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استخراج ویژگی جهت شناسایی ترافیک شبکه با درنظر‌گرفتن اثرات اتلاف بسته‌ها Feature Extraction to Identify Network Traffic with Considering Packet Loss Effects 1 1 شناسایی ترافیک شبکه یکی از نیازهای اساسی مدیران جهت کنترل شبکه، برای بهبود کیفیت خدمات‌دهی و حفظ امنیت در شبکه است. یکی از چالش‌­های اساسی در روش­‌های مبتنی بر تحلیل آماری بسته­ها، شناسایی ترافیک شبکه، مسأله از دست‌دادن (اتلاف) بسته­‌ها است که استفاده از ویژگی­‌های آماری در تحلیل ترافیک شبکه را با مشکل جدی روبه‌رو می­‌سازد. این مسأله، ویژگی­‌های آماری بسته­‌ها نظیر فاصله زمانی بین ارسال بسته‌­های متوالی برنامه­‌های کاربردی را تحت تأثیر قرار می­‌دهد، و در مواردی دقت شناسایی ترافیک را به میزان قابل توجهی کاهش می­‌دهد. هدف اصلی این مقاله بررسی تأثیرات اتلاف بسته­‌ها بر روی ویژگی­‌های آماری، و در نتیجه دقت شناسایی برنامه‌های کاربردی، و همچنین استخراج ویژگی‌­های مناسب جهت چیره‌شدن بر این تأثیرات است. بدین منظور، رفتار چهار ویژگی آماری، مورد بررسی قرار گرفته و با استخراج ویژگی از توزیع آنها ترافیک شبکه شناسایی می­‌شود. به همین منظور پایگاه داده‌­ای از ترافیک هفت برنامه کاربردی با نرخ­‌های مختلفی از اتلاف بسته، تهیه شده و میزان صحت تشخیص برنامه­‌های کاربردی به‌وسیله شبکه عصبی، مورد تحلیل قرار گرفته است. نتایج نشان می­‌دهد که ویژگی‌­های استخراج‌شده در مقابل رخداد اتلاف بسته­‌ها مقاوم بوده و دقت شناسایی ترافیک شبکه را در حالت­‌های مختلف رخداد اتلاف بسته به حالت ایده‌­آل (عدم رخداد اتلاف بسته در شبکه) نزدیک می­‌کند. 2 There are huge petitions of network traffic coming from various applications on Internet. In dealing with this volume of network traffic, network management plays a crucial rule. Traffic classification is a basic technique which is used by Internet service providers (ISP) to manage network resources and to guarantee Internet security. In addition, growing bandwidth usage, at one hand, and limited physical capacity of communication lines, at the other hand, lead providers to improve utilization quality of network resources. In fact, classification or identification of network is a critical task in network processing for traffic management, anomaly detection, and also to improve network quality-of-service (QoS). Port and payload based methods are two classical techniques which are applicable under traditional network conditions. However, many Internet applications use dynamic port numbers for communications, which lead to difficulties in identifying traffic using port numbers. Also many applications encrypt the data before transmitting to avoid detection. Therefore, payload-based techniques are inefficient for these traffics. In recent years, statistical feature-based traffic flow identification methods (STFIM) have attracted the interest of many researchers. The most important part of a STFIM is the selection of efficient statistical features. Preliminary analysis shows that the problem of packet loss in data transmission is one of the major challenges in employing STFIM for network traffic identification. This affects the statistical characteristics of packets, such as the time interval between sending successive application packets, and in some cases significantly reduces the accuracy of traffic identification. The main goal of this paper is to examine the effects of packet loss on statistical features, and therefore the accuracy of identifying applications, as well as extracting appropriate features to overcome these effects. For this purpose, the behavior of four statistical features, including the packet size, the time interval between sending and receiving packets, the duration of the flows and the rate of sending packets, are investigated; then applications traffics are identified via considering characteristics of their distribution. We collected a database of network traffic flow from seven applications with different rates of packet loss. We used the extracted features in a multilayer neural network, as a classifier, to differentiate between different traffic applications. Experimental results show that the extracted features are robust against the packets loss, and the accuracy of the network traffic identification is close to the ideal state (traffic flow with no packet lost).   3 16 2018/01/3 1396/10/13 2018/05/23 1397/3/2 محمدرضا گندمی Mohammadreza Gandomi دانشگاه صنعتی شاهرود ایران ga.mohamadreza@gmail.com حمید حسن پور Hamid Hassanpour دانشگاه صنعتی شاهرود ایران h.hassanpour@shahroodut.ac.ir Network Traffic Network traffic Identification Machine Learning Packet Loss ترافیک شبکه شناسایی ترافیک شبکه یادگیری ماشین از دست دادن بسته [1] M. Crotti, M. Dusi, F. Gringoli, L. Salgarelli, “ Traffic classification through simple statistical fingerprinting”, ACM SIGCOMM Comput. Commun, Rev. 37, pp.5–16, 2007.##[2] M. Jain, D.S. Tomar, S.K. Singh, “A Survey on TCP Congestion Control Schemes in Guided Media and Unguided Media Communication”, Int. J. Comput. Appl. Pp.118, 2015.##[3] M.A. Kafi, D. Djenouri, J. Ben-Othman, N. Badache, “Congestion control protocols in wireless sensor networks: a survey”, IEEE Commun. Surv. Tutorials, vol.16, pp.1369–1390 2014.##[4] B. Yamansavascilar, M.A. Guvensan, A.G. Yavuz, M.E. Karsligil, “Application identification via network traffic classification”, Computing, Networking and Communications (ICNC), International Conference on, pp. 843–848, 2017. ##[5] J. Kim, J. Hwang, K. Kim, K, “High-performance internet traffic classification using a Markov model and Kullback-Leibler divergence”, Mob. Inf. Syst. 2016.##[6] J. Muehlstein, Y. Zion, M. Bahumi, I. Kirshenboim, R. Dubin, A. Dvir, O. Pele, “Analyzing HTTPS Encrypted Traffic to Identify User Operating System, Browser and Application” arXiv Prepr. arXiv1603.04865, 2016.##[7] H.R. Loo, S.B. Joseph, M.N. Marsono, “ Online incremental learning for high bandwidth network traffic classification”, Appl. Comput. Intell. Soft Comput, vol. 1, 2016.##[8] T. Qin, L.Wang, Z. Liu, X. Guan, “Robust application identification methods for P2P and VoIP traffic classification in backbone networks”, Knowledge-Based Syst. Vol.82, pp.152–162, 2016.##[9] M.S. Aliakbarian, A. Fanian, F.S. Saleh, T.A. Gulliver, “ Optimal supervised feature extraction in internet traffic classification” Communications, Computers and Signal Processing (PACRIM), IEEE Pacific Rim Conference on, pp. 102–107, 2013.##[10] Z. Chen, L. Peng, C. Gao, B. Yang, Y. Chen, J. Li, “Flexible neural trees based early stage identification for IP traffic”, Soft Comput. Vol. 21, pp. 2035–2046, 2017.##[11] F. Ertam, E. Avci, “A new approach for internet traffic classification: GA-WK-ELM”, Measurement, Vol. 95, pp.135–142,2017.##[12] A. Este, F. Gringoli, L. Salgarelli, “On the stability of the information carried by traffic flow features at the packet level”, ACM SIGCOMM Comput. Commun. Rev. 39, 2009.##[13] M. Gandomi, H. Hassanpour, “Behavioral Analysis of Traffic Flow for an Effective Network Traffic Identification”, International Journal of Engineering (IJE), TRANSACTIONS B: Applications ,Vol. 30, No. 11, 2017, pp.150-160.##[14] J. Bolot, “End-to-end packet delay and loss behavior in the Internet”, ACM SIGCOMM Computer Communication Review, pp. 289–298, 1993.##[15] Z. Chen, Z. Liu, L. Peng, L. Wang, L. Zhang, “A novel semi-supervised learning method for Internet application identification”, Soft Computing, Vol. 21 , pp. 1963—1975, 2017.##[16] N. Saqib, Y. Shakeel, M. Khan, H. Mehmood, M. Zia, “An effective empirical approach to VoIP traffic classification”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 25, pp. 888—900,2017.##[17] H. Shi, H. Li, D. Zhang, C. Cheng, W. Wu, “Efficient and robust feature extraction and selection for traffic classification”, Computer Networks, Vol. 119, 2017, pp. 1—16.##[18] J. Yang, J. Deng, S. Li, Y. Hao, “Improved traffic detection with support vector machine based on restricted Boltzmann machine”, Soft Computing, Vol. 21, pp. 3101—3112, 2017.##[1] M. Crotti, M. Dusi, F. Gringoli, L. Salgarelli, “ Traffic classification through simple statistical fingerprinting”, ACM SIGCOMM Comput. Commun, Rev. 37, pp.5–16, 2007.##[2] M. Jain, D.S. Tomar, S.K. Singh, “A Survey on TCP Congestion Control Schemes in Guided Media and Unguided Media Communication”, Int. J. Comput. Appl. Pp.118, 2015.##[3] M.A. Kafi, D. Djenouri, J. Ben-Othman, N. Badache, “Congestion control protocols in wireless sensor networks: a survey”, IEEE Commun. Surv. Tutorials, vol.16, pp.1369–1390 2014.##[4] B. Yamansavascilar, M.A. Guvensan, A.G. Yavuz, M.E. Karsligil, “Application identification via network traffic classification”, Computing, Networking and Communications (ICNC), International Conference on, pp. 843–848, 2017. ##[5] J. Kim, J. Hwang, K. Kim, K, “High-performance internet traffic classification using a Markov model and Kullback-Leibler divergence”, Mob. Inf. Syst. 2016.##[6] J. Muehlstein, Y. Zion, M. Bahumi, I. Kirshenboim, R. Dubin, A. Dvir, O. Pele, “Analyzing HTTPS Encrypted Traffic to Identify User Operating System, Browser and Application” arXiv Prepr. arXiv1603.04865, 2016.##[7] H.R. Loo, S.B. Joseph, M.N. Marsono, “ Online incremental learning for high bandwidth network traffic classification”, Appl. Comput. Intell. Soft Comput, vol. 1, 2016.##[8] T. Qin, L.Wang, Z. Liu, X. Guan, “Robust application identification methods for P2P and VoIP traffic classification in backbone networks”, Knowledge-Based Syst. Vol.82, pp.152–162, 2016.##[9] M.S. Aliakbarian, A. Fanian, F.S. Saleh, T.A. Gulliver, “ Optimal supervised feature extraction in internet traffic classification” Communications, Computers and Signal Processing (PACRIM), IEEE Pacific Rim Conference on, pp. 102–107, 2013.##[10] Z. Chen, L. Peng, C. Gao, B. Yang, Y. Chen, J. Li, “Flexible neural trees based early stage identification for IP traffic”, Soft Comput. Vol. 21, pp. 2035–2046, 2017.##[11] F. Ertam, E. Avci, “A new approach for internet traffic classification: GA-WK-ELM”, Measurement, Vol. 95, pp.135–142,2017.##[12] A. Este, F. Gringoli, L. Salgarelli, “On the stability of the information carried by traffic flow features at the packet level”, ACM SIGCOMM Comput. Commun. Rev. 39, 2009.##[13] M. Gandomi, H. Hassanpour, “Behavioral Analysis of Traffic Flow for an Effective Network Traffic Identification”, International Journal of Engineering (IJE), TRANSACTIONS B: Applications ,Vol. 30, No. 11, 2017, pp.150-160.##[14] J. Bolot, “End-to-end packet delay and loss behavior in the Internet”, ACM SIGCOMM Computer Communication Review, pp. 289–298, 1993.##[15] Z. Chen, Z. Liu, L. Peng, L. Wang, L. Zhang, “A novel semi-supervised learning method for Internet application identification”, Soft Computing, Vol. 21 , pp. 1963—1975, 2017.##[16] N. Saqib, Y. Shakeel, M. Khan, H. Mehmood, M. Zia, “An effective empirical approach to VoIP traffic classification”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 25, pp. 888—900,2017.##[17] H. Shi, H. Li, D. Zhang, C. Cheng, W. Wu, “Efficient and robust feature extraction and selection for traffic classification”, Computer Networks, Vol. 119, 2017, pp. 1—16.##[18] J. Yang, J. Deng, S. Li, Y. Hao, “Improved traffic detection with support vector machine based on restricted Boltzmann machine”, Soft Computing, Vol. 21, pp. 3101—3112, 2017. ##

یک تحلیل تفاضل ناممکن از الگوریتم رمزقالبی Zorro Novel Impossible Differential Cryptanalysis of Zorro Block Cipher 1 1 تحلیل تفاضل ناممکن ابزاری قوی به منظور ارزیابی امنیتی رمزهای قالبی است که بر پایه یافتن یک مشخصه تفاضلی با احتمال به‌طوردقیق صفر بنا شده است. سرعت انتشار لایه خطی یک رمز قالبی، نقشی اساسی در امنیت الگوریتم رمز در مقابل تحلیل تفاضل ناممکن دارد و با تغییر لایه خطی، امنیت الگوریتم در مقابل تحلیل تفاضل ناممکن به‌شدت تغییر می­‌کند. در این مقاله، روشی کارا و متفاوت برای یافتن مشخصه‌­های تفاضلی رمز قالبی سبک­وزن Zorro ارائه می­‌کنیم که مستقل از ویژگی­‌های لایه خطی الگوریتم است. به بیان دیگر در این مقاله نشان خواهیم داد که مستقل از ویژگی­‌های عناصر الگوریتم، می‌­توان برای نُه دور از الگوریتم Zorro مشخصه تفاضل ناممکن کارایی به‌دست آورد. همچنین برپایه این مشخصه نُه‌دوری، یک حمله بازیابی کلید برای ده دور الگوریتمZorroارائه می‌­کنیم. 2 Impossible difference attack is a powerful tool for evaluating the security of block ciphers based on finding a differential characteristic with the probability of exactly zero. The linear layer diffusion rate of a cipher plays a fundamental role in the security of the algorithm against the impossible difference attack. In this paper, we show an efficient method, which is independent of the quality of the linear layer, can find impossible differential characteristics of Zorro block cipher. In other words, using the proposed method, we show that, independent of the linear layer feature and other internal elements of the algorithm, it is possible to achieve effective impossible differential characteristic for the 9-round Zorro algorithm. Also, based on represented 9-round impossible differential characteristic, we provide a key recovery attack on reduced 10-round Zorro algorithm. In this paper, we propose a robust and different method to find impossible difference characteristics for Zorro cipher, which is independent of the linear layer of the algorithm. The main observation in this method is that the number of possible differences in that which may occur in the middle of Zorro algorithm might be very limited. This is due to the different structure of Zorro. We show how this attribute can be used to construct impossible difference characteristics. Then, using the described method, we show that, independent of the features of the algorithm elements, it is possible to achieve efficient 9-round impossible differential characteristics of Zorro cipher. It is important to note that the best impossible differential characteristics of the AES encryption algorithm are only practicable for four rounds. So the best impossible differential characteristic of Zorro cipher is far more than the best characteristic of AES, while both algorithms use an equal linear layer. Also, the analysis presented in the article, in contrast to previous analyzes, can be applied to all ciphers with the same structure as Zorro, because our analysis is independent of the internal components of the algorithm. In particular, the method presented in this paper shows that for all Zorro modified versions, there are similarly impossible differential characteristics. Zorro cipher is a block cipher algorithm with 128-bit block size and 128-bit key size. Zorro consists of 6 different sections, each with 4 rounds (24 rounds in all). Zorro does not have any subkey production algorithm and the main key is simply added to the value of the beginning state of each section using the XOR operator. Internal rounds of one section do not use the key. Similar to AES, Zorro state matrix can be shown by a 4 × 4 matrix, which each of these 16 components represent one byte. One round of Zorro, consists of four functions, which are SB*, AC, SR, and MC, respectively. The SB* function is a nonlinear function applying only to the four bytes in the first row of the state matrix. Therefore, in the opposite of the AES, where the substitution box is applied to all bytes, the Zorro substitution box only applies to four bytes. The AC operator is to add a round constant. Finally, the two SR and MC transforms are applied to the state matrix, which is, respectively, the shift row and mixed column used in the AES standard algorithm. Since the analyzes presented in this article are independent of the substitution properties, we do not use the S-box definition used by Zorro. Our proposed model uses this Zorro property that the number of possible differences after limited rounds can be much less than the total number of possible differences. In this paper, we introduce features of the Zorro, which can provide a high bound for the number of possible values of an intermediate difference. We will then present a model for how to find Zorro impossible differential characteristics, based on the limitations of the intermediate differences and using the miss-in-the-middle attack. Finally, we show that based on the proposed method, it is possible to find an impossible differential characteristic for 9 rounds of algorithms with a Zorro-like structure and regardless of the linear layer properties. Also, it is possible to apply the key recovery attack on 10 rounds of the algorithm. So, regardless of the features of the used elements, it can be shown that this number of round of algorithms is not secure even by changing the linear layer. 