2014.##[7] J. Blitzer, “Dimensionality Reduction for Language, A Survey of Dimensionality Reduction Techniques for Natural Language”, 2008, [Online]. Available:http://john.blitzer.com/papers/wpe2.pdf. [Accessed: 10 July 2017].##[8] M. Chu, F. Diele, R. Plemmons, and S. Ragni, “Optimality, Computation and Interpretation of NonNegative Matrix Factorizations”, October 2014. Available: http://users.wfu.edu/ple-mmons/papers/chu_ple.pdf. [Accessed: 10 July 2017].##[9] G. Golub, and C. V. Loan, Matrix computation, 3th ed. Baltimore, Maryland: JHU Press, 1989.##[10] J. Jotheeswaran, B. MadhuSudhanan, and R.Loganathan, “Feature Reduction using Principal Component Analysis for Opinion Mining”, International Journal of Computer Science and Telecommunications, vol. 3, no. 5, pp. 118-121, May 2012.##[11] J. Jotheeswaran, and S.Koteeswaran, “Feature Selection using Random Forest method for Sentiment Analysis”, Indian Journal of Science and Technology, vol. 9, no. 3, pp. 1-7, January 2016.##[12] E. Keogh, and A. Mueen, “Curse of dimensionality”, In: Encyclopedia of Machine Learning, Springer, pp. 257–258, 2010.##[13] J. Kim, and H. Park, “Sparse nonnegative matrix factorization for clustering”, Technical Report CSE Technical Reports, GTCSE-08-01, Georgia Institute of Technology, 2008.##[14] D.P. Kingma, and M. Welling, “Auto-Encoding Variational Bayes”, Cornell University Library, ArXiv: 1312.6114, December 2013.##[15] D. D. Lee, and H. Sebastian Seung, “Algorithms for Non-Negative Matrix Factorization”, Advances in Neural Information Processing Systems, vol. 13, pp. 556-562, 2001.##[16] TS. Lee, BC. Shia, and CL. Huh, “Social Media Sentimental Analysis in Exhibition’s Visitor Engagement Prediction”, American Journal of Industrial and Business Management, vol. 06, pp. 392-400. March 2016.##[17] T. Li, Y. Zhang, and V. Sindhwani, “A non-negative matrix tri-factorization approach to sentiment classification with lexical prior kno-wledge”, in Proceedings of ACL-IJCNLP, 2009, pp. 244–252.##[18] C. Y. Cheng, J. W Liou, D. R Liou, “Autoencoder for Words”, Neurocomputing, vol. 139, pp. 84–96, September 2014.##[19] B. Liu,”Sentiment Analysis and Opinion Mining”, Synthesis lectures on human language technologies, vol. 5. no. 1, pp. 1-167, 2012.##[20] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093-1113, December 2014.##[21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space”, ICLR, 2013.##[22] T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 2010, pp. 1045–1048.##[23] B. Pang, L. Lee, “Opinion mining and sentiment analysis”, Foundations and Trends in Infor-mation Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.##[24] B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86, 2002.##[25] W. Rong, Y. Nie, Y. Ouyang, B. Peng, and Z. Xiong, “Auto-encoder Based Bagging Architecture for Sentiment Analysis”, Journal of Visual Languages and Computing, vol. 25, pp. 840-849, 2014.##[26] G. Vinodhini, and RM. Chandrasekaran, “Opinion mining using principal component analysis based ensemble model for e-commerce application”, CSI Transactions on ICT, vol. 2, pp. 169–179, November 2014.##[27] M. E. Wall, A. Rechtsteiner, and L. M. Rocho, “Singular Value Decomposition and Principal Component Analysis”, chapter 5 in A Practical Approach to Microarray Data Analysis Kluwer Academic Publishers, Boston, MA, 91-109, 2003.##[28] Wikipedia-Autoencoder, [Online]. Available: https://en.wikipedia.org/wiki/Autoencoder. [Accessed: 10 July 2017].##[29] Y. Yoshida, T. Hirao, T. Iwata, M. Nagata, and Y. Matsumoto, “Transfer learning for multiple-domain sentiment analysis identifying domain dependent/independent word polarity.” in Proceedings of the Twenty-Fifth AAAI Con-ference on Artificial Intelligence, 2011.##[30] N. Zainuddin,A. Selamat and R. Ibrahim, "Hybrid Sentiment Classification on Twitter Aspect-Based Sentiment Analysis," Applied Intelligence, vol. 48, no. 5, pp. 1218-1232, May 2018.##[1] حسینی، پدرام، احمدیان رمکی، علی، ملکی، حسن، انواری، منصوره، میرروشندل، ابولقاسم، "پیکره فارسی تحلیل احساس سِنتی پِرس"، سومین همایش ملی زبان‌شناسی رایانشی، تهران، دانشگاه صنعتی شریف، 1393.##[1] P. Hosseini, A. Ahmadian-Ramaki, H. Maleki, M. Anvari and A. Mirroshandel, "Sentipers: A sentiment analysis corpus for Persian", in 3th National Conference on Linguistics, Tehran: Sharif University of Technology, 2015.##[2] شاهدوستی، حمید رضا، قاسمیان، حسن، "استفاده از تبدیلPCA مکانی جهت ادغام تصاویر چند طیفی و تک رنگ"، پردازش علائم و داده‌ها، دوره 10، شماره 1، صفحات 69-78، ۱۳۹۲.##[2] H. Ghassemian, and H. R. Shahdoosti. "Multispectral and Panchromatic image fusion using Spatial PCA", in Signal and Data Processing, vol. 10, pp. 78-69, 2013.##[3] عسکریان، احسان، کاهانی، محسن، شریفی، شهلا، "حس‌نگار: شبکه واژگان حسی فارسی"، پردازش علائم و داده‌ها. دوره ۱۵، شماره ۱، صفحات ۷۱-۸۶، ۱۳۹۷.##[3] E. Asgarian, M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet", Signal and Data Processing, vol. 15, pp. 71-86, 2018.##[4] نجف‌زاده، محسن، راحتی قوچانی، سعید، قائمی، رضا، "یک چارچوب نیمه‌نظارتی مبتنی بر لغت‌نامه وفقی خودساخت جهت تحلیل نظرات فارسی"، پردازش علائم و داده‌ها، دوره 15، شماره 2، صفحات 89-102، ۱۳۹۷.##[4] M. Najafzadeh, S. Rahati Quchani, R. Ghaemi, "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing, vol. 15, pp. 89-102, 2018.##[5] نوفرستی، سمیرا، شمس فرد، مهرنوش، "ساخت نیمه‎خودکار یک پیکره از نظرات غیر مستقیم در دامنه دارو و به کارگیری آن در تعیین قطبیت نظرات"، مجله پردازش علائم و داده‌ها، شماره 2، صفحات 35-42، 1395.##[5] S. Noferesti, and M. Shamsfard. “Automatic building a corpus and exploiting it for polarity classification of indirect opinions about drugs”, Signal and Data Processing, vol 2, pp. 35-42, 2017.##[6] D. Ankitkumar, R. Badre, and M. Kinikar, “A Survey on Sentiment Analysis and Opinion Mining”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 11, November 2014.##[7] J. Blitzer, “Dimensionality Reduction for Language, A Survey of Dimensionality Reduction Techniques for Natural Language”, 2008, [Online]. Available:http://john.blitzer.com/papers/wpe2.pdf. [Accessed: 10 July 2017].##[8] M. Chu, F. Diele, R. Plemmons, and S. Ragni, “Optimality, Computation and Interpretation of NonNegative Matrix Factorizations”, October 2014. Available: http://users.wfu.edu/ple-mmons/papers/chu_ple.pdf. [Accessed: 10 July 2017].##[9] G. Golub, and C. V. Loan, Matrix computation, 3th ed. Baltimore, Maryland: JHU Press, 1989.##[10] J. Jotheeswaran, B. MadhuSudhanan, and R.Loganathan, “Feature Reduction using Principal Component Analysis for Opinion Mining”, International Journal of Computer Science and Telecommunications, vol. 3, no. 5, pp. 118-121, May 2012.##[11] J. Jotheeswaran, and S.Koteeswaran, “Feature Selection using Random Forest method for Sentiment Analysis”, Indian Journal of Science and Technology, vol. 9, no. 3, pp. 1-7, January 2016.##[12] E. Keogh, and A. Mueen, “Curse of dimensionality”, In: Encyclopedia of Machine Learning, Springer, pp. 257–258, 2010.##[13] J. Kim, and H. Park, “Sparse nonnegative matrix factorization for clustering”, Technical Report CSE Technical Reports, GTCSE-08-01, Georgia Institute of Technology, 2008.##[14] D.P. Kingma, and M. Welling, “Auto-Encoding Variational Bayes”, Cornell University Library, ArXiv: 1312.6114, December 2013.##[15] D. D. Lee, and H. Sebastian Seung, “Algorithms for Non-Negative Matrix Factorization”, Advances in Neural Information Processing Systems, vol. 13, pp. 556-562, 2001.##[16] TS. Lee, BC. Shia, and CL. Huh, “Social Media Sentimental Analysis in Exhibition’s Visitor Engagement Prediction”, American Journal of Industrial and Business Management, vol. 06, pp. 392-400. March 2016.##[17] T. Li, Y. Zhang, and V. Sindhwani, “A non-negative matrix tri-factorization approach to sentiment classification with lexical prior kno-wledge”, in Proceedings of ACL-IJCNLP, 2009, pp. 244–252.##[18] C. Y. Cheng, J. W Liou, D. R Liou, “Autoencoder for Words”, Neurocomputing, vol. 139, pp. 84–96, September 2014.##[19] B. Liu,”Sentiment Analysis and Opinion Mining”, Synthesis lectures on human language technologies, vol. 5. no. 1, pp. 1-167, 2012.