17 26 2018/01/32018/02/27 1396/12/8 2018/05/232019/06/19 1398/3/29 هادی سلیمانی Hadi Soleimany دانشگاه شهید بهشتی، پژوهشکده فضای مجازی ایران h_soleimany@sbu.ac.ir علیرضا مهرداد Alireza Mehrdad دانشگاه شهید بهشتی، پژوهشکده فضای مجازی ایران a.mehrdad@mail.sbu.ac.ir سعیده صادقی Saeideh Sadeghi دانشگاه شهید بهشتی، پژوهشکده فضای مجازی ایران saei.sadeghi@mail.sbu.ac.ir فرخ لقا معظمی Farokhlagha Moazemi دانشگاه شهید بهشتی، پژوهشکده فضای مجازی ایران f_moazemi@mail.sbu.ac.ir block cipher cryptanalysis impossible difference attack Zorro block cipher algorithm رمز قالبی تحلیل رمز تحلیل تفاضل ناممکن الگوریتم رمز قالبی Zorro [1] S. Mangard, E. Oswald, and T. Popp, Power analysis attacks: Revealing the secrets of smart cards vol. 31: Springer Science & Business Media, 2008.##[2] j.Sheikhzadegan, A.Vizandan,E.Mirqadri, "Cryp-tanalysis of the stream cipher HC-256' based on distinguishing attack", Signal and data processing, Vol. 7(2), pp.13-22, 1389.##[3] H. Soleimany, "Probabilistic slide cryptanalysis and its applications to LED-64 and Zorro," in International Workshop on Fast Software Encryption, pp. 373-389, 2014.##[4] J. Guo, I. Nikolic, T. Peyrin, and L. Wang, "Cryptanalysis of Zorro," IACR Cryptology ePrint Archive, vol. 2013, p. 713, 2013.##[5] S. Rasoolzadeh, Z. Ahmadian, M. Salmasizadeh, and M. R. Aref, "Total break of Zorro using linear and differential attacks," The ISC International Journal of Information Security, vol. 6, pp. 23-34, 2014##[6] Y. Wang, W. Wu, Z. Guo, and X. Yu, "Differential cryptanalysis and linear distin-guisher of full-round Zorro," in International Conference on Applied Cryptography and Network Security , pp. 308-323, 2014.##[7] G. Leander, B. Minaud, and S. Rønjom, "A Generic Approach to Invariant Subspace Attacks: Cryptanalysis of Robin, iSCREAM and Zorro," EUROCRYPT (1), vol. 9056, 2015, pp. 254-283.##[8] A. Bar-On, I. Dinur, O. Dunkelman, V. Lallemand, N. Keller, and B. Tsaban, "Cryptanalysis of SP Networks with Partial Non-Linear Layers," in EUROCRYPT (1) , pp. 315-342, 2015.##[9] N. F. Pub, "197: Advanced encryption standard (AES)," Federal information processing standards publication, vol. 197, p. 0311, 2001.##[10] B. Gérard, V. Grosso, M. Naya-Plasencia, and F.-X. Standaert, "Block ciphers that are easier to mask: How far can we go?," in International Workshop on Cryptographic Hardware and Embedded Systems, pp. 383-399, 2013.##[11] B. Bahrak and M. R. Aref, "Impossible differential attack on seven-round AES-128," IET Information Security, vol. 2 ,pp. 28-32, 2008.##[12] E. Biham, A. Biryukov, and A. Shamir, "Cryptanalysis of Skipjack reduced to 31 rounds using impossible differentials," in International Conference on the Theory and Applications of Cryptographic Techniques, pp. 12-23,1999.##[13] E. Biham, A. Biryukov, and A. Shamir, "Miss in the Middle Attacks on IDEA and Khufu," in FSE, pp. 124-138, 1999##[14] P. Derbez, P.-A. Fouque, and J. Jean, "Improved key recovery attacks on reduced-round AES in the single-key setting," in Annual International Conference on the Theory and Applications of Cryptographic Techniques , pp. 371-387, 2013.##[15] M. Shakiba, M. Dakhilalian, H. Mala, "Impossible Differential Cryptanalysis of 3D Block Cipher," in The Modares Journal of Electrical Engineering, vol.16(3), pp.24-8, 2016 Oct 1##[16] A. R. Shahmirzadi, S. A. Azimi, M. Salmasizadeh, J. Mohajeri, M. R. Aref, "Impossible Differential Cryptanalysis of Reduced-Round Midori64 Block Cipher," in ISeCure. 2018 Jan 1;10(1).##[1]S. Mangard, E. Oswald, and T. Popp, Power analysis attacks: Revealing the secrets of smart cards vol. 31: Springer Science & Business Media, 2008.## [2]ج. شیخ‌زادگان، ا. ویزندان، ع. میرقدری. "تحلیل الگوریتم رمز جریانی 'HC-256 بر اساس حمله تمایز". پردازش علائم و داده‌ها. 7 (2): 13-22. 1389.##[2] j.Sheikhzadegan, A.Vizandan,E.Mirqadri, "Cryp-tanalysis of the stream cipher HC-256' based on distinguishing attack", Signal and data processing, Vol. 7(2), pp.13-22, 1389.##[3] H. Soleimany, "Probabilistic slide cryptanalysis and its applications to LED-64 and Zorro," in International Workshop on Fast Software Encryption, pp. 373-389, 2014.##[4] J. Guo, I. Nikolic, T. Peyrin, and L. Wang, "Cryptanalysis of Zorro," IACR Cryptology ePrint Archive, vol. 2013, p. 713, 2013.##[5] S. Rasoolzadeh, Z. Ahmadian, M. Salmasizadeh, and M. R. Aref, "Total break of Zorro using linear and differential attacks," The ISC International Journal of Information Security, vol. 6, pp. 23-34, 2014##[6] Y. Wang, W. Wu, Z. Guo, and X. Yu, "Differential cryptanalysis and linear distin-guisher of full-round Zorro," in International Conference on Applied Cryptography and Network Security , pp. 308-323, 2014.##[7] G. Leander, B. Minaud, and S. Rønjom, "A Generic Approach to Invariant Subspace Attacks: Cryptanalysis of Robin, iSCREAM and Zorro," EUROCRYPT (1), vol. 9056, 2015, pp. 254-283.##[8] A. Bar-On, I. Dinur, O. Dunkelman, V. Lallemand, N. Keller, and B. Tsaban, "Cryptanalysis of SP Networks with Partial Non-Linear Layers," in EUROCRYPT (1) , pp. 315-342, 2015.##[9] N. F. Pub, "197: Advanced encryption standard (AES)," Federal information processing standards publication, vol. 197, p. 0311, 2001.##[10] B. Gérard, V. Grosso, M. Naya-Plasencia, and F.-X. Standaert, "Block ciphers that are easier to mask: How far can we go?," in International Workshop on Cryptographic Hardware and Embedded Systems, pp. 383-399, 2013.##[11] B. Bahrak and M. R. Aref, "Impossible differential attack on seven-round AES-128," IET Information Security, vol. 2 ,pp. 28-32, 2008.##[12] E. Biham, A. Biryukov, and A. Shamir, "Cryptanalysis of Skipjack reduced to 31 rounds using impossible differentials," in International Conference on the Theory and Applications of Cryptographic Techniques, pp. 12-23,1999.##[13] E. Biham, A. Biryukov, and A. Shamir, "Miss in the Middle Attacks on IDEA and Khufu," in FSE, pp. 124-138, 1999##[14] P. Derbez, P.-A. Fouque, and J. Jean, "Improved key recovery attacks on reduced-round AES in the single-key setting," in Annual International Conference on the Theory and Applications of Cryptographic Techniques , pp. 371-387, 2013.##[15] M. Shakiba, M. Dakhilalian, H. Mala, "Impossible Differential Cryptanalysis of 3D Block Cipher," in The Modares Journal of Electrical Engineering, vol.16(3), pp.24-8, 2016 Oct 1##[16] A. R. Shahmirzadi, S. A. Azimi, M. Salmasizadeh, J. Mohajeri, M. R. Aref, "Impossible Differential Cryptanalysis of Reduced-Round Midori64 Block Cipher," in ISeCure. 2018 Jan 1;10(1). ##

بهینه‌سازی در محیط‌های غیرقطعی و پیچیده پویا با روش‌های تکاملی Optimization in Uncertain and Complex Dynamic Environments with Evolutionary Methods 1 1 در دنیای واقعی بسیاری از مسائل بهینه‌­سازی، پویا، غیرقطعی و پیچیده هستند که در آن تابع هدف یا محدودیت‌­ها می‌­توانند در طول زمان تغییر یابند و در‌نتیجه، بهینه این مسائل نیز می‌­تواند تغییر کند؛ از‌این‌رو الگوریتم‌­های بهینه‌­سازی نه‌تنها باید مقدار بهینه سراسری را در فضای جستجو پیدا، بلکه باید مسیر تغییرات بهینه را در محیط پویا دنبال کنند. در این مقاله برای دست‌یابی به این توانایی الگوریتم جدیدی بر مبنای الگوریتم بهینه­‌سازی ذرات به نام الگوریتم بهینه­‌سازی ذرات افزایشی کاهشی، پیشنهاد شده است. این الگوریتم همواره در روند بهینه­‌سازی به‌طور انطباقی با کاهش یا افزایش تعداد ذرات الگوریتم، توانایی یافتن و دنبال‌کردن تعداد بهینه متغیر با زمان را در محیط‌­هایی که تغییرات آن قابل آشکارسازی نیست، دارد؛ علاوه‌بر‌این تعریف جدیدی به نام ناحیه جستجو متمرکز با هدف برجسته‌کردن فضاهای امیدبخش برای سرعت بخشیدن به فرآیند جستجوی محلی و جلوگیری از همگرایی زودرس تعریف شده است. نتایج حاصل از الگوریتم پیشنهادی بر روی معیار قله­‌های متحرک ارزیابی و با نتایج چندین الگوریتم معتبر مقایسه شده است. نتایج نشان‌دهنده تأثیر مثبت سازوکار کاهش/افزایش ذرات بر زمان یافتن و دنبال‌کردن چندین بهینه در مقایسه با سایر الگوریتم‌های بهینه­‌سازی مبتنی بر چند جمعیتی است.   2 In the real world, many of the optimization issues are dynamic, uncertain, and complex in which the objective function or constraints can be changed over time. Consequently, the optimum of these issues is changed nonlinearly. Therefore, the optimization algorithms not only should search the global optimum value in the space but also should follow the path of optimal change in dynamic environment. Accordingly, several researchers believe in the effectiveness of following a series of optimums compared to a global optimum. Therefore, when an environment is changed, following a global optimum in a series of best optimums is more efficient. Evolutionary algorithms (EA) were inspired by biological and natural evolution. Because of changing characteristic of nature, it can be a good option for dynamic optimization. In recent years, different methods have been proposed to improve EA of static environments. One of the most common methods is multi-population method. In this method, the whole space is divided into sub-spaces. Each sub-space covers some local optimums and represents a sub-population. The algorithm updates the particles of each sub-space and searches the best optimum. The most challenging issue of multi-population method is to create the desired number of sub-population and people to cover different sub-spaces in the search space. In the present study, in order to deal with the challenges, a new algorithm based on particle optimization algorithm, which is called decrement and increment particle optimization algorithm, was proposed. The algorithm is able to follow and find the number of time-varied optimum in an environment with invisible changes by increasing or decreasing the number of particles adaptively. Another challenging issue in dynamic optimization is the detection of environmental changes, due to the impossibility of this issue and failure of detection-based algorithms.  In the proposed method, there is no need to detect the environmental changes and it always adapts itself to the environment. Furthermore, the terms of focused search area were defined to emphasize on promising spaces to accelerate the local search process and prevent early convergence. The results of the proposed algorithm were evaluated on moving peaks and compared with several valid algorithms. The results showed the positive effect of decrement/increment mechanism of particles on finding and following time of many optimums compared to other multi-population based optimization algorithm. 27 44 2018/01/32018/02/272017/11/17 1396/8/26 2018/05/232019/06/192019/03/13 1397/12/22 سیدمسعود اجابتی Seyyed Masoud Ejabati دانشگاه بیرجند ایران ejabati_masoud@yahoo.com سید حمید ظهیری Seyed Hamid Zahiri دانشگاه بیرجند ایران shzahiri@yahoo.com Increase decrease particle Dynamic optimization problems (DOPs) Multi-population approach Particle swarm optimization افزایش و کاهش ذرات مسائل بهینه‌سازی پویا (DOPs) روش چندجمعیتی الگوریتم بهینه‌سازی ذرات [1] M. Mavrovouniotis, Ch. Li and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithm and application”, Swarm and Evo-lutionary Computation, Vol. 33, pp. 1-17, 2017.##[2] T. T. Nguyen, S. Yang, and J. Branke, “Evo-lutionary dynamic optimization: A survey of the state of the art”, Swarm and Evolutionary Com-putation, Vol. 6, pp. 1-24, 2012.##[3] J. Euchi, A. Yassine, H. Chabchoub, “The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach”, Swarm and Evol. Com-put, Vol. 21, pp. 41–53, 2015.##[4] Y. E. Demirtas¸, E. ¨Ozdemir, U. Demirtas¸, “A particle swarm optimization for the dynamic vehicle routing problem”, in Modeling, Simu-lation, and Applied Optimization (ICMSAO), 6th International Conference on, 2015, pp. 1–5.##[5] M. Mavrovouniotis, S. Yang, “Applying ant colony optimization to dynamic binary-encoded problem”, in A. Mora, G. Squillero (Eds.), Evo-Applications: Applications of Evolutionary Com-putation, Vol. 9028 of Lecture Notes in Computer Science, Springer International Publishing, 2015, pp. 845–856.##[6] U. Boryczka, Ł. Stra¸k, “Diversification and en-tropy improvement on the DPSO algorithm for DTSP”, in T. N. Nguyen, B. Trawi ´nski, R. Kosala (Eds.), Intelligent Information and Database Syst-ems: 7th Asian Conference, ACIIDS 2015, Bali, Indonesia, Proceedings, Part I, Springer Inter-national Publishing, Cham, 2015, pp. 337–347.##[7] M.Mavrovouniotis, S. Yang, “Ant colony opti-mization with self-adaptive evaporation rate in dy-namic environments”, in IEEE Symposium on Computational Intelligence in Dynamic and Un-certain Environments (CIDUE), pp. 47–54, 2014.##[8] S. Gao, Y. Wang, J. Cheng, Y. Inazumi, Z. Tang, “Ant colony optimization with clustering for solving the dynamic location routing problem”, Appl. Math. Comput., Vol. 285, pp. 149–173, 2016.##[9] M. Mavrovouniotis, S. Yang, “Ant colony optimization with memorybased immigrants for the dynamic vehicle routing problem”, in 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 2645– 2652, 2012.##[10] M. Mavrovouniotis, S. Yang, “Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors”, Appl. Soft Comput., Vol. 13, pp. 4023–4037, 2013.##[11] M. Mavrovouniotis, S. Yang, X. Yao, “Multi-colony ant algorithms for the dynamic travelling salesman problem”, in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 9–16, 2014.##[12] B. van Veen, M. Emmerich, Z. Yang, T. B¨ack, J. Kok, “Ant colony algorithms for the dynamic vehicle routing problem with time windows”, in J. M. Ferr´andez Vicente, J. R. ´Alvarez S´anchez, F. de la Paz L´opez, F. J. Toledo Moreo (Eds.), Natural and Artificial Computation in Engineering and Medical Applications: 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, Proceedings, Part II, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 1–10.##[13] Z. Yang, M. Emmerich, T. B¨ack, “Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities”, in 2015 IEEE Congress on Evolutionary Computation (CEC), 2015, pp. 2813– 2819.##[14] L. Melo, F. Pereira, E. Costa, “Multi-caste ant colony algorithm for the dynamic traveling salesperson problem”, in M. Tomassini, A. Antonioni, F. Daolio, P. Buesser (Eds.), Adaptive and Natural Computing Algorithms, Vol. 7824 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2013, pp. 179–188.##[15] L. Melo, F. Pereira, E. Costa, “Extended experiments with ant colony optimization with heterogeneous ants for large dynamic traveling salesperson problems”, in 14th International Conference on Computational Science and Its Applications (ICCSA), 2014, pp. 171–175.##[16] U. Boryczka, Ł. Stra¸k, “Heterogeneous DPSO algorithm for DTSP”, in M. N´u˜nez, T. N. Nguyen, D. Camacho, B. Trawi ´nski (Eds.), Computational Collective Intelligence: 7th International Conference, ICCCI 2015, Madrid, Spain, Proceedings, Part II, Springer Inter-national Publishing, Cham, 2015, pp. 119–128.##[17] M. Okulewicz, J. Ma´ndziuk, “Two-phase multi-swarm PSO and the dynamic vehicle routing problem”, in IEEE Symposium on Computational Intelligence for Human-like Intelligence (CI-HLI), pp. 1–8, 2014.##[18] M. Mavrovouniotis, S. Yang, “Dynamic vehicle routing: A memetic ant colony optimization approach”, in A. Uyar, E. Ozcan, N. Urquhart (Eds.), Automated Scheduling and Planning, Vol. 505 of Studies in Computational Intelligence, Springer Berlin Heidelberg, 2013, pp. 283– 301.##[19] M. Mavrovouniotis, F. M. M¨uller, S. Yang, “Ant colony optimization with local search for the dynamic travelling salesman problems”, IEEE Trans. Cybern, Vol. 99, pp. 1–14, 2016.##[20] L. Liu and S. R. Ranjithan, “An adaptive optimization technique for dynamic envir-onments”, Eng. Appl. Artif. Intell., Vol. 23, No. 5, pp. 772– 779, 2010.##[21] D. Parrott and X. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation”, IEEE Trans. Evol. Com-put., Vol. 10, No. 4, pp. 440–458, 2006.##[22] S. Yang and C. Li, “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments”, IEEE Trans. Evol. Comput., Vol. 14, No. 6, pp. 959–974, 2010.##[23] C. Li and S. Yang, “A clustering particle swarm optimizer for dynamic optimization”, in Congr. Evol. Comput., pp. 439–446, 2009.##[24] J. Branke, The Moving Peaks Benchmark, 1999.Available:http://web.archive.org/web/20130906140931/http:/people.aifb.kit.edu/jbr/MovPeaks/##[25] J. Branke, “Memory enhanced evolutionary algorithms for changing optimization prob-lems”, in Congr. Evol. Comput., Vol. 3, 1999, pp. 1875–1882.##[26] J. Yaochu and J. Branke, “Evolutionary optimization in uncertain environments-a survey”, IEEE Transactions on Evolutionary Computation, Vol. 9, pp. 303-317, 2005.##[27] M. Khouadjia, E. Alba, L. Jourdan, E.-G. Talbi, “Multi-swarm optimization for dy-namic combinatorial problems: A case study on dynamic vehicle routing problem”, in M. Dorigo, M. Birattari, G. Di Caro, R. Doursat, A. Engelbrecht, D. Floreano, L. Gambardella, R. Groß, E. S¸ ahin, H. Sayama, T. St ¨utzle (Eds.), Swarm Intelligence, Vol. 6234 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2010, pp. 227– 238.##[28] J. Branke, T. Kaußler, C. Schmidth, and H. Schmeck, “A multipopulation approach to dynamic optimization problem”, in 4th Int. Conf. Adaptive Comput. Des. Manuf., 2000 pp. 299–308.##[29] C. Li and S. Yang, “Fast multi-swarm optimization for dynamic optimization prob-lems”, in 4th Int. Conf. Natural Comput., Vol. 7, 2008, pp. 624–628.##[30] M. Kamosi, A. B. Hashemi, and M. R. Meybodi, “A hibernating multiswarm opti-mization algorithm for dynamic environ-ments”, in World Congr. on Nature and Biologically Inspired Computing, NaBIC-2010, 2010, pp. 363–369.##[31] T. M. Blackwell and J. Branke, “Multi-swarm optimization in dynamic environments”, in EvoWorkshops 2004: Appl. Evol. Comput., LNCS 3005, 2004, pp. 489–500.##[32] T. M. Blackwell and J. Branke, “Multi-swarms, exclusion, and anticonvergence in dynamic environments”, IEEE Trans. Evol. Comput., Vol. 10, No. 4, pp. 459–472, 2006.##[33] I. del Amo, D. Pelta, Gonz´aez, and J. lez, “Using heuristic rules to enhance a multi-swarm pso for dynamic environments”, in 2010 Congr. Evol. Comput., 2010, pp. 1–8.##[34] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, and M. R. Meybodi, “A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization”, Applied Soft Computing, Vol. 13, pp. 2144–2158, 2013.##[35] A. Boulesnane, S. Meshoul, "WD2O: a novel wind driven dynamic optimization approach with effective change detection," Applied Intelligence, Vol. 47, pp. 488–504, September 2017.##[36] J. Kazemi Kordestani, H. Abedi Firouzjaee, M. R. Meybodi, "An adaptive bi-flight cuckoo search with variable nests for cont-inuous dynamic optimization problems," Applied Intelligence, Vol. 48, pp. 97–117, January 2018.##[37] N. Fouladgar, S. Lotfi, "A novel approach for optimization in dynamic environments based on modified cuckoo search algorithm," Soft Comput, pp 1–15, 2015.##[38] C. Li, S. Yang, “A general framework of multipopulation methods with clustering in undetectable dynamic environments”, IEEE Trans. Evol. Comput, Vol. 16, No. 4, pp. 556–577, 2012.##[39] R. I. Lung and D. Dumitrescu, “Evolutionary swarm cooperative optimization in dynamic environments”, Natural Comput., Nol. 9, No. 1, pp. 83–94, 2010.##[40] H. Richter, “Detecting change in dynamic fitness landscapes”, in 2009 Congr. Evol. Comput., 2009, pp. 1613–1620.##[41] A. Simoes and E. Costa, “Evolutionary algo-rithms for dynamic environments: prediction using linear regression and markov chains”, in Parallel Problem Solving from Nature, pp. 306–315, 2008.##[42] R. C. Eberhart and J. Kennedy, “A new opti-mizer using particle swarm theory”, in Proc. 6th Int. Symp. Micro Mach. Human Sci., 1995, pp. 39–43.##[43] J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, in Proc. IEEE Int. Conf. Neural Netw., 1995, pp. 1942–1948.##[44] Y. Shi, R. Eberhart, “A modified particle swarm optimizer”, in Proc. IEEE Conf. Evol. Comput., 1998, pp. 69–73.##[45] R. I. Lung and D. Dumitrescu, “A colla-borative model for tracking optima in dy-namic environments”, in Proc. Congr. Evol. Comput., 2007, pp. 564–567.##[46] S. Bird and X. Li, “Using regression to improve local convergence”, in Proc. IEEE Congr. Evol. Comput., 2007, pp. 592–599.##[47] T. Blackwell, J. Branke, and X. Li, “Particle swarms for dynamic optimization problems", Swarm Intelligence, pp. 193-217, 2008.##[48] M. Kamosi, A. Hashemi, and M. Meybodi, “A New Particle Swarm Optimization Algo-rithm for Dynamic Environments", Swarm Evolutionary and Memetic Com-puting, pp. 129-138, 2010.##[49] I. Rezazadeh, M. Meybodi, and A. Naebi, “Adaptive particle swarm optimization algo-rithm for dynamic environments”, Ad-vances in Swarm Intelligence, pp. 120-129, 2011.##[50] B. Nasiri and M. Meybodi, “Speciation based firefly algorithm for optimization in dynamic environments”, International Journal of Artificial Intelligence, Vol. 8, pp. 118-132, 2012.##[51] D. Yazdani, B. Nasiri, R. Azizi, A. Sepas-Moghaddam, and M. R. Meybodi, “Opti-mization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization”, International Journal of Artificial Intelligence, Vol. 11, pp. 170-192, 2013.##[52] J. K. Kordestani, A. Rezvanian, and M. R. Meybodi, “CDEPSO: a bi-population hybrid approach for dynamic optimization prob-lems”, Applied intelligence, Vol. 40, pp. 682-694, 2014.##[53] R. Mukherjee, G. R. Patra, R. Kundu, and S. Das, “Cluster-based differential evolution with Crowding Archive for niching in dy-namic environments”, Information Sciences, Vol. 267, pp. 58- 82, 2014.##[54] M. Mohammadpour, H. Parvin, M. Sina, "Chaotic Genetic Algorithm based on Ex-plicit Memory with a new Strategy for Updating and Retrieval of Memory in Dy-namic Environments," AI and Data Mining, Vol. 6, pp. 191-205, Winter and Spring 2018.##[55] E. Wang, Z. Bovik, A. Sheik, H. Simoncelli, "Image quality assessment: from error visi-bility to structural similarity", IEEE Trans-actions on Image processing, 2004.##[1] M. Mavrovouniotis, Ch. Li and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithm and application”, Swarm and Evo-lutionary Computation, Vol. 33, pp. 1-17, 2017.##[2] T. T. Nguyen, S. Yang, and J. Branke, “Evo-lutionary dynamic optimization: A survey of the state of the art”, Swarm and Evolutionary Com-putation, Vol. 6, pp. 1-24, 2012.##[3] J. Euchi, A. Yassine, H. Chabchoub, “The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach”, Swarm and Evol. Com-put, Vol. 21, pp. 41–53, 2015.##[4] Y. E. Demirtas¸, E. ¨Ozdemir, U. Demirtas¸, “A particle swarm optimization for the dynamic vehicle routing problem”, in Modeling, Simu-lation, and Applied Optimization (ICMSAO), 6th International Conference on, 2015, pp. 1–5.##[5] M. Mavrovouniotis, S. Yang, “Applying ant colony optimization to dynamic binary-encoded problem”, in A. Mora, G. Squillero (Eds.), Evo-Applications: Applications of Evolutionary Com-putation, Vol. 9028 of Lecture Notes in Computer Science, Springer International Publishing, 2015, pp. 845–856.##[6] U. Boryczka, Ł. Stra¸k, “Diversification and en-tropy improvement on the DPSO algorithm for DTSP”, in T. N. Nguyen, B. Trawi ´nski, R. Kosala (Eds.), Intelligent Information and Database Syst-ems: 7th Asian Conference, ACIIDS 2015, Bali, Indonesia, Proceedings, Part I, Springer Inter-national Publishing, Cham, 2015, pp. 337–347.##[7] M.Mavrovouniotis, S. Yang, “Ant colony opti-mization with self-adaptive evaporation rate in dy-namic environments”, in IEEE Symposium on Computational Intelligence in Dynamic and Un-certain Environments (CIDUE), pp. 47–54, 2014.##[8] S. Gao, Y. Wang, J. Cheng, Y. Inazumi, Z. Tang, “Ant colony optimization with clustering for solving the dynamic location routing problem”, Appl. Math. Comput., Vol. 285, pp. 149–173, 2016.##[9] M. Mavrovouniotis, S. Yang, “Ant colony optimization with memorybased immigrants for the dynamic vehicle routing problem”, in 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 2645– 2652, 2012.##[10] M. Mavrovouniotis, S. Yang, “Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors”, Appl. Soft Comput., Vol. 13, pp. 4023–4037, 2013.##[11] M. Mavrovouniotis, S. Yang, X. Yao, “Multi-colony ant algorithms for the dynamic travelling salesman problem”, in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 9–16, 2014.##[12] B. van Veen, M. Emmerich, Z. Yang, T. B¨ack, J. Kok, “Ant colony algorithms for the dynamic vehicle routing problem with time windows”, in J. M. Ferr´andez Vicente, J. R. ´Alvarez S´anchez, F. de la Paz L´opez, F. J. Toledo Moreo (Eds.), Natural and Artificial Computation in Engineering and Medical Applications: 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, Proceedings, Part II, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 1–10.##[13] Z. Yang, M. Emmerich, T. B¨ack, “Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities”, in 2015 IEEE Congress on Evolutionary Computation (CEC), 2015, pp. 2813– 2819.##[14] L. Melo, F. Pereira, E. Costa, “Multi-caste ant colony algorithm for the dynamic traveling salesperson problem”, in M. Tomassini, A. Antonioni, F. Daolio, P. Buesser (Eds.), Adaptive and Natural Computing Algorithms, Vol. 7824 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2013, pp. 179–188.##[15] L. Melo, F. Pereira, E. Costa, “Extended experiments with ant colony optimization with heterogeneous ants for large dynamic traveling salesperson problems”, in 14th International Conference on Computational Science and Its Applications (ICCSA), 2014, pp. 171–175.##[16] U. Boryczka, Ł. Stra¸k, “Heterogeneous DPSO algorithm for DTSP”, in M. N´u˜nez, T. N. Nguyen, D. Camacho, B. Trawi ´nski (Eds.), Computational Collective Intelligence: 7th International Conference, ICCCI 2015, Madrid, Spain, Proceedings, Part II, Springer Inter-national Publishing, Cham, 2015, pp. 119–128.##[17] M. Okulewicz, J. Ma´ndziuk, “Two-phase multi-swarm PSO and the dynamic vehicle routing problem”, in IEEE Symposium on Computational Intelligence for Human-like Intelligence (CI-HLI), pp. 1–8, 2014.##[18] M. Mavrovouniotis, S. Yang, “Dynamic vehicle routing: A memetic ant colony optimization approach”, in A. Uyar, E. Ozcan, N. Urquhart (Eds.), Automated Scheduling and Planning, Vol. 505 of Studies in Computational Intelligence, Springer Berlin Heidelberg, 2013, pp. 283– 301.##[19] M. Mavrovouniotis, F. M. M¨uller, S. Yang, “Ant colony optimization with local search for the dynamic travelling salesman problems”, IEEE Trans. Cybern, Vol. 99, pp. 1–14, 2016.##[20] L. Liu and S. R. Ranjithan, “An adaptive optimization technique for dynamic envir-onments”, Eng. Appl. Artif. Intell., Vol. 23, No. 5, pp. 772– 779, 2010.##[21] D. Parrott and X. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation”, IEEE Trans. Evol. Com-put., Vol. 10, No. 4, pp. 440–458, 2006.##[22] S. Yang and C. Li, “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments”, IEEE Trans. Evol. Comput., Vol. 14, No. 6, pp. 959–974, 2010.##[23] C. Li and S. Yang, “A clustering particle swarm optimizer for dynamic optimization”, in Congr. Evol. Comput., pp. 439–446, 2009.##[24] J. Branke, The Moving Peaks Benchmark, 1999.Available:http://web.archive.org/web/20130906140931/http:/people.aifb.kit.edu/jbr/MovPeaks/##[25] J. Branke, “Memory enhanced evolutionary algorithms for changing optimization prob-lems”, in Congr. Evol. Comput., Vol. 3, 1999, pp. 1875–1882.##[26] J. Yaochu and J. Branke, “Evolutionary optimization in uncertain environments-a survey”, IEEE Transactions on Evolutionary Computation, Vol. 9, pp. 303-317, 2005.##[27] M. Khouadjia, E. Alba, L. Jourdan, E.-G. Talbi, “Multi-swarm optimization for dy-namic combinatorial problems: A case study on dynamic vehicle routing problem”, in M. Dorigo, M. Birattari, G. Di Caro, R. Doursat, A. Engelbrecht, D. Floreano, L. Gambardella, R. Groß, E. S¸ ahin, H. Sayama, T. St ¨utzle (Eds.), Swarm Intelligence, Vol. 6234 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2010, pp. 227– 238.##[28] J. Branke, T. Kaußler, C. Schmidth, and H. Schmeck, “A multipopulation approach to dynamic optimization problem”, in 4th Int. Conf. Adaptive Comput. Des. Manuf., 2000 pp. 299–308.##[29] C. Li and S. Yang, “Fast multi-swarm optimization for dynamic optimization prob-lems”, in 4th Int. Conf. Natural Comput., Vol. 7, 2008, pp. 624–628.##[30] M. Kamosi, A. B. Hashemi, and M. R. Meybodi, “A hibernating multiswarm opti-mization algorithm for dynamic environ-ments”, in World Congr. on Nature and Biologically Inspired Computing, NaBIC-2010, 2010, pp. 363–369.##[31] T. M. Blackwell and J. Branke, “Multi-swarm optimization in dynamic environments”, in EvoWorkshops 2004: Appl. Evol. Comput., LNCS 3005, 2004, pp. 489–500.##[32] T. M. Blackwell and J. Branke, “Multi-swarms, exclusion, and anticonvergence in dynamic environments”, IEEE Trans. Evol. Comput., Vol. 10, No. 4, pp. 459–472, 2006.##[33] I. del Amo, D. Pelta, Gonz´aez, and J. lez, “Using heuristic rules to enhance a multi-swarm pso for dynamic environments”, in 2010 Congr. Evol. Comput., 2010, pp. 1–8.