##[20] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093-1113, December 2014.##[21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space”, ICLR, 2013.##[22] T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 2010, pp. 1045–1048.##[23] B. Pang, L. Lee, “Opinion mining and sentiment analysis”, Foundations and Trends in Infor-mation Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.##[24] B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86, 2002.##[25] W. Rong, Y. Nie, Y. Ouyang, B. Peng, and Z. Xiong, “Auto-encoder Based Bagging Architecture for Sentiment Analysis”, Journal of Visual Languages and Computing, vol. 25, pp. 840-849, 2014.##[26] G. Vinodhini, and RM. Chandrasekaran, “Opinion mining using principal component analysis based ensemble model for e-commerce application”, CSI Transactions on ICT, vol. 2, pp. 169–179, November 2014.##[27] M. E. Wall, A. Rechtsteiner, and L. M. Rocho, “Singular Value Decomposition and Principal Component Analysis”, chapter 5 in A Practical Approach to Microarray Data Analysis Kluwer Academic Publishers, Boston, MA, 91-109, 2003.##[28] Wikipedia-Autoencoder, [Online]. Available: https://en.wikipedia.org/wiki/Autoencoder. [Accessed: 10 July 2017].##[29] Y. Yoshida, T. Hirao, T. Iwata, M. Nagata, and Y. Matsumoto, “Transfer learning for multiple-domain sentiment analysis identifying domain dependent/independent word polarity.” in Proceedings of the Twenty-Fifth AAAI Con-ference on Artificial Intelligence, 2011.##[30] N. Zainuddin,A. Selamat and R. Ibrahim, "Hybrid Sentiment Classification on Twitter Aspect-Based Sentiment Analysis," Applied Intelligence, vol. 48, no. 5, pp. 1218-1232, May 2018.## ##

پیاده‌سازی ممیز ثابت فیلتر کالمن بر روی FPGA برای تخمین فاصله و سرعت اهداف متحرک Fixed-point FPGA Implementation of a Kalman Filter for Range and Velocity Estimation of Moving Targets 1 1 در سامانه‌های ردیابی هدف، از فیلتر ردیابی برای تخمین پیاپی و هموار موقعیت و سرعت هدف متحرک با کمینه خطا استفاده می‌­شود. در این مقاله، روشی برای طراحی و پیاده‌­سازی سخت‌افزاری فیلتر کالمن در چنین کاربردی ارائه ‌شده است. روش پیشنهادی شامل یک پیاده‌سازی ممیز ثابت فیلتر روی FPGA است که در آن سرعت اجرای الگوریتم از طریق موازی­‌سازی عملیات­ غیر وابسته بهبود یافته است. پس از طراحی بر اساس مسأله داده‌­شده، نسخه­‌های ممیز شناور و ممیز ثابت فیلتر شبیه­‌سازی و نسخه ممیز ثابت روی سخت­افزار پیاده‌­سازی شده است. برای ارزیابی کارایی فیلتر، داده­‌های فاصله‌-سرعت یک هدف متحرک با مدل مناسب تولید و پس از چندی‌سازی و درآمیختن با اغتشاش به فیلتر اعمال می‌­شوند. نتایج نشان می­‌دهد که با انتخاب طول بیت مناسب، فیلتر پیاده‌سازی‌‌شده سریع و کارآمد بوده و با زمان اجرای حدود µs 4/0، موجب dB 11 کاهش در خطای تخمین فاصله شده و عملکردی نزدیک به نمونه ممیز شناور فراهم می‌­آورد.    2 Tracking filters are extensively used within object tracking systems in order to provide consecutive smooth estimations of position and velocity of the object with minimum error. Namely, Kalman filter and its numerous variants are widely known as simple yet effective linear tracking filters in many diverse applications. In this paper, an effective method is proposed for designing and implementation of a Kalman filter in an object tracking application. The considered tracking application implies the capability to produce a smooth and reliable output stream by the tracking filter, even in presence of different disturbing types of noise, including background or spontaneous noises, as well as disturbances with continues or discrete nature. The presented method includes a fixed-point implementation of the Kalman filter on FPGA, which targets the joint estimation of position-velocity pair of an intended object in heavy presence of noise. The execution speed of the Kalman algorithm is drastically enhanced in the proposed implementation. This enhancement is attained by emphasis on hardware implementation of every single computational block on the one hand, and    through appropriate parallelization and pipelining of independent tasks within the Kalman process on the other hand. After designing the filter parameters with respect to the requirements of a given tracking problem, a floating-point model and a fixed-point hardware model of the filter are implemented using MATLAB and Xilinx System Generator, respectively.   In order to evaluate the performance of the filter under realistic circumstances, a set of appropriately defined scenarios are carried out. The simulations are carefully designed in order to represent the extremely harsh scenarios in which the input measurements to the filter are deeply polluted by different kinds of noises. In each simulation the position-velocity data corresponding to a moving object is generated according to an appropriate model, quantized, and contaminated by noise and fed into the filter. Performances of the Kalman filter in software version (i.e. the floating point replica) and hardware version (i.e. the fixed-point replica) are quantitatively compared in the designed scenario. Our comparison employs NMSE and maximum error values as quantitative measures, verifying the competency of our proposed fixed-point hardware implementation. The results of our work show that, with adequate selection word length, the implemented filter is fast and efficient; it confines the algorithm execution time to 50 clock pulses, i.e. about 0.4 µs when a 125 MHz clock is used. It is also verified that our implementation reduces the position and velocity estimation errors by 11 dB and 1.2 dB, respectively. The implemented filter also confines the absolute values of maximum error in position and velocity to 10 meter and 0.7 meter/sec. in the considered scenario, which is almost resembles the performance of its floating point counterpart. The presented Kalman filter is finally implemented on Zc706 evaluation board and the amount of utilized hardware resource (FFs, LUTs, DSP48, etc.) are reported as well as the estimated power consumption of the implemented design. The paper is concluded through comparison of the proposed design with some recent works which confirms the efficacy of the presented implementation. 100 89 2017/12/152018/04/252017/11/242018/06/222016/11/42017/07/222017/10/4 1396/7/12 2019/06/192019/07/102019/01/92019/09/42019/06/192019/06/192019/08/31 1398/6/9 شهاب الدین رحمانیان Shahabuddin Rahmanian پژوهشکده اویونیک، دانشگاه صنعتی اصفهان ایران rahmanian@cc.iut.ac.ir محمد حسین باطنی Mohammad Hossein Bateni پژوهشکده اویونیک، دانشگاه صنعتی اصفهان ایران mh.bateni@ec.iut.ac.ir محمد فرداد Mohammad Fardad پژوهشکده اویونیک، دانشگاه صنعتی اصفهان ایران m.fardad@ec.iut.ac.ir مجد‌الدین نجفی Majdeddin Najafi پژوهشکده اویونیک، دانشگاه صنعتی اصفهان ایران majd_najafi@cc.iut.ac.ir Kalman filter FPGA implementation Tracking Distance estimation Velocity estimation فیلتر کالمن پیاده‌سازی FPGA ردیابی تخمین فاصله تخمین سرعت [1] R. Kalman, “A new approach to linear filtering and prediction problems,” Trans. ASME, J. Basic Eng., vol. 82, pp. 35–45, 1960, series D.##[2] Z. Hanifelou, S. A. H. Monadjemi and P. Moallem, “Robust method of changes of light to detect and track vehicles in traffic scenes” Journal of Signal and Data Processing (JSDP), vol. 13, no. 3, pp. 79-98, 2016.##[3] F. Shayegh, F. Ghasemi, R. Amirfattahi, S. Sadri ,and K. Ansariasl, “Online Single-Channel Seizure Prediction, Based on Seizure Genesis Model of Depth-EEG Signals Using Extended Kalman Filter,” Journal of Signal and Data Processing (JSDP), vol. 15, no. 1, pp. 3-28, 2018.##[4] M. Grewal and A. Andrews, Kalman Theory, Theory and Practice Using MATLAB. 3rd ed. Hoboken, NJ: Wiley, 2008.##[5] M. Verhaegen and P. V. Dooren, “Numerical aspects of different Kalman filter implementations,” IEEE Trans. Autom. Control, vol. AC 31, no. 10, pp. 907–917, Oct. 1986.##[6] V. Smidl and Z. Peroutka, “Advantages of square-root extended Kalman filter for sensorless control of AC drives,” IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4189–4196, Nov. 2012.##[7] J. Mendel, “Computational requirements for a discrete Kalman filter,” IEEE Trans. Autom. Control, vol. AC-16, no. 6, pp. 748–758, Dec. 1971.