##[34] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, and M. R. Meybodi, “A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization”, Applied Soft Computing, Vol. 13, pp. 2144–2158, 2013.##[35] A. Boulesnane, S. Meshoul, "WD2O: a novel wind driven dynamic optimization approach with effective change detection," Applied Intelligence, Vol. 47, pp. 488–504, September 2017.##[36] J. Kazemi Kordestani, H. Abedi Firouzjaee, M. R. Meybodi, "An adaptive bi-flight cuckoo search with variable nests for cont-inuous dynamic optimization problems," Applied Intelligence, Vol. 48, pp. 97–117, January 2018.##[37] N. Fouladgar, S. Lotfi, "A novel approach for optimization in dynamic environments based on modified cuckoo search algorithm," Soft Comput, pp 1–15, 2015.##[38] C. Li, S. Yang, “A general framework of multipopulation methods with clustering in undetectable dynamic environments”, IEEE Trans. Evol. Comput, Vol. 16, No. 4, pp. 556–577, 2012.##[39] R. I. Lung and D. Dumitrescu, “Evolutionary swarm cooperative optimization in dynamic environments”, Natural Comput., Nol. 9, No. 1, pp. 83–94, 2010.##[40] H. Richter, “Detecting change in dynamic fitness landscapes”, in 2009 Congr. Evol. Comput., 2009, pp. 1613–1620.##[41] A. Simoes and E. Costa, “Evolutionary algo-rithms for dynamic environments: prediction using linear regression and markov chains”, in Parallel Problem Solving from Nature, pp. 306–315, 2008.##[42] R. C. Eberhart and J. Kennedy, “A new opti-mizer using particle swarm theory”, in Proc. 6th Int. Symp. Micro Mach. Human Sci., 1995, pp. 39–43.##[43] J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, in Proc. IEEE Int. Conf. Neural Netw., 1995, pp. 1942–1948.##[44] Y. Shi, R. Eberhart, “A modified particle swarm optimizer”, in Proc. IEEE Conf. Evol. Comput., 1998, pp. 69–73.##[45] R. I. Lung and D. Dumitrescu, “A colla-borative model for tracking optima in dy-namic environments”, in Proc. Congr. Evol. Comput., 2007, pp. 564–567.##[46] S. Bird and X. Li, “Using regression to improve local convergence”, in Proc. IEEE Congr. Evol. Comput., 2007, pp. 592–599.##[47] T. Blackwell, J. Branke, and X. Li, “Particle swarms for dynamic optimization problems", Swarm Intelligence, pp. 193-217, 2008.##[48] M. Kamosi, A. Hashemi, and M. Meybodi, “A New Particle Swarm Optimization Algo-rithm for Dynamic Environments", Swarm Evolutionary and Memetic Com-puting, pp. 129-138, 2010.##[49] I. Rezazadeh, M. Meybodi, and A. Naebi, “Adaptive particle swarm optimization algo-rithm for dynamic environments”, Ad-vances in Swarm Intelligence, pp. 120-129, 2011.##[50] B. Nasiri and M. Meybodi, “Speciation based firefly algorithm for optimization in dynamic environments”, International Journal of Artificial Intelligence, Vol. 8, pp. 118-132, 2012.##[51] D. Yazdani, B. Nasiri, R. Azizi, A. Sepas-Moghaddam, and M. R. Meybodi, “Opti-mization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization”, International Journal of Artificial Intelligence, Vol. 11, pp. 170-192, 2013.##[52] J. K. Kordestani, A. Rezvanian, and M. R. Meybodi, “CDEPSO: a bi-population hybrid approach for dynamic optimization prob-lems”, Applied intelligence, Vol. 40, pp. 682-694, 2014.##[53] R. Mukherjee, G. R. Patra, R. Kundu, and S. Das, “Cluster-based differential evolution with Crowding Archive for niching in dy-namic environments”, Information Sciences, Vol. 267, pp. 58- 82, 2014.##[54] M. Mohammadpour, H. Parvin, M. Sina, "Chaotic Genetic Algorithm based on Ex-plicit Memory with a new Strategy for Updating and Retrieval of Memory in Dy-namic Environments," AI and Data Mining, Vol. 6, pp. 191-205, Winter and Spring 2018.##[55] E. Wang, Z. Bovik, A. Sheik, H. Simoncelli, "Image quality assessment: from error visi-bility to structural similarity", IEEE Trans-actions on Image processing, 2004. ##

ارائه یک مدل پیش‌بینی یال مبتنی بر شباهت ساختاری و هوموفیلی در شبکه‌های اجتماعی Providing a Link Prediction Model based on Structural and Homophily Similarity in Social Networks 1 1 در سال‌های اخیرشبکه‌های اجتماعی مجازی روز به روز در حال رشد و تغییر هستند. یال‌های جدید نشان‌دهنده تعاملات میان گره‌ها هستند و پیش‌بینی آن‌ها از اهمیت بالایی برخوردار است. معیارهای پیش‌بینی یال را می‌توان به دو گروه مبتنی بر همسایگی گره و مبتنی بر پیمایش مسیر تقسیم کرد. پژوهش‌گران ایجاد یال جدید در شبکه را از منظر نظری به دو علت نزدیکی در گراف و هوموفیلی نسبت می‌دهند. با وجود مطالعات بسیار در حوزه علوم شبکه مطالعه تأثیر دو رویکرد نظری در کنار یکدیگر در ایجاد یال‌ها مسئله‌ای باز محسوب می‌شود و تاکنون معیارهای شباهت مبتنی بر همسایگی گره از این منظر مطالعه نشده‌اند. در این پژوهش مدلی ارائه کردیم تا با استفاده از آن از مزایای هر دو رویکرد نزدیکی در گراف و هوموفیلی استفاده کنیم و با استفاده از آن توانستیم بر دقت معیارهای شباهت مبتنی بر همسایگی گره بیفزاییم. برای ارزیابی این پژوهش از دو مجموعه‌داده شبکه اجتماعی مجازی زنجان و شبکه اجتماعی مجازی پوکک استفاده شده است که مجموعه‌داده نخست برای این پژوهش جمع‌آوری و سپس تکمیل شده است.   2 In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Most online social networks are growing and changing with new communications (new edges). Forecasting new edges in online social networks can give us a better understanding of the growth of these networks. Link prediction has many important applications. These include predicting future social networking interactions, the ability to manage and design useful organizational communications, and predicting and preventing relationships in terrorist gangs. There have been many studies of link prediction in the field of engineering and humanities. Scientists attribute the existence of a new relationship between two individuals for two reasons: 1) Proximity to the graph (structure) 2) Similar properties of the two individuals (Homophile law). Based on the two approaches mentioned, many studies have been carried out and the researchers have presented different similarity metrics for each category. However, studying the impact of the two approaches working together to create new edges remains an open problem. Similarity metrics can also be divided into two categories; Neighborhood-based and path-based. Neighborhood-based metrics have the advantage that they do not need to access the whole graph to compute, whereas the whole graph must be available at the same time to calculate path-based metrics. So far, above the two theoretical approaches (proximity and homophile) have not been found together in the neighborhood-based metrics. In this paper, we first attempt to provide a solution to determine importance of the proximity to the graph and similar features in the connectivity of the graphs. Then obtained weights are assigned to both proximity and homophile. Then the best similarity metric in each approach are obtained. Finally, the selected metric of homophily similarity and structural similarity are combined with the obtained weights. The results of this study were evaluated on two datasets; Zanjan University Graduate School of Social Sciences and Pokec online Social Network. The first data set was collected for this study and then the questionnaires and data collection methods were filled out. Since this dataset is one of the few Iranian datasets that has been compiled with its users' specifications, it can be of great value. In this paper, we have been able to increase the accuracy of Neighborhood-based similarity metric by using two proximity in graph and homophily approaches.   45 58 2018/01/32018/02/272017/11/172018/04/23 1397/2/3 2018/05/232019/06/192019/03/132019/07/10 1398/4/19 علیرضا اسحاقپور Alireza Eshaghpoor دانشگاه تهران ایران a_eshaghpoor@ut.ac.ir مصطفی صالحی Mostafa Salehi دانشگاه تهران ایران mostafa_salehi@ut.ac.ir وحید رنجبر Vahid Ranjbar دانشگاه یزد ایران vranjbar@yazd.ac.ir Link prediction Homophily similarity Network similarity Social networks پیش‌بینی یال شباهت هوموفیلی شباهت ساختاری شبکه‌های اجتماعی [1] d. boyd and N. B. Ellison, "Social network sites: Definition, history, and scholarship," Journal of Computer‐Mediated Communi-cation, vol. 13, no. 1, pp. 210-230, 2007.##[2] H. Gangadharbatla, "Facebook me: Collec-tive self-esteem, need to belong, and internet self-efficacy as predictors of the iGenera-tion’s attitudes toward social networking sites," Journal of interactive advertising, vol. 8, no. 2, pp. 5-15, 2008.##[3] M. E. J. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, no. 2, p. 167–256, 2003.##[4] V. Ranjbar, M. Salehi, P. Jandaghi, and M. Jalili. "QANet: Tensor Decomposition App-roach for Query-based Anomaly Detection in Heterogeneous Information Networks." IEEE Transactions on Knowledge and Data Engi-neering, 31(11), pp.2178-2189, 2019.##[5] R. Albert and A.-L. Barabasi, "Statistical mechanics of complex networks," Reviews of Modern Physics, vol. 74, no. 47, January 2002.##[6] C. Zhong, M. Salehi, S. Shah, M. Cobzarenco, N. Sastry and M. Cha, "Social bootstrapping: how pinterest and last. fm social communities benefit by borrowing links from facebook," in Proceedings of the 23rd international con-ference on World wide web, 2014.##[7] S. N. Dorogovtsev and J. F. F. Mendes, Evolution of networks: From biological nets to the Internet and WWW, Oxford University Press, 2003.##[8] J. Leskovec and L. Backstrom, "Supervised random walks: predicting and recommending links in social networks," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011.##[9] M. McPherson, L. Smith-Lovin and J. M Cook, "Birds of a Feather Homophily in Social Networks," jstor, vol. 27, pp. 415-444, 2001.##[10] A. Eshaghpour, M. Salehi and H. Abdollahyan, "Homophily in Virtual Social Networks: A Case Study of Virtual Social Network in Graduate University of Zanjan," New media studies, vol. 2, no. 6, pp. 93-120, 2016.##[11] S. Boriah, V. Chandola and V. Kumar, "Simi-larity measures for categorical data: A com-parative evaluation," Red, vol. 30, no. 2, pp. 3, 2008.##[12] C. G. Akcora, B. Carminati and E. Ferrari, "User similarities on social networks," Social Network Analysis and Mining, vol. 3, no. 3, pp. 475-495, 2013.##[13] Hosseini M, Nasrollahi M, Baghaei A. “A hybrid recommender system using trust and bi-clustering in order to increase the effi-ciency of collaborative filtering”. JSDP.; vol.15 (2) . pp.119-132, 2018.##[14] L. Lü and T. Zhou, "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, pp. 1150-1170, 2011.##[15] C. S. a. D. Waltz, "Toward memory based rea-soning," ACM, vol. 29, no. 12, pp. 1213-1228, 1986.##[16] E. Eskin, A. Arnold, M. Prerau, L. Portnoy and S. Stolfo, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data," Applications of data mining in computer security, vol. 6, pp. 77-102, 2002.##[17] D. W. Goodall, "A New Similarity Index Bas-ed on Probability," Biometrics, vol. 22, no. 4, pp. 882-907, 1966.##[18] T. J. Cover TM, Elements of information theory, New York: Wiley-Interscience, 1991.##[19] P. Jaccard, Etude de la distribution florale dans une portion des Alpes et du Jura, Nature-lles: Bulletin de la Societe Vaudoise des Sciences Naturelles, 1901.##[20] E. A. L. A. Adamic, "Friends and neighbors on the Web," Social Networks, vol. 25, no. 3, pp. 211-230, 2003.##[21] M. Jalili, Y. Orouskhani, M. Asgari, N. Alipourfard and M. Perc, "Link prediction in multiplex online social networks," Royal Society Open Science, vol. 4, no. 2, 2017.##[22] Z. Wu, Y. Lin and J. Wang, "Link prediction with node clustering coefficient," Physica A: Statistical Mechanics and its Applications, vol. 452, pp. 1-8, 2016.##[23] Sh .Najari, M. Salehi, V. Ranjbar, and M. Jalili. "Link prediction in multiplex networks based on interlayer similarity." Physica A: Statistical Mechanics and its Applications, 2019..##[24] M. Deshpande and G. Karypis, "Item-based top-n recommendation algorithms," ACM Transactions on Information Systems (TOIS), vol. 22, no. 1, pp. 143-177, 2004.##[25] T. Zhou, L. Lü and Y.-C. Zhang, "Predicting missing links via local information," The European Physical Journal B-Condensed Matter and Complex Systems, vol. 71, no. 4, pp. 623-630, 2009.##[26] H. Shakibian and N. Moghadam Charkari, "Mutual information model for link prediction in heterogeneous complex networks," Scien-tific Reports, vol. 7, 2017.##[27] H. Zhao, Q. Yao, J. Li, Y. Song and D. Lee, "Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks," in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining, 2017.##[28] X. Yang, T. Deng, Z. Guo and . Z. Ding, "Advertising Keyword Recommendation bas-ed on Supervised Link Prediction in Multi-Relational Network," in Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, 2017.##[29] M. E. J.33 Newman, Networks An Intro-duction, New York: Oxford University Press Inc, 2010.##[30] R. Milo , S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii and U. Alon, "Net-work motifs: simple building blocks of complex networks," Science, vol. 298, no. 5594, pp. 824-827, 2002.##[31] L. Takac and M. Zabovsky, "Data Analysis in Public Social Networks," in International Scientific Conference and International Work-shop Present Day Trends of Innovations, 2012##[32] K.-L. Goh, B. Kahng and D. Kim, "Universal Behavior of Load Distribution in Scale-Free Networks," Physical Review Letters, vol. 87, no. 27, 2001.##[33] M. McPherso, L. Smith-Lovin and J. M Cook, "Classification of scale free networks," Pro-ceedings of the National Academy of Sciences of the United States of America, vol. 99, 2002.##[34] J. A Smith, M. McPherson and L. Smith-Lovin, "Social Distance in the United States Sex, Race, Religion, Age, and Education Homophily among Confidants, 1985 to 2004," American Sociological Review, vol. 79, no. 3, pp. 432-456, 1 6 2014.##[35] X. Han, "Mining user similarity in online social networks: analysis, modeling and appli-cations," 2015.##[36] J. Hyung Kang and K. Lerman, "Using Lists to Measure Homophily on Twitter," in Work-shops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.##[1] d. boyd and N. B. Ellison, "Social network sites: Definition, history, and scholarship," Journal of Computer‐Mediated Communi-cation, vol. 13, no. 1, pp. 210-230, 2007.##[2] H. Gangadharbatla, "Facebook me: Collec-tive self-esteem, need to belong, and internet self-efficacy as predictors of the iGenera-tion’s attitudes toward social networking sites," Journal of interactive advertising, vol. 8, no. 2, pp. 5-15, 2008.##[3] M. E. J. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, no. 2, p. 167–256, 2003.##[4] V. Ranjbar, M. Salehi, P. Jandaghi, and M. Jalili. "QANet: Tensor Decomposition App-roach for Query-based Anomaly Detection in Heterogeneous Information Networks." IEEE Transactions on Knowledge and Data Engi-neering, 31(11), pp.2178-2189, 2019.##[5] R. Albert and A.-L. Barabasi, "Statistical mechanics of complex networks," Reviews of Modern Physics, vol. 74, no. 47, January 2002.##[6] C. Zhong, M. Salehi, S. Shah, M. Cobzarenco, N. Sastry and M. Cha, "Social bootstrapping: how pinterest and last. fm social communities benefit by borrowing links from facebook," in Proceedings of the 23rd international con-ference on World wide web, 2014.##[7] S. N. Dorogovtsev and J. F. F. Mendes, Evolution of networks: From biological nets to the Internet and WWW, Oxford University Press, 2003.##[8] J. Leskovec and L. Backstrom, "Supervised random walks: predicting and recommending links in social networks," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011.##[9] M. McPherson, L. Smith-Lovin and J. M Cook, "Birds of a Feather Homophily in Social Networks," jstor, vol. 27, pp. 415-444, 2001.##م. صالحی, ح. عبداللهیان و ع. اسحاقپور, “هوموفیلی در شبکههای اجتماعی مجازی (مطالعه موردی شبکه اجتماعی مجازی دانشگاه تحصیلات تکمیلی زنجان),” فصلنامه مطالعات رسانههای نوین, جلد 6, صفحه 91 تا 119, 1395. [10]##[10] A. Eshaghpour, M. Salehi and H. Abdollahyan, "Homophily in Virtual Social Networks: A Case Study of Virtual Social Network in Graduate University of Zanjan," New media studies, vol. 2, no. 6, pp. 93-120, 2016.##[11] S. Boriah, V. Chandola and V. Kumar, "Simi-larity measures for categorical data: A com-parative evaluation," Red, vol. 30, no. 2, pp. 3, 2008.##[12] C. G. Akcora, B. Carminati and E. Ferrari, "User similarities on social networks," Social Network Analysis and Mining, vol. 3, no. 3, pp. 475-495, 2013.##م. حسینی، م. نصرالهی، ع. بقایی، "یک سامانه توصیه‌گر ترکیبی با استفاده از اعتماد و خوشه‌بندی دوجهته به منظور افزایش کارایی پالایش گروهی"، فصل نامه پردازش علائم و داده‌ها، جلد 15، شماره 2، صفحه 119-132، 1397. [13]##[13] Hosseini M, Nasrollahi M, Baghaei A. “A hybrid recommender system using trust and bi-clustering in order to increase the effi-ciency of collaborative filtering”. JSDP.; vol.15 (2) . pp.119-132, 2018.##[14] L. Lü and T. Zhou, "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, pp. 1150-1170, 2011.##[15] C. S. a. D. Waltz, "Toward memory based rea-soning," ACM, vol. 29, no. 12, pp. 1213-1228, 1986.##[16] E. Eskin, A. Arnold, M. Prerau, L. Portnoy and S. Stolfo, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data," Applications of data mining in computer security, vol. 6, pp. 77-102, 2002.##[17] D. W. Goodall, "A New Similarity Index Bas-ed on Probability," Biometrics, vol. 22, no. 4, pp. 882-907, 1966.##[18] T. J. Cover TM, Elements of information theory, New York: Wiley-Interscience, 1991.##[19] P. Jaccard, Etude de la distribution florale dans une portion des Alpes et du Jura, Nature-lles: Bulletin de la Societe Vaudoise des Sciences Naturelles, 1901.##[20] E. A. L. A. Adamic, "Friends and neighbors on the Web," Social Networks, vol. 25, no. 3, pp. 211-230, 2003.##[21] M. Jalili, Y. Orouskhani, M. Asgari, N. Alipourfard and M. Perc, "Link prediction in multiplex online social networks," Royal Society Open Science, vol. 4, no. 2, 2017.##[22] Z. Wu, Y. Lin and J. Wang, "Link prediction with node clustering coefficient," Physica A: Statistical Mechanics and its Applications, vol. 452, pp. 1-8, 2016.##[23] Sh .Najari, M. Salehi, V. Ranjbar, and M. Jalili. "Link prediction in multiplex networks based on interlayer similarity." Physica A: Statistical Mechanics and its Applications, 2019..##[24] M. Deshpande and G. Karypis, "Item-based top-n recommendation algorithms," ACM Transactions on Information Systems (TOIS), vol. 22, no. 1, pp. 143-177, 2004.##[25] T. Zhou, L. Lü and Y.-C. Zhang, "Predicting missing links via local information," The European Physical Journal B-Condensed Matter and Complex Systems, vol. 71, no. 4, pp. 623-630, 2009.##[26] H. Shakibian and N. Moghadam Charkari, "Mutual information model for link prediction in heterogeneous complex networks," Scien-tific Reports, vol. 7, 2017.##[27] H. Zhao, Q. Yao, J. Li, Y. Song and D. Lee, "Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks," in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining, 2017.##[28] X. Yang, T. Deng, Z. Guo and . Z. Ding, "Advertising Keyword Recommendation bas-ed on Supervised Link Prediction in Multi-Relational Network," in Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, 2017.##[29] M. E. J.33 Newman, Networks An Intro-duction, New York: Oxford University Press Inc, 2010.##[30] R. Milo , S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii and U. Alon, "Net-work motifs: simple building blocks of complex networks," Science, vol. 298, no. 5594, pp. 824-827, 2002.##[31] L. Takac and M. Zabovsky, "Data Analysis in Public Social Networks," in International Scientific Conference and International Work-shop Present Day Trends of Innovations, 2012##[32] K.-L. Goh, B. Kahng and D. Kim, "Universal Behavior of Load Distribution in Scale-Free Networks," Physical Review Letters, vol. 87, no. 27, 2001.##[33] M. McPherso, L. Smith-Lovin and J. M Cook, "Classification of scale free networks," Pro-ceedings of the National Academy of Sciences of the United States of America, vol. 99, 2002.##[34] J. A Smith, M. McPherson and L. Smith-Lovin, "Social Distance in the United States Sex, Race, Religion, Age, and Education Homophily among Confidants, 1985 to 2004," American Sociological Review, vol. 79, no. 3, pp. 432-456, 1 6 2014.##[35] X. Han, "Mining user similarity in online social networks: analysis, modeling and appli-cations," 2015.##[36] J. Hyung Kang and K. Lerman, "Using Lists to Measure Homophily on Twitter," in Work-shops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012. ##

تشخیص نقاط برجسته تصاویر با استفاده از نمونه‌برداری فشرده در حوزه موجک Compressed-Sampling-Based Image Saliency Detection in the Wavelet Domain 1 1 امروزه پژوهش‌گران، از مزایای بسیار زیاد استفاده از مدل‌سازی توجه بصری انسان، در زمینه‌های مختلف، به‌صورت گسترده استفاده می‌کنند. در روش‌های مختلف ارایه‌شده در این راستا، نقشه‌هایی دو بُعدی موسوم به "نقشه نقاط برجسته" استخراج می‌شود که مقادیر نقاط مختلف در آن، بیان‌گر میزان جلب توجه بیننده به نقاط متناظر در تصویر است. در این مقاله نیز برای به‌دست‌آوردن نقشه برجستگی از ضرایب موجک تصاویر، براساس تکنیک نمونه‌برداری فشرده، نمونه‌های تصادفی انتخاب می‌شوند. در ادامه، از نمونه‌های انتخاب‌شده نقشه‌های ویژگی تولید می‌شود. با استفاده از نقشه‌های ویژگی به‌دست‌آمده، نقشه برجستگی محلی و نقشه برجستگی کلی محاسبه می‌شود. در‌نهایت، با ترکیب خطی نقشه برجستگی محلی و کلی به‌دست‌آمده، نقشه برجستگی نهایی محاسبه می‌شود. ارزیابی‌های تجربی حاکی از نتایج امیدوارکننده‌ای از برتری روش ارایه‌شده نسبت به سایر مدل‌های تشخیص برجستگی، در آشکارسازی نواحی برجسته و در عین حال در کاهش حجم محاسباتی است.   2 When watching natural scenes, an overwhelming amount of information is delivered to the Human Visual System (HVS). The optic nerve is estimated to receive around 108 bits of information a second. This large amount of information can’t be processed right away through our neural system. Visual attention mechanism enables HVS to spend neural resources efficiently, only on the selected parts of the scene at order. This results in a better and faster perception of events. In order to perform saliency measurement on visual data, subjective eye-tracking experiments may be carried out. These experiments involve using devices to track eye movements of a number of subjects while they watch images or videos on a screen. That being said, such devices are not very suitable in practice due to hardship involved with carrying out experiments, such as need to have restricted test environment, being time consuming as well as expensive. Instead, researchers developed Computational Visual Attention Models (VAMs) in attempts to mimic the HVS saliency prediction process. Visual Attention Modelling has widely been used in various areas of image processing and understanding. Computational models of visual attention aim to predict the most interesting areas of an image to the observers. To this end, these models produce saliency maps, in which each pixel is assigned a likelihood value of being looked at. In other words, saliency maps highlight where the most likely for viewers  to look at in an image is. Knowing the Regions of Interests (ROIs) can be helpful in applications such as image and video compression, object recognition and detection, visual search, retargeting, retrieval, image matching, and segmentation. Saliency prediction is generally done in a bottom-up, top-down, or hybrid fashion. Bottom-up approaches exploit low-level attributes such as brightness, color, edges, texture, etc. Top-down approaches focus on context-dependent information from the scene such as appearance of humans, animals, text, etc. Hybrid methods combine the two streams. This paper proposes a new method of saliency prediction using sparse wavelet coefficients selected from low-level bottom-up saliency features. Wavelet based image methods are used widely in image processing algorithms as they are especially powerful in decomposing images into several scales of resolutions. In our method, first random compressive sampling is performed on wavelet coefficients in the Lab color space. Random sampling enables a reduction in computational complexity and provides a sparse representation of the coefficients. The number of decomposition levels is chosen based on the information diffusion property of the signal. In the proposed method, the sampling can be done at a rate different than the Nyquist rate, and based on the sparsity degree of the signal. It is shown that having the basis vectors of a sparse representation of the signal, can result in an accurate signal reconstruction. In this work, the sparsity degree and thus the sampling rate is computed empirically. Next, local and global saliency maps are generated from these random samples to account for small-scale and large-scale (scene-wide) saliency attributes. These maps are then combined to form an overall saliency map. The overall saliency map therefore includes both local, and global saliency attributes. The main contribution of this paper is the use of compressive sampling in creating a novel wavelet domain representation for image saliency prediction. Extensive performance evaluations show that the proposed method provides a promising saliency prediction performance while the computation complexity remains reasonable, thanks to the dimensionality reduction of compressive sampling. In particular, the proposed method demonstrated favorable precision, recall, and F-measure, when compared to state-of-the-art saliency detection methods, over large-scale datasets. We hope the proposed approach brings ideas to the saliency analysis research community. 59 72 2018/01/32018/02/272017/11/172018/04/232018/06/13 1397/3/23 2018/05/232019/06/192019/03/132019/07/102019/02/13 1397/11/24 مهدی بنی‌طالبی دهکردی Mehdi Banitalebi-Dehkordi دانشگاه فردوسی مشهد ایران mehdi.banitalebidehkordi@mail.um.ac.ir عباس ابراهیمی‌مقدم Abbas Ebrahimi-moghadam دانشگاه فردوسی مشهد ایران a.ebrahimi@um.ac.ir مرتضی خادمی Morteza Khademi دانشگاه فردوسی مشهد ایران khademi@um.ac.ir هادی هادی‌زاده Hadi Hadizadeh دانشگاه صنعتی قوچان ایران h.hadizadeh@qiet.ac.ir Saliency map visual attention wavelet transform sparsity compressive sampling نقشه نقاط برجسته توجه بصری تبدیل موجک تُنُکی نمونه‌برداری فشرده [1] Y. Fang, W. Lin, Zh. Chen, Chia-Ming Tsai, and Chia-Wen Lin, “A Video Saliency Detection Model in Compressed Domain”, IEEE Trans. Circuits and Systems for Video Technology, vol. 24, no. 1, Jan. 2014.##[2] S. Mathe and C. Sminchisescu, “Actions in the Eye: Dynamic Gaze Datasets and Learnt Salie-ncy Models for Visual Recognition”, IEEE Trans. Pattern Analysis and Machine Intell-egence, vol. 37, no. 7, Jul 2015.##[3] S. H. Khatoonabadi, I. V. Bajc, Yufeng Shan, “Comparison of visual saliency models for compressed video”, in Proc. IEEE International Conference on Image Processing (ICIP), Jan 2015.##[4] M. Xu, L. Jiang, X. Sun, Zhaoting Ye, and Z. Wang, “Learning to Detect Video Saliency with HEVC Features”, IEEE Trans. Image Procesing, vol. 26, no. 1, Jan 2017.##[5] C. Li, Q. Tu, M. Zhao, J. Xu and A. Men, “A multiscale compressed video saliency detection model based on ant colony optimization”, in Proc. IEEE CIC/ICCC 2015 Symposium on Signal Processing for Communications, Nov. 2015.##[6] M. Jiang, X. Boix, G. Roig, J. Xu, L. V. Gool and Q. Zhao, “Learning to Predict Sequences of Human Visual Fixations”, IEEE Trans. Neural Networks and Learning Systems, vol 27, no.6, Jun 2016.##[7] H.Fayazi, H.Dehqani, and M.Hosseini,”sparse unmixing of hyper-spectral images using a pruned spectral library”, signal and data processing, no.13, pp.155-169, 1395.##[8] M,Bani Talebi.D, M.T.Sadeqi, and H.R. Aboutalebi, “New comprehensive sampling based feauture extraction method and its APPLICATION in audio signal processing”, signal and data processing, no.01,pp.57-68,1392.##[9] N. Li, J. Ye, Y. Ji, H. Ling and J. Yu, “Saliency Detection on Light Field”, IEEE Trans. Pattern Analysis and Machine Intellegence, vol. 39, no. 8, Aug 2017.##[10] S. Tajima and K. Komine, “Saliency-Based Color Accessibility”, IEEE Trans. Image Procesing, vol. 24, no. 3, Mur 2015.##[11] S. Frintrop, “VOCUS: A visual attention system for object detection and goal directed search,” Ph.D. dissertation, Rheinische Friedrich- Wil-helms-Universi¨tat Bonn, Bonn, Germany, 2005.##[12] L. Itti, “Models of bottom-up and top-down visual attention,” Ph.D. dissertation, Dept. Com-putat. Neur. Syst., California Inst. Technol, Pasadena, 2000.##[13] L. Itti, C. Koch, and E. Niebur, “Model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998.##[14] J. Harel, Ch. Koch, P. Perona, “Graph-Based Visual Saliency”, Advances in Neural Infor-mation Processing Systems, No.19. MIT Press, Cambridge, MA, pp. 545-552, 2007.##[15] T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1–8, Apr. 2007.##[16] X. Hou, J. Harel, and Ch. Koch, “Image Signature: Highlighting sparse salient regions”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 194-201, Jan 2012.##[17] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequencytuned salient region de-tection”, in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1597–1604, 2009. ##[18] L. Ye, Z. Liu, L. Li, L. Shen, C. Bai, and Yang Wang, “Salient Object Segmentation via Effec-tive Integration of Saliency and Objectness”, IEEE Trans. Multimedia, vol. 19, no. 8, pp. 1742 - 1756, Apr 2017.##[19] L. Zhang, X. Li, L. Nie, Y. Yang, and Y. Xia, “Weakly Supervised Human Fixations Predic-tion”, IEEE Trans. Cybernetics, vol. 46, no. 1, pp. 258 - 269, Jan. 2016.##[20] J. Li, L. Duan, X. Chen, T. Huang, and Y. Tian, “Finding the Secret of Image Saliency in the Frequency Domain”, IEEE Trans. Pattern Ana-lysis and Machine Intellegence, vol. 37, no. 12, pp. 2428-2440, Apr 2015.##[21] X. Hou and L. Zhang, “Saliency detection: A spectral residual approach,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1–8, Nov. 2007.##[22] C. Guo, Q. Ma, and L. Zhang, “Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recog-nition, pp. 1–8, 2008.##[23] L. Zhang, J. Chen, and B. Qiu, “Region-of-Interest Coding Based on Saliency Detection and Directional Wavelet for Remote Sensing Imag-es”, IEEE Trans. Geoscience and Remote Sen-sing Letters, vol. 14, no. 1, pp. 23-27, Jan 2017.##[24] Y. Yang, Y. Que, Sh. Huang, and P. Lin, “Multiple Visual Features Measurement with Gradient Domain Guided Filtering for Multi-sensor Image Fusion”, IEEE Trans. Instru-mentation and Measurement, vol. 66, no. 4, Apr 2017.