##[8] M. Hilairet, F. Auger, and C. Darengosse, “Two efficient Kalman filters for flux and velocity estimation of induction motors,” in Proc. IEEE Power Electron. Spec. Conf., Jun. 2000, vol. 2, pp. 891–896.##[9] L. Idkhajine and E. Monmasson, “Design methodology for complex FPGA-based con-trollers—Application to an EKF sensorless ac drive,” in Proc. XIX ICEM, Sep. 2010, pp. 1–6.##[10] J. Keller and M. Darouach, “Two-stage Kalman estimator with unknown exogenous inputs,” Automatica, vol. 35, no. 2, pp. 339–342, Feb. 1999.##[11] C. Hsieh and F. Chen, “Optimal solution of the two-stage Kalman estimator,” IEEE Trans. Autom. Control, vol. 44, no. 1, pp. 194–199, Jan. 1999.##[12] C. Hsieh, “General two-stage extended Kalman filters,” IEEE Trans. Autom. Control, vol. 48, no. 2, pp. 289–293, Feb. 2003.##[13] M. Hilairet, F. Auger, and E. Berthelot, “Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter,” Auto-matica, vol. 45, no. 8, pp. 1819–1827, Aug. 2009.##[14] A. Akrad, M. Hilairet, and D. Diallo, “Design of a fault-tolerant controller based on observers for a PMSM drive,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1416–1427, Apr. 2011.##[15] S. Bolognani, R. Oboe, and M. Zigliotto, “Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position,” IEEE Trans. Ind. Electron., vol. 46, no. 1, pp. 184–191, Feb. 1999.##[16] H.-G. Yeh, “Systolic implementation on Kalman filters,” IEEE Trans. Acoust., Speech, Signal Process., vol. 36, no. 9, pp. 1514–1517, Sep. 1988.##[17] S.-Y. Kung and J.-N. Hwang, “Systolic array designs for Kalman filtering,” IEEE Trans. Signal Process., vol. 39, no. 1, pp. 171–182, Jan. 1991.##[18] E. Monmasson, L. Idkhajine, M. Cirstea, I. Bahri, A. Tisan, and M. Naouar, “FPGAs in industrial control applications,” IEEE Trans. Ind. Informat., vol. 7, no. 2, pp. 224–243, May 2011.##[19] C. Lee and Z. Salcic, “High-performance FPGA-based implementation of Kalman filter,” Micro-process. Microsyst., vol. 21, no. 4, pp. 257 265, Dec. 1997.##[20] L. Idkhajine, E. Monmasson, and A. Maalouf, “Fully FPGA-based sensorless control for syn-chronous AC drive using an extended Kalman filter,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3908–3918, Oct. 2012.##[21] A. Jarrah, A. Al-Tamimi, and T. Albashir, “Opti-mized parallel implementation of Extended Kal-man filter using FPGA,” Journal of Circuits, Sys-tems and Computers, vol. 01, no. 27 no. 01, June 2017.##[22] N. Noordin, Z. Ibrahim, M. H. J. Xie, R. Samad and N. Hasan, “FPGA implementation of simulated Kalman filter optimization algo-rithm,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), VOL. 10, no. 1-3, pp. 21-24, 2018.##[23] D. Pritsker, “Hybrid implementation of Extended Kalman Filter on an FPGA,” in Proc. IEEE Radar Conf. (RadarCon), pp. 0077-0082, 2015.##[24] P. L. Wu, L. Z. Zhang and X. Y. Zhang, “The design of DSP/FPGA based maneuvering target tracking system,” WSEAS Trans. Circ. Syst., vol. 13, pp. 75-84, 2014.##[1] R. Kalman, “A new approach to linear filtering and prediction problems,” Trans. ASME, J. Basic Eng., vol. 82, pp. 35–45, 1960, series D.##[2] حنیفه‌لو زهرا، منجمی سید امیرحسن، معلم پیمان، "ارائه روشی مقاوم نسبت به تغییرات روشنایی در آشکارسازی و ردیابی خودروها درصحنه‌های ترافیکی،" مجله پردازش علائم و داده‌ها. ۱۳۹۵; ۱۳ (۳) :۷۹-۹۸.##[2] Z. Hanifelou, S. A. H. Monadjemi and P. Moallem, “Robust method of changes of light to detect and track vehicles in traffic scenes” Journal of Signal and Data Processing (JSDP), vol. 13, no. 3, pp. 79-98, 2016.##[3] شایق فرزانه، قاسمی فهیمه، امیر فتاحی رسول، صدری سعید، انصاری اصل کریم، "پیش‌گویی برخط و تک‌کاناله وقوع حمله‌های صرعی با ارائه الگوی تولید صرع بر روی سیگنال‌های depth-EEG با استفاده از فیلتر کالمن توسعه‌یافته،" مجله پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۱) :۳-۲۸.##[3] F. Shayegh, F. Ghasemi, R. Amirfattahi, S. Sadri ,and K. Ansariasl, “Online Single-Channel Seizure Prediction, Based on Seizure Genesis Model of Depth-EEG Signals Using Extended Kalman Filter,” Journal of Signal and Data Processing (JSDP), vol. 15, no. 1, pp. 3-28, 2018.##[4] M. Grewal and A. Andrews, Kalman Theory, Theory and Practice Using MATLAB. 3rd ed. Hoboken, NJ: Wiley, 2008.##[5] M. Verhaegen and P. V. Dooren, “Numerical aspects of different Kalman filter implementations,” IEEE Trans. Autom. Control, vol. AC 31, no. 10, pp. 907–917, Oct. 1986.##[6] V. Smidl and Z. Peroutka, “Advantages of square-root extended Kalman filter for sensorless control of AC drives,” IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4189–4196, Nov. 2012.##[7] J. Mendel, “Computational requirements for a discrete Kalman filter,” IEEE Trans. Autom. Control, vol. AC-16, no. 6, pp. 748–758, Dec. 1971.##[8] M. Hilairet, F. Auger, and C. Darengosse, “Two efficient Kalman filters for flux and velocity estimation of induction motors,” in Proc. IEEE Power Electron. Spec. Conf., Jun. 2000, vol. 2, pp. 891–896.##[9] L. Idkhajine and E. Monmasson, “Design methodology for complex FPGA-based con-trollers—Application to an EKF sensorless ac drive,” in Proc. XIX ICEM, Sep. 2010, pp. 1–6.##[10] J. Keller and M. Darouach, “Two-stage Kalman estimator with unknown exogenous inputs,” Automatica, vol. 35, no. 2, pp. 339–342, Feb. 1999.##[11] C. Hsieh and F. Chen, “Optimal solution of the two-stage Kalman estimator,” IEEE Trans. Autom. Control, vol. 44, no. 1, pp. 194–199, Jan. 1999.##[12] C. Hsieh, “General two-stage extended Kalman filters,” IEEE Trans. Autom. Control, vol. 48, no. 2, pp. 289–293, Feb. 2003.##[13] M. Hilairet, F. Auger, and E. Berthelot, “Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter,” Auto-matica, vol. 45, no. 8, pp. 1819–1827, Aug. 2009.##[14] A. Akrad, M. Hilairet, and D. Diallo, “Design of a fault-tolerant controller based on observers for a PMSM drive,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1416–1427, Apr. 2011.##[15] S. Bolognani, R. Oboe, and M. Zigliotto, “Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position,” IEEE Trans. Ind. Electron., vol. 46, no. 1, pp. 184–191, Feb. 1999.##[16] H.-G. Yeh, “Systolic implementation on Kalman filters,” IEEE Trans. Acoust., Speech, Signal Process., vol. 36, no. 9, pp. 1514–1517, Sep. 1988.##[17] S.-Y. Kung and J.-N. Hwang, “Systolic array designs for Kalman filtering,” IEEE Trans. Signal Process., vol. 39, no. 1, pp. 171–182, Jan. 1991.##[18] E. Monmasson, L. Idkhajine, M. Cirstea, I. Bahri, A. Tisan, and M. Naouar, “FPGAs in industrial control applications,” IEEE Trans. Ind. Informat., vol. 7, no. 2, pp. 224–243, May 2011.##[19] C. Lee and Z. Salcic, “High-performance FPGA-based implementation of Kalman filter,” Micro-process. Microsyst., vol. 21, no. 4, pp. 257 265, Dec. 1997.##[20] L. Idkhajine, E. Monmasson, and A. Maalouf, “Fully FPGA-based sensorless control for syn-chronous AC drive using an extended Kalman filter,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3908–3918, Oct. 2012.##[21] A. Jarrah, A. Al-Tamimi, and T. Albashir, “Opti-mized parallel implementation of Extended Kal-man filter using FPGA,” Journal of Circuits, Sys-tems and Computers, vol. 01, no. 27 no. 01, June 2017.##[22] N. Noordin, Z. Ibrahim, M. H. J. Xie, R. Samad and N. Hasan, “FPGA implementation of simulated Kalman filter optimization algo-rithm,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), VOL. 10, no. 1-3, pp. 21-24, 2018.##[23] D. Pritsker, “Hybrid implementation of Extended Kalman Filter on an FPGA,” in Proc. IEEE Radar Conf. (RadarCon), pp. 0077-0082, 2015.##[24] P. L. Wu, L. Z. Zhang and X. Y. Zhang, “The design of DSP/FPGA based maneuvering target tracking system,” WSEAS Trans. Circ. Syst., vol. 13, pp. 75-84, 2014.## ##