##[25] N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga, “Saliency estimation using a non-para-metric low-level vision model,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recog-nition, 2011.##[26] X. Liu, D. Zhai, J. Zhou, X. Zhang, D. Zhao, and Wen Gao, “Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication”, IEEE Trans. Image Procesing, vol. 25, no. 6, Jun 2016.##[27] M. Aghagolzadeh, H. Radha, “Joint Estimation of Dictionary and Image from Compressive Samples”, IEEE Trans. Computational Imaging, vol. pp, no. 99, Feb 2017.##[28] N. İmamoğlu, W. Lin, Y. Fang, “A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform”, IEEE Trans. Multimedia, vol. 15, no. 1, pp. 96-106, Jan 2013.##[29] S. A. Raza Naqvi, “Image compression using haar wavelet based tetrolet transform”, in Proc. IEEE Int. Conf. Open Source Systems and Technologies (ICOSST), pp. 50-54, Jan 2014.##[30] N. D. B. Bruce, Sh. Rahman, D. Carrier, “Sparse Coding in Early Visual Representation: From Specific Properties to General Principles”, Neurocomputing, vol. 2, no. 1, pp. 1085–1098, Aug 2015.##[31] M. Banitalebi-Dehkordi, A. Banitalebi-Dehkordi, J. Abouei, K. N. Plataniotis, “Face recognition using a new compressive sensing-based feature extraction method”, Multimedia Tools and Applications, vol. 1, no.2, pp.1-21, Jul. 2017.##[32] S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 2376–2383, 2010.##[33] Microsoft Research Cambridge-12 database, available in: https://www.microsoft.com/en-us/-download/details.aspx? id= 52283& from=h-ttp%3A%2F%2Fresearch.microsoft.com%2Fenus%2Fum%2Fcambridge%2Fprojects%2Fmsrc12%2F, April, 2012.##[34] S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. London, U.K.: Academic-/Elsevier, pp. 20–24, 2009.##[1] Y. Fang, W. Lin, Zh. Chen, Chia-Ming Tsai, and Chia-Wen Lin, “A Video Saliency Detection Model in Compressed Domain”, IEEE Trans. Circuits and Systems for Video Technology, vol. 24, no. 1, Jan. 2014.##[2] S. Mathe and C. Sminchisescu, “Actions in the Eye: Dynamic Gaze Datasets and Learnt Salie-ncy Models for Visual Recognition”, IEEE Trans. Pattern Analysis and Machine Intell-egence, vol. 37, no. 7, Jul 2015.##[3] S. H. Khatoonabadi, I. V. Bajc, Yufeng Shan, “Comparison of visual saliency models for compressed video”, in Proc. IEEE International Conference on Image Processing (ICIP), Jan 2015.##[4] M. Xu, L. Jiang, X. Sun, Zhaoting Ye, and Z. Wang, “Learning to Detect Video Saliency with HEVC Features”, IEEE Trans. Image Procesing, vol. 26, no. 1, Jan 2017.##[5] C. Li, Q. Tu, M. Zhao, J. Xu and A. Men, “A multiscale compressed video saliency detection model based on ant colony optimization”, in Proc. IEEE CIC/ICCC 2015 Symposium on Signal Processing for Communications, Nov. 2015.##[6] M. Jiang, X. Boix, G. Roig, J. Xu, L. V. Gool and Q. Zhao, “Learning to Predict Sequences of Human Visual Fixations”, IEEE Trans. Neural Networks and Learning Systems, vol 27, no.6, Jun 2016.##[7] حسین فیاضی، حمید دهقانی، مجتبی حسینی، "تجزیه تُنُک تصاویر ابرطیفی با استفاده از یک کتابخانه طیفی هرس شده"، فصل‌نامه پردازش علائم و داده‌ها، سال سیزدهم شماره ۳ (پیاپی ۲۹، ۱۳۹۵)، ۱۵۵ -۱۶۹، 1395.##[7] H.Fayazi, H.Dehqani, and M.Hosseini,”sparse unmixing of hyper-spectral images using a pruned spectral library”, signal and data processing, no.13, pp.155-169, 1395.##[8] مهدی بنی طالبی دهکردی، محمد تقی صادقی، حمید رضا ابوطالبی، "راهکار جدید استخراج ویژگی مبتنی بر نمونه‌برداری فشرده در پردازش سیگنال‌های صوتی"، فصل‌نامه پردازش علائم و داده‌ها، سال دهم شماره 1 (پیاپی 10، ۱۳۹2)، 57 -68، 1392.##[8] M,Bani Talebi.D, M.T.Sadeqi, and H.R. Aboutalebi, “New comprehensive sampling based feauture extraction method and its APPLICATION in audio signal processing”, signal and data processing, no.01,pp.57-68,1392.##[9] N. Li, J. Ye, Y. Ji, H. Ling and J. Yu, “Saliency Detection on Light Field”, IEEE Trans. Pattern Analysis and Machine Intellegence, vol. 39, no. 8, Aug 2017.##[10] S. Tajima and K. Komine, “Saliency-Based Color Accessibility”, IEEE Trans. Image Procesing, vol. 24, no. 3, Mur 2015.##[11] S. Frintrop, “VOCUS: A visual attention system for object detection and goal directed search,” Ph.D. dissertation, Rheinische Friedrich- Wil-helms-Universi¨tat Bonn, Bonn, Germany, 2005.##[12] L. Itti, “Models of bottom-up and top-down visual attention,” Ph.D. dissertation, Dept. Com-putat. Neur. Syst., California Inst. Technol, Pasadena, 2000.##[13] L. Itti, C. Koch, and E. Niebur, “Model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998.##[14] J. Harel, Ch. Koch, P. Perona, “Graph-Based Visual Saliency”, Advances in Neural Infor-mation Processing Systems, No.19. MIT Press, Cambridge, MA, pp. 545-552, 2007.##[15] T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1–8, Apr. 2007.##[16] X. Hou, J. Harel, and Ch. Koch, “Image Signature: Highlighting sparse salient regions”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 194-201, Jan 2012.##[17] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequencytuned salient region de-tection”, in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1597–1604, 2009. ##[18] L. Ye, Z. Liu, L. Li, L. Shen, C. Bai, and Yang Wang, “Salient Object Segmentation via Effec-tive Integration of Saliency and Objectness”, IEEE Trans. Multimedia, vol. 19, no. 8, pp. 1742 - 1756, Apr 2017.##[19] L. Zhang, X. Li, L. Nie, Y. Yang, and Y. Xia, “Weakly Supervised Human Fixations Predic-tion”, IEEE Trans. Cybernetics, vol. 46, no. 1, pp. 258 - 269, Jan. 2016.##[20] J. Li, L. Duan, X. Chen, T. Huang, and Y. Tian, “Finding the Secret of Image Saliency in the Frequency Domain”, IEEE Trans. Pattern Ana-lysis and Machine Intellegence, vol. 37, no. 12, pp. 2428-2440, Apr 2015.##[21] X. Hou and L. Zhang, “Saliency detection: A spectral residual approach,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 1–8, Nov. 2007.##[22] C. Guo, Q. Ma, and L. Zhang, “Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recog-nition, pp. 1–8, 2008.##[23] L. Zhang, J. Chen, and B. Qiu, “Region-of-Interest Coding Based on Saliency Detection and Directional Wavelet for Remote Sensing Imag-es”, IEEE Trans. Geoscience and Remote Sen-sing Letters, vol. 14, no. 1, pp. 23-27, Jan 2017.##[24] Y. Yang, Y. Que, Sh. Huang, and P. Lin, “Multiple Visual Features Measurement with Gradient Domain Guided Filtering for Multi-sensor Image Fusion”, IEEE Trans. Instru-mentation and Measurement, vol. 66, no. 4, Apr 2017.##[25] N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga, “Saliency estimation using a non-para-metric low-level vision model,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recog-nition, 2011.##[26] X. Liu, D. Zhai, J. Zhou, X. Zhang, D. Zhao, and Wen Gao, “Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication”, IEEE Trans. Image Procesing, vol. 25, no. 6, Jun 2016.##[27] M. Aghagolzadeh, H. Radha, “Joint Estimation of Dictionary and Image from Compressive Samples”, IEEE Trans. Computational Imaging, vol. pp, no. 99, Feb 2017.##[28] N. İmamoğlu, W. Lin, Y. Fang, “A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform”, IEEE Trans. Multimedia, vol. 15, no. 1, pp. 96-106, Jan 2013.##[29] S. A. Raza Naqvi, “Image compression using haar wavelet based tetrolet transform”, in Proc. IEEE Int. Conf. Open Source Systems and Technologies (ICOSST), pp. 50-54, Jan 2014.##[30] N. D. B. Bruce, Sh. Rahman, D. Carrier, “Sparse Coding in Early Visual Representation: From Specific Properties to General Principles”, Neurocomputing, vol. 2, no. 1, pp. 1085–1098, Aug 2015.##[31] M. Banitalebi-Dehkordi, A. Banitalebi-Dehkordi, J. Abouei, K. N. Plataniotis, “Face recognition using a new compressive sensing-based feature extraction method”, Multimedia Tools and Applications, vol. 1, no.2, pp.1-21, Jul. 2017.##[32] S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” in Proc. IEEE Int. Conf. Comput. Vision and Pattern Recognition, pp. 2376–2383, 2010.##[33] Microsoft Research Cambridge-12 database, available in: https://www.microsoft.com/en-us/-download/details.aspx? id= 52283& from=h-ttp%3A%2F%2Fresearch.microsoft.com%2Fenus%2Fum%2Fcambridge%2Fprojects%2Fmsrc12%2F, April, 2012.##[34] S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. London, U.K.: Academic-/Elsevier, pp. 20–24, 2009. ##

ارائه روش جدید حذف نوفه تصویر براساس یادگیری واژه‌نامه ناهمدوس و روش تطبیق فضا A Novel Image Denoising Method Based on Incoherent Dictionary Learning and Domain Adaptation Technique 1 1 در این مقاله یک روش جدید به‌منظور حذف نوفه تصویر براساس یادگیری واژه‌نامه ناهمدوس در فضای تطبیق یافته ارائه می‌شود. روال یادگیری واژه‌نامه براساس در‌نظر‌گرفتن معیار همدوسی به‌منظور حصول واژه‌نامه‌های فراکامل با اتم‌های ناهمدوس و به‌کارگیری روش تطبیق فضا به‌منظور کاهش زمان پردازش و دست‌یابی به تصویر حذف نوفه‌شده با دقت بیشتر است. با استفاده از این روش، واژه‌نامه اولیه‌ای از داده تصویر در دسترس تهیه و سپس اتم‌های آموزش‌دیده متناسب با نوفه‌ای که محیط آزمایش با آن درگیر است به‌کمک یک الگوریتم بهینه‌سازی جدید مبتنی‌بر روش حافظه محدود BFGS به‌روز می‌شوند. همچنین گام بازنمایی تُنُک در این الگوریتم بر مبنای یک الگوریتم مبتنی بر افزایش همدوسی اتم-داده است. آموزش واژه‌نامه فراکامل با اتم‌های ناهمدوس بسیار حائز اهمیت است؛ زیرا به خطای تقریب کوچک‌تر در بازنمایی تُنُک منتهی می‌شود چون در بازنمایی داده تصویر، اتم‌های مستقل از هم نقش بیشتری خواهند داشت و فضای داده را به بهترین نحو پوشش می‌دهند. همچنین از یک روش بازنمایی تُنُک ناهمدوس نیز در روال یادگیری واژه‌نامه بهره گرفته می‌شود. به‌کارگیری این روال یادگیری موجب دست‌یابی به تصویر حذف نوفه‌شده با دقت بالا می‌شود. نتایج شبیه‌سازی با نتایج الگوریتم حذف نویز تصویر مبتنی بر روال تطبیق فضای پایه و روش یادگیری واژه‌نامه مبتنی بر K-SVD مقایسه شده است. نتایج شبیه‌سازی‌های انجام‌شده نشان می‌دهد که الگوریتم پیشنهادی در حذف نوفه گوسین به نتایج مناسب‌تری نسبت به سایر الگوریتم‌ها دست یافته و توانسته است با به‌کارگیری اتم‌های ناهمدوس، ساختار داده ورودی را به‌گونه مناسبی بازنمایی کند. 2 In this paper, a new method for image denoising based on incoherent dictionary learning and domain transfer technique is proposed. The idea of using sparse representation concept is one of the most interesting areas for researchers. The goal of sparse coding is to approximately model the input data as a weighted linear combination of a small number of basis vectors. Two characteristics should be considered in the dictionary learning process: Atom-data coherence and mutual coherence between dictionary atoms. The first one determines the dependency between the dictionary atoms and training data frames. This criterion value should be high. Another parameter expresses the dependency between atoms defined as the maximum absolute value of the cross-correlations between them. Higher coherence to the data class and lower mutual coherence between atoms result in a small approximation error in sparse coding procedure. In the proposed dictionary learning process, a coherence criterion is employed to yield over complete dictionaries with the incoherent atoms. The purpose of learning dictionary with low mutual coherence value is to reduce the approximation error of sparse representation in the denoising process and also decrease the computing time. We utilize the least angle regression with coherence criterion (LARC) algorithm for sparse representation based on atom-data coherence in the first step of dictionary learning process. LARC sparse coding is an optimized generalization of the least angle regression algorithm with stopping condition based on a residual coherence. This approach is based on setting a variable cardinality value. Using atom-data coherence measure as stopping criteria in the sparse coding process yields the capability of balancing between source confusion and source distortion. A high value for the cardinality parameter or too dense coding results in the source confusion since the number of dictionary atoms is more than what is required for a proper representation. Source degradation occurs when the sparse coding is done with low cardinality parameter or too sparse coding. Therefore, the number of required atoms will not be enough and data cannot be coded exactly over these atoms. Therefore, the setting procedure of cardinality parameter must be performed precisely. The problem of finding a dictionary with low mutual coherence between its normalized atoms can be obtained by considering the Gram matrix. The mutual coherence is described by the maximum absolute value of the off-diagonal elements of this matrix. If all off-diagonal elements are the same, a dictionary with minimum self-coherence value is obtained. Also, we take advantage of domain adaptation technique to transfer a learned dictionary to an adapted dictionary in the denoising process. The initial atoms set randomly and are updated based on the selected patches of input noisy image using the proposed alternating optimization algorithm. According to these issues, the fitness function in dictionary learning problem includes three main sections: The first term is related to the minimization of approximation error. The next items are the incoherence criterion of dictionary atoms. The last one includes a transformation of initial atoms according to some patches of the noisy input data in the test step. We use limited-memory BFGS algorithm as an iterative solution for regular minimization of our objective function involved different terms. The simulation results show that the proposed method leads to significantly better results in comparison with the earlier methods in this context and the traditional procedures. 73 92 2018/01/32018/02/272017/11/172018/04/232018/06/132017/12/25 1396/10/4 2018/05/232019/06/192019/03/132019/07/102019/02/132019/02/23 1397/12/4 رضا مظفری Reza Mozaffari موسسه آموزش عالی علوم و فناوری آریان ایران rezamozaffari3@gmail.com سمیرا مودّتی Samira Mavaddati دانشگاه مازندران ایران s.mavaddati@umz.ac.ir Image denoising Dictionary learning Coherence Domain adaptation Image processing حذف نوفه پردازش تصویر یادگیری واژه‌نامه همدوسی تطبیق فضا ]1[ H. Naimi, A. B. H. Adamou-Mitiche, L. Mitiche, “Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter”, Journal of King Saud University-Computer and Information Sciences, vol: 27, no.1, pp. 40-45, 2015.##]2[ A. Teodoro, M. Almeida, M. Figueiredo, “Single-frame image denoising and inpainting using Gaussian mixtures”, International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 283-288, 2015.##]3[ S. Beckouche, J. L. Starck, and J. Fadili, “Astronomical image denoising using dictionary learning”, Astronomy & Astrophysics 556, A132, 2013.##]4[ Y. Zhu, C. Huang, “An improved median filtering algorithm for image noise reduction”, Physics Procedia, no. 25, pp. 609-616, 2012.##]5[ Y. L. You, M. Kaveh., “Fourth order partial differential equations for noise removal?”, IEEE Trans. Image Processing, vol. 9, no. 10, pp. 1723-1730, 2000.##]6[ A. Mehdizadeh, M. Hosseinifatemi, “Image denoising based on modified adapted block and 3D filter”, 1st Conference on electrical engi-neering (ICEE2015), Langaroud, 2015.##]7[ C. Huang, Y. Zhu, “New Morphological Filtering Algorithm for Image Noise Reduction”, Second International Congress on Image and Signal Processing, 2009, pp. 1-6.##]8[ R. G. Baraniuk, “Compressive Sensing,” IEEE Signal Processing Magazine, pp. 118-121, 2007. ##]9[ D. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.##]10[ T. Gan, W. Lu, “Image denoising using multi-scale sparse representation”, School of Elec-trical & Electronic Engineering, Nanyang Tech-nological University, Singapore, Proceedings of IEEE 17th International Conference on Image Processing, pp. 1165-1168, 2010.##]11[ P. Chatterjee, P. Milanfar, “Image denoising using locally learned dictionaries”, Department of Electrical Engineering, University of California, Santa Cruz, CA 95064, USA, 2009.##]12[ M. Aharon, M. Elad, A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dic-tionaries for sparse representation”, IEEE Trans. Signal Process, vol. 54, no. 11, pp. 4311-4322, 2006.##]13[ Z. Fen, X. Kai, “A novel image denoising method based on DCT basis and sparse representation”, Cross Strait Quad-Regional Radio Science and Wireless Technology Con-ference, College of information &mechanical engineering Beijing Institute of Graphic Communication, pp.1307-1310, 2011.##]14[ J. Mairal, M. Elad, G. Sapiro, “Sparse learned representations for image restoration”, In Proc. of the 4th World Conf. of the Int. Assoc. for Statistical Computing (IASC), Yokohama, Japan, 2008.##]15[ S. Li, L. Fang, H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising”, IEEE transactions on biomedical engineering, vol. 59, no. 2, pp. 417-427, 2012.##]16[ W. S. Dong, X. Li, L. Zhang, G. M. Shi, “Sparsity-based Image Denoising via Dic-tionary Learning and Structural Clus-tering”, In Proceedings of the International Conference on Image Processing (ICIP), Brussels, Belgium, pp. 11–14, 2011.##]17[ X. Lu, H. Yuan, P. Yan, Y. Yuan, L. Li, X. Li, “Image denoising via improved sparse coding”, Proceedings of the British Machine Vision Conference, pp. 74-81, 2011.##]18[ J. Wang, J. F., Cai, Y. Shi, B. Yin, “Incoherent dictionary learning for sparse representation based image denoising”, IEEE international Conference on Image Processsing. Piscataway, NJ: IEEE, pp. 4582-4586, 2014.##]19[ T. Tong, J. Caballero, K. Bhatia, D. Rueckert, “Dictionary learning for medical image denoising, reconstruction, and segmentation”, Machine Learning and Medical Imaging, pp. 153-181, 2016.##]20[ M. Karimipoor, V. Abolghasemi, S. Ferdowsi, “An Efficient Image Denoising Approach Based on Dictionary Learning”, International Journal of Mathematics and Computational Science, vol. 2, no. 1, , pp. 1-7, 2016.##]21[ G. Davis, S. Mallat, Z. Zhang, “Adaptive time-frequency decompositions”, Optical-Engi-neering, vol. 33, pp. 218-391, 1994.##]22[ A. Agarwal, A. Anandkumar, P. Jain, P. Netrapalli, R. Tandon, “Learning sparsely used overcomplete dictionaries”, JMLR: Workshop and Conference Proceedings, vol. 35, pp. 1-15, 2014. ##]23[ H. Lee, A. Battle, R. Raina, A. Y. Ng, “Efficient sparse coding algorithms”, Advances in Neural Information Processing Systems, 2006.##]24[ J. Portilla, L. Mancera, “L0-based sparse approximation: Two alternative methods and some applications”, Proceedings of the 16th IEEE international conference on Image pro-cessing, pp. 3865-3868, 2009.##]25[ K. Engan, S. O. Aase, J. H. Hakon-Husoy, “Method of optimal directions for frame design”, IEEE International Conference on Acoustics, Speech, and Signal Processing., vol. 5 , pp. 2443-2446, 1999.##]26[ K. Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, T. J. Sejnowski, “Dictionary learning algorithms for sparse representation”, Neural Computation., vol. 15, no. 2, pp. 349-396, 2003.##]27[ G. Chen, C. Xiong, J. J. Corso, “Dictionary transfer for image denoising via domain adap-tation,” In Proceedings of IEEE International Conference on Image Processing, 2012.##]28[ S. Mavaddaty, S. M. Ahadi, S. Seyedin, “A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation”, Speech Communication, vol. 76, pp. 42-60, 2016.##]29[ S. Chen, S.A. Billings, W. Luo. "Orthogonal least squares methods and their application to non-linear system identication", International Journal of Control., vol. 50, pp.1873-1896, 1989.##]30[ D. Barchiesi, M. D. Plumbley, “Learning incoherent dictionaries for sparse approximation using iterative projections and rotations”, IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2055-2065, 2013.##]31[ A. Mirjalili, V. Abootalebi, M. T. Sadeghi, “Improving the performance of sparse representation-based classifier for EEG classi-fication”, JSDP, vol. 12, no. 3, pp. 43-55, 2015.##]32[ C. D. Sigg, T. Dikk, J. M. Buhmann, “Speech enhancement using generative dictionary learning”, IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 6, pp.1698-1712, 2012.##]33[ B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, “Least angle regression”, Ann. Stat., vol. 32, pp. 407-499, 2004.##]34[ D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization”, Math. Program, vol. 45, pp. 503-528, 1989.##]35[ X. Song, Z. Liu, “A Fuzzy adaptive K-SVD dictionary algorithm for face recognition”, Proceedings of the 2nd International Con-ference on Computer Science and Electronics Engineering (ICCSEE), pp. 2164-2168, 2013. ##]36[ G. Jenatton, F. Bach. “Structured sparse principal component analysis”, Technical report, pp. 366-373, 2009.##]37[ M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries”, IEEE Trans. on Image Processing, vol. 15, no. 12, pp. 3736-3745, 2006.##]1[ H. Naimi, A. B. H. Adamou-Mitiche, L. Mitiche, “Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter”, Journal of King Saud University-Computer and Information Sciences, vol: 27, no.1, pp. 40-45, 2015.##]2[ A. Teodoro, M. Almeida, M. Figueiredo, “Single-frame image denoising and inpainting using Gaussian mixtures”, International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 283-288, 2015.##]3[ S. Beckouche, J. L. Starck, and J. Fadili, “Astronomical image denoising using dictionary learning”, Astronomy & Astrophysics 556, A132, 2013.##]4[ Y. Zhu, C. Huang, “An improved median filtering algorithm for image noise reduction”, Physics Procedia, no. 25, pp. 609-616, 2012.##]5[ Y. L. You, M. Kaveh., “Fourth order partial differential equations for noise removal?”, IEEE Trans. Image Processing, vol. 9, no. 10, pp. 1723-1730, 2000.##]6[ مهدی زاده همت آبادی، امیر، حسینی فاطمی، محمدرضا، "حذف نویز تصاویر با استفاده از الگوریتم بهبود یافته تطبیق بلوک و فیلتر سه بعدی"، اولین کنفرانس ملی مهندسی برق دانشگاه آزاد اسلامی واحد لنگرود، 1393.##]6[ A. Mehdizadeh, M. Hosseinifatemi, “Image denoising based on modified adapted block and 3D filter”, 1st Conference on electrical engi-neering (ICEE2015), Langaroud, 2015.##]7[ C. Huang, Y. Zhu, “New Morphological Filtering Algorithm for Image Noise Reduction”, Second International Congress on Image and Signal Processing, 2009, pp. 1-6.##]8[ R. G. Baraniuk, “Compressive Sensing,” IEEE Signal Processing Magazine, pp. 118-121, 2007. ##]9[ D. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.##]10[ T. Gan, W. Lu, “Image denoising using multi-scale sparse representation”, School of Elec-trical & Electronic Engineering, Nanyang Tech-nological University, Singapore, Proceedings of IEEE 17th International Conference on Image Processing, pp. 1165-1168, 2010.##]11[ P. Chatterjee, P. Milanfar, “Image denoising using locally learned dictionaries”, Department of Electrical Engineering, University of California, Santa Cruz, CA 95064, USA, 2009.##]12[ M. Aharon, M. Elad, A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dic-tionaries for sparse representation”, IEEE Trans. Signal Process, vol. 54, no. 11, pp. 4311-4322, 2006.##]13[ Z. Fen, X. Kai, “A novel image denoising method based on DCT basis and sparse representation”, Cross Strait Quad-Regional Radio Science and Wireless Technology Con-ference, College of information &mechanical engineering Beijing Institute of Graphic Communication, pp.1307-1310, 2011.##]14[ J. Mairal, M. Elad, G. Sapiro, “Sparse learned representations for image restoration”, In Proc. of the 4th World Conf. of the Int. Assoc. for Statistical Computing (IASC), Yokohama, Japan, 2008.##]15[ S. Li, L. Fang, H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising”, IEEE transactions on biomedical engineering, vol. 59, no. 2, pp. 417-427, 2012.##]16[ W. S. Dong, X. Li, L. Zhang, G. M. Shi, “Sparsity-based Image Denoising via Dic-tionary Learning and Structural Clus-tering”, In Proceedings of the International Conference on Image Processing (ICIP), Brussels, Belgium, pp. 11–14, 2011.##]17[ X. Lu, H. Yuan, P. Yan, Y. Yuan, L. Li, X. Li, “Image denoising via improved sparse coding”, Proceedings of the British Machine Vision Conference, pp. 74-81, 2011.##]18[ J. Wang, J. F., Cai, Y. Shi, B. Yin, “Incoherent dictionary learning for sparse representation based image denoising”, IEEE international Conference on Image Processsing. Piscataway, NJ: IEEE, pp. 4582-4586, 2014.##]19[ T. Tong, J. Caballero, K. Bhatia, D. Rueckert, “Dictionary learning for medical image denoising, reconstruction, and segmentation”, Machine Learning and Medical Imaging, pp. 153-181, 2016.##]20[ M. Karimipoor, V. Abolghasemi, S. Ferdowsi, “An Efficient Image Denoising Approach Based on Dictionary Learning”, International Journal of Mathematics and Computational Science, vol. 2, no. 1, , pp. 1-7, 2016.##]21[ G. Davis, S. Mallat, Z. Zhang, “Adaptive time-frequency decompositions”, Optical-Engi-neering, vol. 33, pp. 218-391, 1994.##]22[ A. Agarwal, A. Anandkumar, P. Jain, P. Netrapalli, R. Tandon, “Learning sparsely used overcomplete dictionaries”, JMLR: Workshop and Conference Proceedings, vol. 35, pp. 1-15, 2014. ##]23[ H. Lee, A. Battle, R. Raina, A. Y. Ng, “Efficient sparse coding algorithms”, Advances in Neural Information Processing Systems, 2006.##]24[ J. Portilla, L. Mancera, “L0-based sparse approximation: Two alternative methods and some applications”, Proceedings of the 16th IEEE international conference on Image pro-cessing, pp. 3865-3868, 2009.##]25[ K. Engan, S. O. Aase, J. H. Hakon-Husoy, “Method of optimal directions for frame design”, IEEE International Conference on Acoustics, Speech, and Signal Processing., vol. 5 , pp. 2443-2446, 1999.##]26[ K. Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, T. J. Sejnowski, “Dictionary learning algorithms for sparse representation”, Neural Computation., vol. 15, no. 2, pp. 349-396, 2003.##]27[ G. Chen, C. Xiong, J. J. Corso, “Dictionary transfer for image denoising via domain adap-tation,” In Proceedings of IEEE International Conference on Image Processing, 2012.##]28[ S. Mavaddaty, S. M. Ahadi, S. Seyedin, “A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation”, Speech Communication, vol. 76, pp. 42-60, 2016.##]29[ S. Chen, S.A. Billings, W. Luo. "Orthogonal least squares methods and their application to non-linear system identication", International Journal of Control., vol. 50, pp.1873-1896, 1989.##]30[ D. Barchiesi, M. D. Plumbley, “Learning incoherent dictionaries for sparse approximation using iterative projections and rotations”, IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2055-2065, 2013.##]31[ میرجلیلی، علیرضا، ابوطالبی، وحید، صادقی، محمد تقی، "بهبود کارایی طبقه‌بندی‌کننده مبتنی‌بر نمایش تنک برای طبقه‌بندی سیگنال‌های مغزی"، پردازش علائم و داده‌ها، جلد 12، شماره 3، صفحه ۴۳-۵۵، ۱۳۹۴.##]31[ A. Mirjalili, V. Abootalebi, M. T. Sadeghi, “Improving the performance of sparse representation-based classifier for EEG classi-fication”, JSDP, vol. 12, no. 3, pp. 43-55, 2015.##]32[ C. D. Sigg, T. Dikk, J. M. Buhmann, “Speech enhancement using generative dictionary learning”, IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 6, pp.1698-1712, 2012.##]33[ B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, “Least angle regression”, Ann. Stat., vol. 32, pp. 407-499, 2004.##]34[ D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization”, Math. Program, vol. 45, pp. 503-528, 1989.##]35[ X. Song, Z. Liu, “A Fuzzy adaptive K-SVD dictionary algorithm for face recognition”, Proceedings of the 2nd International Con-ference on Computer Science and Electronics Engineering (ICCSEE), pp. 2164-2168, 2013. ##]36[ G. Jenatton, F. Bach. “Structured sparse principal component analysis”, Technical report, pp. 366-373, 2009.##]37[ M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries”, IEEE Trans. on Image Processing, vol. 15, no. 12, pp. 3736-3745, 2006. ##

تشخیص موجودیت‌های نامدار در متون فارسی با استفاده از یادگیری عمیق Named Entity Recognition in Persian Text using Deep Learning 1 1 شناسایی موجودیت‌­های نامدار[1] یکی از فعالیت‌­های زیربنایی در حوزه پردازش زبان طبیعی[2] و به‌طور‌کلی زیر‌مجموعه‌­ای از استخراج اطلاعات[3] است. در فرآیند شناسایی موجودیت‌­های نامدار به‌دنبال یافتن عناصر اسمی در متن و دسته­‌بندی آنها به رده‌­هایی ازپیش‌­تعیین­‌شده از قبیل اسامی اشخاص، سازمان­‌ها، مکان‌­ها، مذاهب، عنوان کتاب­‌ها، عنوان فیلم­ها و غیره هستیم. در این مقاله با بهره­گیری از روش­های نوین در این حوزه مانند استفاده از دو بُردار مختلف بازنمایی معنایی واژگان برمبنای کلمه و حروف تشکیل‌دهنده آن برمبنای شبکه­‌های عصبیو همچنین استفاده از روش‌­های یادگیری عمیق[4] یک سامانه تشخیص موجودیت­‌های نامدار معرفی می‌شود. همچنین در راستای پژوهش حاضر، یک پیکره برچسب­‌گذاری­شده شامل سه‌هزار چکیده از ویکی­‌پدیای فارسی که شامل نود‌هزار واژه است با استفاده از پانزده برچسب مختلف ارائه می‌شود که گام مهمی در ارتقای پژوهش‌­های آینده این حوزه برداشته ­خواهد شد. نتایج حاصل از ارزیابی سامانه پیشنهادی نشان می­‌دهد که می­‌توان با استفاده از داده معرفی­‌شده به دقت 09/72 در معیار F رسید. [1] Named Entity Recognition [2] Natural Language Processing [3] Information Extraction [4] Deep Learning 2 Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefits from neural network-based approaches for both word representation and entity tagging. In the word representation part of the proposed model, two different vector representations are used and compared: (1) the semantic representation of words based on their context using word2vec continues skip-gram model, and (2) the semantic representation of words based on their context as well as characters forming them using fasttext. While the former model captures the semantic concepts of words, the latter one considers the morphological similarity of words as well. For the entity identification, a deep Bidirectional Long Short Term Memory (BiLSTM) network is used. Using LSTM model helps to consider the history of text when predicting entities, while the BiLSTM model expands this idea by benefiting from the history from both sides of the context. Moreover, inline of the present research, an annotated corpus containing 3000 abstracts (90000 tokens) from the Persian Wikipedia is provided. In contrast to the available datasets in the field, which includes up to 7 label types, the new dataset contains 15 different labels, namely person individual, person group, organizations, locations, religions, books, magazines, movies, languages, nationalities, events, jobs, dates, fields, and other. Developing this dataset will be an important step in promoting future research in this field, especially for the tasks such as question answering that need wider range of entity types. The results of the proposed system show that by using the introduced model and the provided data, the system can achieve 72.92 F-measure. 