کاهش فضای جستجو در بازشناسی زیرواژگان تایپی فارسی با استفاده از موقعیت نقاط و علائم Search Space Reduction for Farsi Printed Subwords Recognition by Position of the Points and Signs 1 1 در این مقاله با استفاده از روشی ساده، اما کارا سعی شده دامنه جستجوی زیرواژگان به‌شدّت کاهش یابد. در گام آموزش، داده‌های آموزشی بر‌اساس موقعیت علائم گروه‌بندی می‌شوند، در گروه‌هایی که تعداد عناصر بیش از ده زیرواژه است، برای کاهش فضای جستجو با توجه به تعداد عناصر گروه، با استخراج ویژگی‌های ساده‌ای از پروفایل‌های افقی و عمودی خوشه‌بندی صورت می‌گیرد. در مرحله بازشناسی در نخستین مرحله با تعیین نسبت پهنا به ارتفاع زیرواژه (با علائم و بی‌علائم) و کد موقعیت نقاط و علائم، دامنه جستجو به زیرواژگانی با این کد موقعیت که در محدوده‌ای از نسبت‌های یاد‌شده باشند، محدود می‌‌شود؛ در‌صورتی‌که تعداد زیرواژگان محدود‌شده در این مرحله کمتر از ده باشد، این محدوده پذیرفته و در غیر این‌صورت در مرحله بعد با استخراج ویژگی‌های ساده‌ای از پروفایل‌های افقی و عمودی فضای جستجو به تعدادی از نزدیکترین خوشه‌ها به این زیرواژه که شرط نسبت پهنا به ارتفاع را نیز ارضا کنند محدود می‌‌شود. با اعمال روش پیشنهادی این مقاله فضای جستجو تا حد قابل قبولی کاهش یافته است. 2 In the field of the words recognition, three approaches of words isolation, the overall shape and combination of them are used. Most optical recognition methods recognize the word based on break the word into its letters and then recogniz them. This approach is faced some problems because of the letters isolation dificulties and its recognition accurcy in texts with a low image quality. Therefore, an approach based on none separating recognition could be useful in such cases. In methods based on the overall shapes for subword recognition after extraction of subword features usually these features are searched in the image dictionary created in the training phase. Therefore, by considering that we are faced with massive amounts of classes, proposing ways to limit the scope of the search are the main challenges in the overall shape methods. Thus, the information of the overall shape usually is used to reduce the scope search in a hierarchical form. In this paper, it is tried to reduce the search space of the subwords severely by using a simple and efficient method.  In training phase, training data is grouped based on the location of the points and signs, in the groups where have more than 10 subwords, to reduce the search space, according to the number of elements in the group, by extracting the simple features of horizontal and vertical profiles clustering takes place. In recognition phase, in the first step, by determining the width to height ratio of the subword (with signs and without signs) and the position code of the points and signs, the search scope is limited to subwords with this position code that are within the range of the ratios mentioned. This range would be accepted if the number of subwords in this phase is less than ten. Otherwise, in the next step, by extracting the simple features of the horizontal and vertical profiles of the subwords, the search space will be limited to a number of the closest clusters to this subword that also satisfies the width-to-height ratio. By using the proposed method of this paper, the search space has fallen to an acceptable level. In this study, a database of 12700 subwords with five Lotus, Zar, Nazanin, Mitra and Yaghut fonts scanned 400 dpi was used. The four Lotus, Zar, Nazanin and Mitra fonts were used in the training phase and in the test phase, Yaghut ​​font is used. 116 101 2017/12/152018/04/252017/11/242018/06/222016/11/42017/07/222017/10/42017/10/24 1396/8/2 2019/06/192019/07/102019/01/92019/09/42019/06/192019/06/192019/08/312019/06/19 1398/3/29 اسماعیل میری Esmail Miri گروه الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند ایران miri.esmail@birjand.ac.ir سیدمحمد رضوی Seyyed Mohammad Razavi گروه الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند ایران smrazavi@birjand.ac.ir ناصر مهرشاد Nasser Mehrshad گروه الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند ایران nmehrshad@birjand.ac.ir Recognition Farsi Typed Subwords Search Space Reduction Position of the Points and Symbols بازشناسی زیرواژگان تایپی فارسی کاهش فضای جستجو موقعیت نقاط و علائم [1] T. Adamek, N. E. Connor, and A. F. Smeaton, "Word matching using single closed contours for indexing Handwritten Historical Documents," International Jurnal of Document Analysis and Recognition, vol. 9, no. 2-4, pp. 153-165, 2007.##[2] J. R. Pinales, R. J. Rivas, and M. J. C. Bleda, "Holistic Cursive word recognition based on perceptual features," Pattern Recognition Letters, vol. 28, no. 13, pp. 1600-1609, 1 Oct. 2007.##[3] A. Amin, "Recognition of printed arabic text based on global features and decision tree learning techniques," Pattern Recognition, vol. 33, no. 8, pp. 1309-1323, 2000.##[4] A. Ebrahimi, ''Using the holistic form of print subwords in retrieving documentary images and recognizing Persian texts'', Ph.D. dissertation, Electron. Eng., Tarbiat Modares Univ., Tehran, 1384.##[5] H. Khosravi, E. Kabir, '' Evaluation of methods for recognizing Persian texts based on the holistic form of subwords,'' Iranian Journal of Electrical and Computer Engineering, vol.7, no.4, pp.267-280, 2005.##[6] S. Madhvanath, G. Kim, and V. Govindaraju, "Chain code contour processing for handwritten word recognition," IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 21, no. 9, pp. 928-932, Sep. 1999.##[7] K. Zagoris, K. Ergina, and N. Papamarkos, "A document image retrieval system," Engineering Application of Artificial Intelligence, vol. 23, no. 6, pp. 872-879, 2010.##[8] S. Bai, L. Li, and C. L. Tan, "Keyword spotting in document images through word shape coding," in Proc. 10th International Conference on Document Analysis and Recognition, ICDAR'09, pp. 331-335, 26-29 Jul. 2009.##[9] L. Li, S. Lu, and C. L. Tan, "A fast keyword-spotting technique," in Proc. 9th Int. Conference on Document Analysis and Recognition, ICDAR'07, pp.68-72, 23-26 Sep. 2007.##[10] S. Lu and C. L. Tan, "Document image retrieval through word shape coding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1913-1918, Nov. 2008.##[11] J. A. Rodriguez-Serrano and F. Perronnin, "Handwritten word spotting using hidden markov models and vocabularies," Pattern Recognition, vol. 42, no. 9, pp. 2106-2116, Sep. 2009.##[12] T. M. Rath and R. Manmatha, "Word spotting for historical documents," International Jurnal on Document Analysis and Recognition, Vol. 9, no. 2-4, pp. 139-152, Apr. 2007.##[13] Y. Lu and C. L. Tan, "Information retrieval in document image databases," IEEE Transactions on nowledge and Data Engineering, Vol. 16, no. 11, pp. 1398-1410, Nov. 2004.##[14] A. Ebrahimi and E. Kabir, "A pictorial dictionary for printed farsi sub words," Pattern Recognition Letters, Vol. 29, no. 5, pp. 656-663, 2008.##[15] A. Rehman and T. Saba, "Off - line cursive script recognition: current advances, comparisons and remaining problems," Artificial Intelligence Review, vol. 37, no. 4, pp. 261-288, 2012.##[16] S. G. Madhvanath and V. Govindaraju, "The role of holistic paradigms in handwritten word recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 149-164, Feb. 2001.##[17] L. M. Lorigo and V. Govindaraju, "Off - line arabic handwriting recognition: a survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 712-724, May 2008.##[18] S. Mozaffari, K. Faez, V. Märgner and H. Elabed, ''Two-stage lexicon reduction for offline Arabic handwritten word recognition,'' International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, No. 07: pp. 1323-1341, November 2008.##[19] H. Davoudi, M. Cheriet and E. Kabir, ''Lexicon reduction of handwritten arabic subwords based on the prominent shape regions,'' International Journal on Document Analysis and Recognition, vol 19, Issue 2, pp 139–153, 2016.##[20] S. Bromand, M. Iranpurmobaraka," Handwritten words recognion with new features and reducing the dictionary," Machine Vision And Image Processing, unpublished.##[21] H. Davoudi, E. Kabir, ''Using compatible shape descriptor for lexicon reduction of printed farsi subwords," International Journal on Document Analysis and Recognition, vol. 19, Issue 2. pp 139-153, 2016.##[22] H. Davoudi, E. Kabir, ''Using compatible shape descriptor for lexicon reduction of printed farsi subwords," Iranian Journal of Electrical and Computer Engineering, vol. 12, Issue1., 2014.##[23] F. Fathi, " Extraction of index letters from Persian printed subwords", M.S. thesis, Dept. Electron.Eng., Sahand University of Technology, Tabriz, Iran, 2009.##[24] M. Alibaigi, "Persian printed subwords recognition", M.Sc. thesis, Departmet of Electronic Engineering, University of Birjand, Birjand, Iran, 2010.##[25] E. Miri, S.M. Razavi, N. Mehrshad, " A simple method for search space reduction in Persian typed subwords recognition," 9th Conference on Machine Vision and Image Processing conference, Shahid Behshti University, Tehran, 2015.##[1] T. Adamek, N. E. Connor, and A. F. Smeaton, "Word matching using single closed contours for indexing Handwritten Historical Documents," International Jurnal of Document Analysis and Recognition, vol. 9, no. 2-4, pp. 153-165, 2007.##[2] J. R. Pinales, R. J. Rivas, and M. J. C. Bleda, "Holistic Cursive word recognition based on perceptual features," Pattern Recognition Letters, vol. 28, no. 13, pp. 1600-1609, 1 Oct. 2007.##[3] A. Amin, "Recognition of printed arabic text based on global features and decision tree learning techniques," Pattern Recognition, vol. 33, no. 8, pp. 1309-1323, 2000.##[4] ابراهیمی، افشین، "استفاده از شکل کلی زیرکلمات چاپی در بازیابی تصویر مستندات و بازشناسی متون فارسی"، رساله دکتری مهندسی برق- الکترونیک، دانشگاه تربیت مدرس، تهران، 1384.##[4] A. Ebrahimi, ''Using the holistic form of print subwords in retrieving documentary images and recognizing Persian texts'', Ph.D. dissertation, Electron. Eng., Tarbiat Modares Univ., Tehran, 1384.##[5] خسروی، حسین و کبیر، احسان الله، "ارزیابی روش‌های بازشناسی متون فارسی بر مبنای شکل کلی زیرکلمات"، نشریه مهندسی برق و کامپیوتر ایران، جلد 7، شماره4، صص. 280-267، 1388.##[5] H. Khosravi, E. Kabir, '' Evaluation of methods for recognizing Persian texts based on the holistic form of subwords,'' Iranian Journal of Electrical and Computer Engineering, vol.7, no.4, pp.267-280, 2005.##[6] S. Madhvanath, G. Kim, and V. Govindaraju, "Chain code contour processing for handwritten word recognition," IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 21, no. 9, pp. 928-932, Sep. 1999.##[7] K. Zagoris, K. Ergina, and N. Papamarkos, "A document image retrieval system," Engineering Application of Artificial Intelligence, vol. 23, no. 6, pp. 872-879, 2010.##[8] S. Bai, L. Li, and C. L. 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طراحی و پیاده‌سازی سامانه شناسایی و تصحیح خطای املایی متون فارسی مبتنی بر معنای واژگان Design and implementation of Persian spelling detection and correction system based on Semantic 1 1 طراحی و پیاده‌سازی ابزارهای پردازش زبان طبیعی فارسی، بر اساس ویژگی‌های خاص این زبان، همواره با چالش‌هایی مواجه است. با توجه به این‌که  سامانه‌های تصحیح املای خودکار در حوزه‌های مختلفی از قبیل تصحیح پرس‌و‌جوها، بررسی املای واژگان در اینترنت و برنامه‌های ویراستاری متنی کاربرد دارد، لازم است تا برای زبان فارسی نیز نرم‌افزارهای مناسب ایجاد شود. در این مقاله ابتدا مقدمه‌ای در‌خصوص انواع خطاهای املایی، راه‌کارهای شناسایی و تصحیح خطاها شرح داده شده و سپس به معرفی سامانه پارسی‌اسپل که بر اساس معنای واژگان فارسی، خطاها را شناسایی و تصحیح می‌کند، می‌پردازیم. با توجه به نتایج حاصله از ارزیابی سامانه پارسی‌اسپل با سایر نرم‌افزارهای  مشابه رایج، مشخص شد که سامانه پارسی اسپل به‌عنوان ابزار مؤثری جهت شناسایی و پیشنهاد واژه‌های صحیح برای خطاهای غیر‌واژه و واژه حقیقی است. در مراحل شناسایی و پیشنهاد، معیارF- به‌صورت معناداری بهبود یافته است. همچنین نتایج ارزیابی نشان داده که سامانه پارسی اسپل خطاهای واژه حقیقی بیشتری را شناسایی کرده و قادر به ارائه  و پیشنهاد واژه‌های جایگزین صحیح، برای واژه‌های نادرست است و مقدار معیار بازخوانی در شناسایی خطای واژه حقیقی به‌صورت معناداری بیشتر از نرم‌افزارهای رقیب آن است.   2 Persian Language has a special feature (grapheme, homophone, and multi-shape clinging characters) in electronic devices. Furthermore, design and implementation of NLP tools for Persian are more challenging than other languages (e.g. English or German). Spelling tools are used widely for editing user texts like emails and text in editors.  Also developing Persian tools will provide Persian programs to check spell and reduce errors in electronic texts. In this work, we review the spelling detection and correction methods, especially for the Persian language. The proposed algorithm consists of two steps. The first step is non-word error detection and correction by intelligent scoring algorithm. The second step is read-word error detection and correction.  We propose a spelling system "Perspell” for Persian non-word and real-word errors using a hybrid scoring system and optimized language model by lexicon. This scoring system uses a combination of lexical and semantic features optimized by learning dataset. The weight of these features in scoring system is also optimized by learning phase. Perspell is compared with known Persian spellchecker systems and could overcome them in precision of detection and correction. Accordingly, the proposed Persian spell-checker system can also detect and correct real-word errors. This open challenge category of spelling is a complicated and time consuming task in Persian as well as, assessing the proposed method, the F-measure metric has improved significantly (about 10%) for detecting and correcting Persian words. In the proposed method, we used Persian language model with bootstrapping and smoothing to overcome data sparseness and lack of data. The bootstrapping is developed using a Persian dictionary and further we used word sense disambiguation to select the correct related replaced word.   128 117 2017/12/152018/04/252017/11/242018/06/222016/11/42017/07/222017/10/42017/10/242017/05/8 1396/2/18 2019/06/192019/07/102019/01/92019/09/42019/06/192019/06/192019/08/312019/06/192019/06/19 1398/3/29 محمدباقر دستغیب M.B. Dastgheib گروه پژوهشی طراحی و عملیات سیستم‌ها، مرکز منطقه‌ای اطلاع‌رسانی علوم و فناوری ایران dastghaib@ricest.ac.ir سارا کلینی Sara koleini کارشناس خبره مهندسی شبکه، مرکز منطقه‌ای اطلاع‌رسانی علوم و فناوری ایران koleini@ricest.ac.ir سید مصطفی فخراحمد S.M. Fakhrahmad بخش علوم و مهندسی کامپیوتر، دانشکده برق و مهندسی کامپیوتر ایران fakhrahmad@shirazu.ac.ir Spell Error Detection Spell Error Correction Persian spell Checker NLP Persian Language Model سیستم خطا‌یاب فارسی تصحیح خطای واژگان شناسایی خطای واژگان پردازش زبان طبیعی مدل زبان فارسی [1] A. Sorokin, “Spelling Correction for Morphologically Rich language: a case study of Russian,” in Proceeding of the 6th on Balto-Slavic Natural Language Processing, Valencia, Spain, pp. 45-53, 2017.##[2] K. Kukich, “Techniques for automatically co-rrecting words in text”, ACM Computing Surveys (CSUR), vol. 24, pp. 377–439, 1992.##[3] O. Kashefi, M Sharifi, and B. Minaie,” A novel string distance metric for ranking Persian res-pelling suggestions”, Natural Language En-gineering, vol. 19, pp. 259–84, 2013.##[4] R. Mitton, “Ordering the suggestions of a spellchecker without using context”, Natural Language Engineering, vol. 15, pp. 173–192, 2008.##[5] F. J. Damerau, “A technique for computer detection and correction of spelling errors”, Communications of the ACM, vol.7, pp. 171–6, 1964.##[6] J. C. Wu, H. W ,Chiu, J. Chang, “Integrating dictionary and Web N-grams for chinese spell checking”, Computational Linguistics and Chinese Language Processing, vol.18, pp.17–30, 2013.##[7] M. Janidarmian, A. Roshan Fekr, K. Radecka, Z. Zilic, “A comprehensive analysis on wearable ac-celeration sensors in human activity recognition”, Sensors. vol 17, No. 3, 2017.##[8] N. Gupta and M. Pratistha, “Spell Checking Techniques in NLP: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, vol 2, Issue 12, December 2012.##[9] F. Ahmed and et al, “Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness” [online].Available:http://-citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.3996##[10] D. Naber, “A Rule-Based Style and Grammar Checker”, 2003, [online].Available: http://www-.danielnaber.de/languagetool/download/style_and_grammar_checker.pdf##[11] R. A. Wagner and M. J. Fischer, “The string-to-string correction problem,” J. ACM, vol. 21, no. 1, pp. 168–173, 1974.##[12] E. Zamora, J. Pollock, “The use of trigram analysis for spelling error Detection”, Information Pro-cessing & Management, vol 17, pp. 305-316, 1981.##[13] K. Toutanova and R. C. Moore, “Pronunciation modeling for improved spelling correction”. In Proceedings of the 40th Annual Meeting on. Association for Computational Linguistics, pp. 144–151, 2002.##[14] J. Schaback and F. Li, “Multi-level feature extraction for spelling correction”, in IJCAI-2007 Workshop on Analytics for Noisy Unstructured Text Data, pp.79–86, 2007.##[15] R. Mitton, “Spelling checkers, spelling correctors and the misspellings of poor spellers,” Inf. Process. Manag., vol. 23, pp. 495–505, 1987.##[16] T. M. Miangah, “FarsiSpell: a spell-checking system for Persian using a large monolingual corpus”. Literary and Linguistic Computing, vol 29, pp. 56–73, 2014.##[17] L. Barar, and B. QasemiZadeh,”CloniZER Spell Checker Adaptive Language Independent Spell Checker” In AIML Conference CICC, Cairo, Egypt, pp. 19–21, 2005.##[18] M. S. Rasooli, O.Kahefi, and B.Minaei-Bidgoli, “Effect of Adaptive Spell Checking in Persian” in Natural Language Processing and Knowledge Engineering (NLP-KE), 7th International Conference on IEEE, 2011. pp. 161–4.##[19] M. Shamsfard, H.S. Jafari, and M.Ilbeygi, “STeP-1: A Set of Fundamental Tools for Persian Text” in Processing. LREC, Malta, 2010.##[20] O, Kashefi, M. Nasri, and K. Kanani.” Automatic Spell Checking in Persian Language”. In Supreme Council of Information and Co-mmunication Technology (SCICT), Tehran, Iran, 2010.##[21] H. Faili, N. Ehsan, M. Montazery and M. T. Pilehvar, “Vafa spell-checker for detecting spelling, grammatical,and real-word errors of Persian language,” Literary and Linguistic Computing, vol. 31, pp. 95-117, 2016.##[22] M. Shamsfard, “Challenges and open problems in Persian text processing,” Proc. LTC, vol. 11, 2011.##[23] P. Samanta and B. Chaudhuri, “A simple Readword Error Detection and Correction Using Local Word Bigram and Trigram,” in Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013), Taiwan, R.O.C, 2013. pp. 211-220.##[1] A. Sorokin, “Spelling Correction for Morphologically Rich language: a case study of Russian,” in Proceeding of the 6th on Balto-Slavic Natural Language Processing, Valencia, Spain, pp. 45-53, 2017.##[2] K. Kukich, “Techniques for automatically co-rrecting words in text”, ACM Computing Surveys (CSUR), vol. 24, pp. 377–439, 1992.##[3] O. Kashefi, M Sharifi, and B. Minaie,” A novel string distance metric for ranking Persian res-pelling suggestions”, Natural Language En-gineering, vol. 19, pp. 259–84, 2013.##[4] R. Mitton, “Ordering the suggestions of a spellchecker without using context”, Natural Language Engineering, vol. 15, pp. 173–192, 2008.##[5] F. J. Damerau, “A technique for computer detection and correction of spelling errors”, Communications of the ACM, vol.7, pp. 171–6, 1964.##[6] J. C. Wu, H. W ,Chiu, J. Chang, “Integrating dictionary and Web N-grams for chinese spell checking”, Computational Linguistics and Chinese Language Processing, vol.18, pp.17–30, 2013.##[7] M. Janidarmian, A. Roshan Fekr, K. Radecka, Z. Zilic, “A comprehensive analysis on wearable ac-celeration sensors in human activity recognition”, Sensors. vol 17, No. 3, 2017.##[8] N. Gupta and M. Pratistha, “Spell Checking Techniques in NLP: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, vol 2, Issue 12, December 2012.##[9] F. Ahmed and et al, “Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness” [online].Available:http://-citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.3996##[10] D. Naber, “A Rule-Based Style and Grammar Checker”, 2003, [online].Available: http://www-.danielnaber.de/languagetool/download/style_and_grammar_checker.pdf##[11] R. A. Wagner and M. J. Fischer, “The string-to-string correction problem,” J. ACM, vol. 21, no. 1, pp. 168–173, 1974.##[12] E. Zamora, J. Pollock, “The use of trigram analysis for spelling error Detection”, Information Pro-cessing & Management, vol 17, pp. 305-316, 1981.##[13] K. Toutanova and R. C. Moore, “Pronunciation modeling for improved spelling correction”. In Proceedings of the 40th Annual Meeting on. Association for Computational Linguistics, pp. 144–151, 2002.##[14] J. Schaback and F. Li, “Multi-level feature extraction for spelling correction”, in IJCAI-2007 Workshop on Analytics for Noisy Unstructured Text Data, pp.79–86, 2007.##[15] R. Mitton, “Spelling checkers, spelling correctors and the misspellings of poor spellers,” Inf. Process. Manag., vol. 23, pp. 495–505, 1987.##[16] T. M. Miangah, “FarsiSpell: a spell-checking system for Persian using a large monolingual corpus”. Literary and Linguistic Computing, vol 29, pp. 56–73, 2014.##[17] L. Barar, and B. QasemiZadeh,”CloniZER Spell Checker Adaptive Language Independent Spell Checker” In AIML Conference CICC, Cairo, Egypt, pp. 19–21, 2005.##[18] M. S. Rasooli, O.Kahefi, and B.Minaei-Bidgoli, “Effect of Adaptive Spell Checking in Persian” in Natural Language Processing and Knowledge Engineering (NLP-KE), 7th International Conference on IEEE, 2011. pp. 161–4.##[19] M. Shamsfard, H.S. Jafari, and M.Ilbeygi, “STeP-1: A Set of Fundamental Tools for Persian Text” in Processing. LREC, Malta, 2010.##[20] O, Kashefi, M. Nasri, and K. Kanani.” Automatic Spell Checking in Persian Language”. In Supreme Council of Information and Co-mmunication Technology (SCICT), Tehran, Iran, 2010.##[21] H. Faili, N. Ehsan, M. Montazery and M. T. Pilehvar, “Vafa spell-checker for detecting spelling, grammatical,and real-word errors of Persian language,” Literary and Linguistic Computing, vol. 31, pp. 95-117, 2016.##[22] M. Shamsfard, “Challenges and open problems in Persian text processing,” Proc. LTC, vol. 11, 2011.##[23] P. Samanta and B. Chaudhuri, “A simple Readword Error Detection and Correction Using Local Word Bigram and Trigram,” in Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013), Taiwan, R.O.C, 2013. pp. 211-220. ##

پردازش تصویر بین‌دامنه‌ای با استفاده از تحلیل تفکیک خطی و تطبیق دامنه مبتنی‌بر نمونه Sample-oriented Domain Adaptation for Image Classification 1 1 پردازش تصویر روشی برای اعمال برخی عملیات­ه‌ا بر روی یک تصویر است به‌­طوری‌­که با استفاده از آن، تصاویری با کیفیت بالاتر به­‌دست آمده یا برخی اطلاعات مفید از تصویر استخراج می­شود. الگوریتم­‌های سنتی پردازش تصویر در شرایطی­‌که تصاویر آموزشی (دامنه منبع) که برای یاددهی مدل استفاده می­‌شوند، توزیع متفاوتی از تصاویر آزمایش (دامنه هدف) داشته باشند، نمی­‌توانند عملکرد خوبی داشته باشند. با این‌حال، بسیاری از برنامه‌های کاربردی دنیای واقعی به‌علت کمبود داده‌های برچسب‌دار آموزشی دارای محدودیت هستند؛ از‌این‌رو از داده‌های برچسب‌دار دامنه‌های دیگر استفاده می‌کنند. به‌این ترتیب به‌خاطر اختلاف توزیع بین دامنه‌های منبع و هدف، طبقه‌بند یادگرفته شده براساس مجموعه آموزشی بر روی داده‌های آزمایشی عملکرد ضعیفی خواهد داشت. یادگیری انتقالی و انطباق دامنه، با به‌کارگیری مجموعه‌داده‌های موجود دو راه ‌حل برجسته برای مقابله با این چالش هستند، و حتی با وجود اختلاف توزیع قابل ملاحظه بین دامنه‌ها می‌توانند دانش را از دامنه‌های مرتبط به دامنه هدف انتقال دهند. فرض اصلی در مسأله تغییر دامنه این است که توزیع حاشیه‌ای یا توزیع شرطی داده‌های منبع و هدف متفاوت باشد. تطبیق دامنه به‌طور صریح با استفاده از معیار فاصله ازپیش تعیین‌شده تفاوت در توزیع حاشیه‌ای، توزیع شرطی یا هر دو توزیع را کاهش می‌دهد. در این مقاله، ما به یک سناریوی چالش‌برانگیز می‌پردازیم که در آن تصاویر دامنه‌های منبع و هدف در توزیع‌های حاشیه‌ای متفاوت بوده و تصاویر هدف دارای برچسب نیستند. بیش‌تر روش‌های قبلی دو استراتژی یادگیری تطابق ویژگی‌ها و وزن‌دهی مجدد نمونه‌ها را به‌طور مستقل برای تطبیق دامنه‌ها مورد بررسی قرار داده‌اند. در این مقاله، ما نشان می‌دهیم زمانی که تفاوت دامنه‌ها به‌طور قابل توجهی بزرگ باشد، هر دو استراتژی مهم و اجتناب‌ناپذیر هستند. روش پیشنهادی ما تحت عنوان تطبیق دامنه مبتنی‌بر نمونه برای طبقه‌بندی تصاویر (DAIC)، یک فرایند کاهش بُعد بوده که با کاهش اختلاف توزیع تصاویر آموزشی و آزمایشی و به‌کارگیری هم‌زمان تطابق ویژگی‌ها و وزن‌دهی مجدد کارایی مدل را افزایش می‌دهد. ما با گسترش واگرایی برگمن غیرخطی برای اندازه‌گیری تفاوت توزیع حاشیه‌ای و اعمال آن به الگوریتم کاهش بعد آنالیز تفکیک خطی فیشر، از آن برای ساخت یک نمایش ویژگی مؤثر و قوی برای تفاوت‌های توزیع قابل ملاحظه بین دامنه‌ها استفاده می‌کنیم؛ همچنین، DAIC از مزیت برچسب‌گذاری اولیه برای داده‌های هدف به‌صورت تکرار‌شونده برای هم‌گرایی مدل استفاده می‌کند. آزمایش‌های گسترده ما نشان می‌دهد که DAIC به‌طور قابل توجهی بهتر از الگوریتم‌های یادگیری ماشین پایه و دیگر روش‌های یادگیری انتقالی در نُه مجموعه داده‌ بصری تحت سناریوهای مختلف عمل می‌کند. 2 Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applications suffer from a limited number of training labeled data and therefore benefit from the related available labeled datasets to train the model. In this way, since there is the distribution difference across the source and target domains (domain shift problem), the learned classifier on the training set might perform poorly on the test set. Transfer learning and domain adaptation are two outstanding solutions to tackle this challenge by employing available datasets, even with significant difference in distribution and properties, to transfer the knowledge from a related domain to the target domain. The main assumption in domain shift problem is that the marginal or the conditional distribution of the source and the target data is different. Distribution adaptation explicitly minimizes predefined distance measures to reduce the difference in the marginal distribution, conditional distribution, or both. In this paper, we address a challenging scenario in which the source and target domains are different in marginal distributions, and the target images have no labeled data. Most prior works have explored two following learning strategies independently for adapting domains: feature matching and instance reweighting. In the instance reweighting approach, samples in the source data are weighted individually so that the distribution of the weighted source data is aligned to that of the target data. Then, a classifier is trained on the weighted source data. This approach can effectively eliminate unrelated source samples to the target data, but it would reduce the number of samples in adapted source data, which results in an increase in generalization errors of the trained classifier. Conversely, the feature-transform approach creates a feature map such that distributions of both datasets are aligned while both datasets are well distributed in the transformed feature space. In this paper, we show that both strategies are important and inevitable when the domain difference is substantially large. Our proposed using sample-oriented Domain Adaptation for Image Classification (DAIC) aims to reduce the domain difference by jointly matching the features and reweighting the instances across images in a principled dimensionality reduction procedure, and construct new feature representation that is invariant to both the distribution difference and the irrelevant instances. We extend the nonlinear Bregman divergence to measure the difference in marginal, and integrate it with Fisher’s linear discriminant analysis (FLDA) to construct feature representation that is effective and robust for substantial distribution difference. DAIC benefits pseudo labels of target data in an iterative manner to converge the model. We consider three types of cross-domain image classification data, which are widely used to evaluate the visual domain adaptation algorithms: object (Office+Caltech- 256), face (PIE) and digit (USPS, MNIST). We use all three datasets prepared by and construct 34 cross-domain problems. The Office-Caltech-256 dataset is a benchmark dataset for cross-domain object recognition tasks, which contains 10 overlapping categories from following four domains: Amazon (A), Webcam (W), DSLR (D) and Caltech256 (C). Therefore 4 × 3 = 12 cross domain adaptation tasks are constructed, namely A → W, ..., C → D. USPS (U) and MNIST (M) datasets are widely used in computer vision and pattern recognition tasks. We conduct two handwriting recognition tasks, i.e., usps-mnist and mnist-usps. PIE is a benchmark dataset for face detection task and has 41,368 face images of size 3232 from 68 individuals. The images were taken by 13 synchronized cameras and 21 flashes, under varying poses, illuminations, and expressions. PIE dataset consists five subsets depending on the different poses as follows: PIE1 (C05, left pose), PIE2 (C07, upward pose), PIE3 (C09, downward pose), PIE4 (C27, frontal pose), PIE5 (C29, right pose). Thus, we can construct 20 cross domain problems, i.e., P1 → P2, P1 → P3, ..., P5 → P4. We compare our proposed DAIC with two baseline machine learning methods, i.e., NN, Fisher linear discriminant analysis (FLDA) and nine state-of-the-art domain adaptation methods for image classification problems (TSL, DAM, TJM, FIDOS and LRSR). Due to these methods are considered as dimensionality reduction approaches, we train a classifier on the labeled training data (e.g., NN classifier), and then apply it on test data to predict the labels of the unlabeled target data. DAIC efficiently preserves and utilizes the specific information among the samples from different domains. The obtained results indicate that DAIC outperforms several state of-the-art adaptation methods even if the distribution difference is substantially large. 148 129 2017/12/152018/04/252017/11/242018/06/222016/11/42017/07/222017/10/42017/10/242017/05/82018/03/13 1396/12/22 2019/06/192019/07/102019/01/92019/09/42019/06/192019/06/192019/08/312019/06/192019/06/192019/07/3 1398/4/12 مژده زندی فر Mozhdeh Zandifar دانشکده مهندسی فناوری اطلاعات و کامپیوتر، دانشگاه صنعتی ارومیه ایران mozhdeh.zandifar@it.uut.ac.ir جعفر طهمورث نژاد Jafar Tahmoresnezhad دانشکده مهندسی فناوری اطلاعات و کامپیوتر، دانشگاه صنعتی ارومیه ایران j.tahmores@it.uut.ac.ir Image processing Transfer learning Bregman divergence Marginal distribution difference reduction Dimensionality reduction پردازش تصویر یادگیری انتقالی واگرایی برگمن کاهش اختلاف توزیع حاشیه‌ای کاهش ابعاد [1] S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2010.##[2] L. Shao, F. Zhu, and X. Li, “Transfer learning for visual categorization: A survey,” IEEE trans-actions on neural networks and learning systems, vol. 26, no. 5, pp. 1019–1034, 2015.##[3] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Machine learning, vol. 79, no. 1-2, pp. 151–175, 2010.##[4] J. Tahmoresnezhad , and S. Hashemi, “A generalized kernel-based random k-sample sets method for transfer learning”, Iran J Sci Technol Trans Electrical Eng, vol. 39, pp. 193-207, 2015.##[5] S. Si, D. Tao, and B. Geng, “Bregman diver-gence-based regularization for transfer subspace learning”, IEEE TKDE, 2010.##[6] L. M. Bregman, “The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming”, USSR Computational Mathe-matics and Mathematical Physics, vol, 7, pp. 200–217, 1967.##[7] M. Long, J. Wang, G. Ding, S. J. Pan and P. Yu, “Adaptation regularization: a general frame-work for transfer learning”, IEEE Trans. Knowl. Data Eng, vol. 26, pp. 1076–1089, 2013.##[8] X. Li, M. Fang, J. J. Zhang, and J. Wu, “Sample selection for visual domain adaptation via sparse coding”, Signal Processing: Image Communi-cation, vol 44, pp. 92-100, 2016.##[9] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer feature learning”, KnowlInf Syst, vol. 50, no. 2, pp.585-605, 2016.##[10] M. Long, J. Wang, G. Ding, J. Sun, and P. Yu, “Transfer feature learning with joint distribution adaptation,” in Proc IEEE International Con-ference on Computer Vision, pp. 2200–2207, 2013.##[11] M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, “Scatter component analysis: A unified framework for domain adaptation and domain generalization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1–1, 2016.##[12] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936.##[13] K. Saenko, B. Kulis, M. Fritz and T. Darrell, “Adapting visual category models to new domains”, Proceedings of the European Conference on Computer Vision, pp. 213-226, 2010.##[14] J. J. Hull, “A database for handwritten text recognition research”, IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550–554, 1994.##[15] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition”, Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.##[16] T. Sim, S. Baker ,and M. Bsat, “The CMU pose, illumination, and expression (PIE) database”, Proceedings of Fifth IEEE International Con-ference on Automatic Face Gesture Recognition, pp. 53-58, 2002.##[17] Cuong V Dinh, Robert PW Duin, Ignacio Piqueras-Salazar, and Marco Loog. Fidos: A generalized fisher based feature extraction method for domain shift. Pattern Recognition, 46(9):2510–2518, 2013.##[18] L. Duan, D. Xu, I.W. Tsang, “Domain adaptation from multiple sources: A domain-dependent regularization approach,” IEEE Trans, Neural Netw. Learn. Syst. vol.23, pp. 504–518, 2012.##[19] M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer joint matching for unsupervised domain adaptation,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1410–1417 , 2014.##[20] Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2016.##[21] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer285 feature learning,” Knowledge and Information Systems, vol. 50, no. 2, pp. 585– 605, 2017.##[22] J. Liu, J. Li, and K. Lu, “Coupled local–global adaptation for multi-source transfer learning,” Neurocomputing, vol. 275, pp. 247–254, 2018.##[23] L. Luo, X. Wang, S. Hu, C. Wang, Y. Tang, and L. Chen, “Close yet distinctive domain adaptation,” arXiv preprint arXiv: 1704.04235, 2017.##[24] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for transfer learning,” in Proceedings of the 24th international conference on Machine learning, 2007, pp. 193–200.##[25] Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama, “Direct density ratio estimation for large-scale covariate shift adaptation.” Information and Media Technologies, vol. 4, no. 2, pp. 529–546, 2009.##[26] J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, “Dataset Shift in Machine Learning”, The MIT Press, 2009.##[27] S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in Proceedings of the 19th international conference on World Wide Web. ACM, pp. 751–760, 2010.##[28] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks, vol. 22, no. 2, pp. 199 – 210, 2011.##[29] X. Shi, Q. Liu, W. Fan, and P. S. Yu, “Transfer across completely different feature spaces via spectral embedding,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 906–918, 2013.##[30] R. Aljundi, R. Emonet, D. Muselet, and M. Sebban, “Landmarks-based kernelized subspace alignment for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 56–63, 2015.##[31] M. Gheisari and M. S. Baghshah, “Joint predictive model and representation learning for visual domain adaptation,” Engineering Appli-cations of Artificial Intelligence, vol. 58, pp. 157–170, 2017.##[32] Z. Ding, M. Shao, and Y. Fu, “Deep low-rank coding for transfer learning,” in Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 2015, pp. 3453–3459.##[33] M. Long, J. Wang, G. Ding, J. Sun, and P. Yu, “Transfer feature learning with joint distribution adaptation,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2200– 2207.##[34] H. Li, T. Jiang, and K. Zhang, “Efficient and robust feature extraction by maximum margin criterion,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 157–165, 2006.##[35] M. Long, J. Wang, G. Ding, S. J. Pan, and P. S. Yu, “Adaptation regularization: A general framework for transfer learning,” IEEE Tran-sactions on Knowledge and Data Engineering, vol. 26, no. 5, pp. 1076–1089, 2014.##[36] Y. Xu, S. J. Pan, H. Xiong, Q. Wu, R. Luo, H. Min, and H. Song, “A uni- 310 fied framework for metric transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 6, pp. 1158–1171, 2017.##[37] S. Z. Seyyedsalehi, S. A. Seyyedsalehi, “Improving nonlinear manifold separator model to the face recognition by a single image of per person”, Signal and Data Processing, vol. 12, no. 1, pp. 3–16, 2015.##[38] S. Ahmadkhani, and P. Adibi, “Supervised Probability Component Analysis Mixture Model in a Lossless Dimensionality Reduction Frame-work for Face Recognition”, Signal and Data Processing, vol. 12, no. 4, pp. 53–65, 2016.##[1] S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2010.##[2] L. Shao, F. Zhu, and X. Li, “Transfer learning for visual categorization: A survey,” IEEE trans-actions on neural networks and learning systems, vol. 26, no. 5, pp. 1019–1034, 2015.##[3] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Machine learning, vol. 79, no. 1-2, pp. 151–175, 2010.##[4] J. Tahmoresnezhad , and S. Hashemi, “A generalized kernel-based random k-sample sets method for transfer learning”, Iran J Sci Technol Trans Electrical Eng, vol. 39, pp. 193-207, 2015.##[5] S. Si, D. Tao, and B. Geng, “Bregman diver-gence-based regularization for transfer subspace learning”, IEEE TKDE, 2010.##[6] L. M. Bregman, “The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming”, USSR Computational Mathe-matics and Mathematical Physics, vol, 7, pp. 200–217, 1967.##[7] M. Long, J. Wang, G. Ding, S. J. Pan and P. Yu, “Adaptation regularization: a general frame-work for transfer learning”, IEEE Trans. Knowl. Data Eng, vol. 26, pp. 1076–1089, 2013.##[8] X. Li, M. Fang, J. J. Zhang, and J. Wu, “Sample selection for visual domain adaptation via sparse coding”, Signal Processing: Image Communi-cation, vol 44, pp. 92-100, 2016.##[9] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer feature learning”, KnowlInf Syst, vol. 50, no. 2, pp.585-605, 2016.##[10] M. Long, J. Wang, G. Ding, J. Sun, and P. Yu, “Transfer feature learning with joint distribution adaptation,” in Proc IEEE International Con-ference on Computer Vision, pp. 2200–2207, 2013.##[11] M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, “Scatter component analysis: A unified framework for domain adaptation and domain generalization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1–1, 2016.##[12] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936.##[13] K. Saenko, B. Kulis, M. Fritz and T. Darrell, “Adapting visual category models to new domains”, Proceedings of the European Conference on Computer Vision, pp. 213-226, 2010.##[14] J. J. Hull, “A database for handwritten text recognition research”, IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550–554, 1994.##[15] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition”, Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.##[16] T. Sim, S. Baker ,and M. Bsat, “The CMU pose, illumination, and expression (PIE) database”, Proceedings of Fifth IEEE International Con-ference on Automatic Face Gesture Recognition, pp. 53-58, 2002.##[17] Cuong V Dinh, Robert PW Duin, Ignacio Piqueras-Salazar, and Marco Loog. Fidos: A generalized fisher based feature extraction method for domain shift. Pattern Recognition, 46(9):2510–2518, 2013.##[18] L. Duan, D. Xu, I.W. Tsang, “Domain adaptation from multiple sources: A domain-dependent regularization approach,” IEEE Trans, Neural Netw. Learn. Syst. vol.23, pp. 504–518, 2012.##[19] M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer joint matching for unsupervised domain adaptation,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1410–1417 , 2014.##[20] Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2016.##[21] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer285 feature learning,” Knowledge and Information Systems, vol. 50, no. 2, pp. 585– 605, 2017.##[22] J. Liu, J. Li, and K. Lu, “Coupled local–global adaptation for multi-source transfer learning,” Neurocomputing, vol. 275, pp. 247–254, 2018.##[23] L. Luo, X. Wang, S. Hu, C. Wang, Y. Tang, and L. Chen, “Close yet distinctive domain adaptation,” arXiv preprint arXiv: 1704.04235, 2017.##[24] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for transfer learning,” in Proceedings of the 24th international conference on Machine learning, 2007, pp. 193–200.##[25] Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama, “Direct density ratio estimation for large-scale covariate shift adaptation.” Information and Media Technologies, vol. 4, no. 2, pp. 529–546, 2009.##[26] J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, “Dataset Shift in Machine Learning”, The MIT Press, 2009.##[27] S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in Proceedings of the 19th international conference on World Wide Web. ACM, pp. 751–760, 2010.##[28] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks, vol. 22, no. 2, pp. 199 – 210, 2011.##[29] X. Shi, Q. Liu, W. Fan, and P. S. 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