93 112 2018/01/32018/02/272017/11/172018/04/232018/06/132017/12/252018/01/21 1396/11/1 2018/05/232019/06/192019/03/132019/07/102019/02/132019/02/232019/06/19 1398/3/29 سعیده ممتازی Saeedeh Momtazi دانشگاه صنعتی امیرکبیر ایران momtazi@aut.ac.ir فرزانه ترابی Farzaneh Torabi دانشگاه صنعتی امیرکبیر ایران f9torabi@gmail.com Name entity recognition natural language processing word embedding deep learning تشخیص موجودیت‌های نامدار پردازش زبان طبیعی بازنمایی معنایی کلمات یادگیری عمیق [1] Y. Chen, T.A. Lasko, Q. Mei, J.C. Denny and H. Xu, “A study of active learning methods for named entity recognition in clinical text”, Journal of Biomedical Informatics, 2015.##[2] Z.S. Abdallaha, M.Carmana and Gh. H7affari, “Multi-domain evaluation framework for named entity recognition tools”,Computer Speech & Language, 2017.##[3] S. Hussain, J.J. K. and G.C. Hazarika, “The First Step Towards Named Entity Recognition in Missing Language”, International Conference on Electrical, Electronics, and Optimization Tech-niques, 2016.##[4] D.Jurafsky, and M.James H, “Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition”, Prentice Hall, 2009.##[5] C.Santos, B.Zadrozny, “Learning Character-level Representations for Part-of-Speech Tagging”, International Conference on Machine Learning, 2014.##[6] O.Moradiannasab, S.Momtazi and A.Palmer, “A Named Entity Recognition Tool for Persian”, In Proceedings of the 3rd Iranian Conference on Computational Linguistics, 2014.##[7] F.Erik, K.S.Tjong, & D.M.Fien, “Introduction to the CoNLL-2003 Shared Task: Language-In-dependent Named Entity Recognition”, Proc-eedings of CoNLL-2003, pp. 142-147, 2003.##[8] S. Morwal, N. Jahan and D. Chopra, “Named Entity Recognition using Hidden Markov Model (HMM)” International Journal on Natural Language Computing (IJNLC), Vol.1, No.4, pp. 15-23, 2012.##[9] C. Lee, Y. Hwang, H. Oh, S. Lim, J. Heo, C. Lee, H. Kim, J. Wang, M. Jang, “Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering”, Asia Information Retrieval Technology, Lecture Notes in Computer Science, vol 4182, 2006.##[10] S. Özkaya and B. Diri, “Named Entity Recognition by Conditional Random Fields from Turkish informal texts”, IEEE 19th Signal Processing and Communications Applications Conference (SIU), pp. 662-665, 2011.##[11] Y. Benajiba and P. Rosso, “Arabic Named Entity Recognition using Conditional Random Fields”, Workshop on HLT&NLP within the Arabic World, LREC, 2008.##[12] A. Ekbal and S. Bandyopadhyay, “A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi”. Linguistic Issues in Language Technology (LiLT), Volume (2:1), pp. 1-44, CSLI Publication, 2009.##[13] J.R. Curran and S. Clark. 2003, “Language independent NER using a maximum entropy tagger”, Seventh conference on Natural lan-guage learning at HLT-NAACL, Association for Computational Linguistics, pp. 164-167, 2003.##[14] L. Li, L. Jin, Z. Jiang, D. Song and D. Huang, “Biomedical named entity recognition based on extended Recurrent Neural Networks”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 649-652, 2015.##[15] N. Suakkaphong, Z. Zhang, H. Chen, “Disease named entity recognition using semisupervised learning and conditional random fields”, Journal of the American Society for Information Science and Technology, Vol. 62, Issue 4, 2011.##[16] Q. Wei, T. Chen, R. Xu, Y. He, L. Gui, “Disease named entity recognition by combining cond-itional random fields and bidirectional recurrent neural networks”, journal of biological data-bases and curation, 2016.##[17] P. Mortazavi and M. Shamsfard. “Named Entity Recognition in Persian Texts”, The 15th National CSI Computer Conference, 2009.##[18] M. Kolali Khormuji and M. Bazrafkan, “Persian named entity recognition based with local filters”, International Journal of Computer Applications 100(4), 2014.##[19] M. Bijankhan, J. Sheykhzadegan, M. Bahrani, and M. Ghayoomi, “Lessons from building a Persian written corpus: Peykare”, Language resources and evaluation, 45(2), pp. 143–164, 2011.##[20] M. Abdoos, B. Manaei, “Improving Named Entity Recognition Using Izafe in Farsi”,JSDP, vol.14 (4), pp.43-54, 2018.##[21] F. Ahmadi and H. Moradi, “A hybrid method for Persian named entity recognition”, The IEEE Conference on Information and Knowledge Technology (IKT), pp. 1–7, 2015.##[22] H. Poostchi, E.Z. Borzeshi, M. Abdous, and M. Piccardi, “PersoNER: Persian named-entity recognition”, International Conference on Com-putational Linguistics (COLING), 2016.##[23] K. Dashtipour, M. Gogate, A. Adeel, A. Algarafi, N. Howard, and A. Hussain, “Persian Named Entity Recognition”, IEEE International Con-ference on Cognitive Informatics & Cognitive Computing, pp. 79–83, 2017.##[24] A. Shakeri, M. Shahshahani, H. Feili, M. Mohseni, and M. Molla Abbasi., “Persian Language Processing Tools (Research on Named Entity Recognition Tools in Natural Language and Presentation of a Laboratory Instance for Persian)”, Technical Report SE-P18-MGT-PRS-01-v2.0. Iran Telecommunica-tion Research Center, 2017.##[25] J. Turian, L. Ratinov, and Y. Bengio, “Word representations: a simple and general method for semi-supervised learning”, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 384-394, 2010.##[26] T. Mikolov, K.Chen, G.Corrado, J.Dean, "Efficient Estimation of Word Representations in Vector Space",arXiv preprint arXiv:1301.-3781, 2016.##[27] C.N.dos Santos and V.Guimaraes, “Boosting Named Entity Recognition with Neural Character Embeddings”,Proceedings of the Fifth Named Entity Workshop, 2015.##[28] P.Bojanowski, E.Grave, A.Joulin, and T.Mikolov, “Enriching Word Vectors with Subword Information”, Transactions of the Association for Computational Linguistics, 5:135–146, 2017.##[29] R. Socher, Ch. D. Manning and A.Y. Ng, “Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks”, Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop, 2010.##[30] Phong Le and Willem H. Zuidema, "Compositional Distributional Semantics with Long Short Term Memory", Proceedings of the Fourth Joint Conference on Lexical and Com-putational Semantics, 2015.##[31] S. Hochreiter and J. Schmidhuber, Long Short-Term Memory. Neural Computing, vol.9 (8), 1997.##[32] G.Lample, M.Ballesteros, Sandeep Subramanian, K.Kawakami and Ch.Dyer, “Neural Architectures for Named Entity Recognition”, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.##[33] Poostchi, Hanieh, Ehsan Zare Borzeshi, Mohammad Abdous and Massimo Piccardi. “PersoNER: Persian Named-Entity Recogni-tion.” COLING, 2016.##[34] M. Ghayoomi, “Bootstrapping the development of an HPSG-based treebank for Persian,” Lin-guistic Issues in Language Technology, vol.7, no.1, 2012.##[35] J.R. Finkel, T.Grenager and Ch.Manning, "Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling", Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics, pp. 363-370, 2005.##[36] M. Konkol, M. Konopík, “Segment Representations in Named Entity Recognition”. International Conference on Text, Speech, and Dialogue (TSD), Lecture Notes in Computer Science, vol 9302. Springer, 2015.##[37] S. Momtazi and O. Moradiannasab, “A Statistical Approach for Knowledge Discovery: Bootstrapped Analysis of Language Models for Knowledge base Population from Unstructured Text”. Scientia Iranica 26 (Special Issue on: Socio-Cognitive Engineering), pp. 26-39, 2019.##[38] S. Momtazi and D. Kalkow, “Bridging the Vocabulary Gap between Questions and Answer Sentences”. Information Processing & Management, 51 (5), 2015.##[39] https://fa.wikipedia.org,96.05.25.##[40] A. Hadifar, S. Momtazi, “The Impact of Corpus Domain on Word Representation: a Study on Persian Word Embeddings”, Lang Resources & Evaluation, 52(4), pp. 997–1019, 2018.##[41] Z. Bairong, W. Wenbo, L. Zhiyu, Z. Chonghui, T. Shinozaki, “Comparative Analysis of Word Embedding Methodsfor DSTC6 End-to-End Conversation Modeling Track”, Proceddings of International Conference of Dialog System Technology Challenges, 2017.##[42] R.Rehurek and S.Petr, “Software Framework for Topic Modelling with Large Corpora”, In Proceedings of LREC2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50, 2010.##[1] Y. Chen, T.A. Lasko, Q. Mei, J.C. Denny and H. Xu, “A study of active learning methods for named entity recognition in clinical text”, Journal of Biomedical Informatics, 2015.##[2] Z.S. Abdallaha, M.Carmana and Gh. H7affari, “Multi-domain evaluation framework for named entity recognition tools”,Computer Speech & Language, 2017.##[3] S. Hussain, J.J. K. and G.C. Hazarika, “The First Step Towards Named Entity Recognition in Missing Language”, International Conference on Electrical, Electronics, and Optimization Tech-niques, 2016.##[4] D.Jurafsky, and M.James H, “Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition”, Prentice Hall, 2009.##[5] C.Santos, B.Zadrozny, “Learning Character-level Representations for Part-of-Speech Tagging”, International Conference on Machine Learning, 2014.##[6] O.Moradiannasab, S.Momtazi and A.Palmer, “A Named Entity Recognition Tool for Persian”, In Proceedings of the 3rd Iranian Conference on Computational Linguistics, 2014.##[7] F.Erik, K.S.Tjong, & D.M.Fien, “Introduction to the CoNLL-2003 Shared Task: Language-In-dependent Named Entity Recognition”, Proc-eedings of CoNLL-2003, pp. 142-147, 2003.##[8] S. Morwal, N. Jahan and D. Chopra, “Named Entity Recognition using Hidden Markov Model (HMM)” International Journal on Natural Language Computing (IJNLC), Vol.1, No.4, pp. 15-23, 2012.##[9] C. Lee, Y. Hwang, H. Oh, S. Lim, J. Heo, C. Lee, H. Kim, J. Wang, M. Jang, “Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering”, Asia Information Retrieval Technology, Lecture Notes in Computer Science, vol 4182, 2006.##[10] S. Özkaya and B. Diri, “Named Entity Recognition by Conditional Random Fields from Turkish informal texts”, IEEE 19th Signal Processing and Communications Applications Conference (SIU), pp. 662-665, 2011.##[11] Y. Benajiba and P. Rosso, “Arabic Named Entity Recognition using Conditional Random Fields”, Workshop on HLT&NLP within the Arabic World, LREC, 2008.##[12] A. Ekbal and S. Bandyopadhyay, “A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi”. Linguistic Issues in Language Technology (LiLT), Volume (2:1), pp. 1-44, CSLI Publication, 2009.##[13] J.R. Curran and S. Clark. 2003, “Language independent NER using a maximum entropy tagger”, Seventh conference on Natural lan-guage learning at HLT-NAACL, Association for Computational Linguistics, pp. 164-167, 2003.##[14] L. Li, L. Jin, Z. Jiang, D. Song and D. Huang, “Biomedical named entity recognition based on extended Recurrent Neural Networks”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 649-652, 2015.##[15] N. Suakkaphong, Z. Zhang, H. Chen, “Disease named entity recognition using semisupervised learning and conditional random fields”, Journal of the American Society for Information Science and Technology, Vol. 62, Issue 4, 2011.##[16] Q. Wei, T. Chen, R. Xu, Y. He, L. Gui, “Disease named entity recognition by combining cond-itional random fields and bidirectional recurrent neural networks”, journal of biological data-bases and curation, 2016.##[17] پ. مرتضوی و م. شمس‌فرد، «شناسایی موجودیت‌های نامدار در متون فارسی». پانزدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران. تهران. انجمن کامپیوتر. مرکز توسعه فناوری نیرو. ۱۳۸۸##[17] P. Mortazavi and M. Shamsfard. “Named Entity Recognition in Persian Texts”, The 15th National CSI Computer Conference, 2009.##[18] M. Kolali Khormuji and M. Bazrafkan, “Persian named entity recognition based with local filters”, International Journal of Computer Applications 100(4), 2014.##[19] M. Bijankhan, J. Sheykhzadegan, M. Bahrani, and M. Ghayoomi, “Lessons from building a Persian written corpus: Peykare”, Language resources and evaluation, 45(2), pp. 143–164, 2011.##[20]م. عبدوس، ب. مینایی بیدگلی. "بهبود شناسایی موجودیت‌های نامدار فارسی با استفاده از کسره اضافه". پردازش علائم و داده‌ها. ۱۴ (۴) :۴۳-۵۴. ۱۳۹۶##[20] M. Abdoos, B. Manaei, “Improving Named Entity Recognition Using Izafe in Farsi”,JSDP, vol.14 (4), pp.43-54, 2018.##[21] F. Ahmadi and H. Moradi, “A hybrid method for Persian named entity recognition”, The IEEE Conference on Information and Knowledge Technology (IKT), pp. 1–7, 2015.##[22] H. Poostchi, E.Z. Borzeshi, M. Abdous, and M. Piccardi, “PersoNER: Persian named-entity recognition”, International Conference on Com-putational Linguistics (COLING), 2016.##[23] K. Dashtipour, M. Gogate, A. Adeel, A. Algarafi, N. Howard, and A. Hussain, “Persian Named Entity Recognition”, IEEE International Con-ference on Cognitive Informatics & Cognitive Computing, pp. 79–83, 2017.##[24]آ. شاکری، م. شهشهانی، ه. فیلی، م. محسنی، م. ملاعباسی، «تشخیص موجودیت‌های اسمی در زبان فارسی». گزارش فنی، SE-P18-MGT-PRS-01-v2، مرکز تحقیقات مخابرات ایران، 1397 ##[24] A. Shakeri, M. Shahshahani, H. Feili, M. Mohseni, and M. Molla Abbasi., “Persian Language Processing Tools (Research on Named Entity Recognition Tools in Natural Language and Presentation of a Laboratory Instance for Persian)”, Technical Report SE-P18-MGT-PRS-01-v2.0. Iran Telecommunica-tion Research Center, 2017.##[25] J. Turian, L. Ratinov, and Y. Bengio, “Word representations: a simple and general method for semi-supervised learning”, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 384-394, 2010.##[26] T. Mikolov, K.Chen, G.Corrado, J.Dean, "Efficient Estimation of Word Representations in Vector Space",arXiv preprint arXiv:1301.-3781, 2016.##[27] C.N.dos Santos and V.Guimaraes, “Boosting Named Entity Recognition with Neural Character Embeddings”,Proceedings of the Fifth Named Entity Workshop, 2015.##[28] P.Bojanowski, E.Grave, A.Joulin, and T.Mikolov, “Enriching Word Vectors with Subword Information”, Transactions of the Association for Computational Linguistics, 5:135–146, 2017.##[29] R. Socher, Ch. D. Manning and A.Y. Ng, “Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks”, Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop, 2010.##[30] Phong Le and Willem H. Zuidema, "Compositional Distributional Semantics with Long Short Term Memory", Proceedings of the Fourth Joint Conference on Lexical and Com-putational Semantics, 2015.##[31] S. Hochreiter and J. Schmidhuber, Long Short-Term Memory. Neural Computing, vol.9 (8), 1997.##[32] G.Lample, M.Ballesteros, Sandeep Subramanian, K.Kawakami and Ch.Dyer, “Neural Architectures for Named Entity Recognition”, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.##[33] Poostchi, Hanieh, Ehsan Zare Borzeshi, Mohammad Abdous and Massimo Piccardi. “PersoNER: Persian Named-Entity Recogni-tion.” COLING, 2016.##[34] M. Ghayoomi, “Bootstrapping the development of an HPSG-based treebank for Persian,” Lin-guistic Issues in Language Technology, vol.7, no.1, 2012.##[35] J.R. Finkel, T.Grenager and Ch.Manning, "Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling", Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics, pp. 363-370, 2005.##[36] M. Konkol, M. Konopík, “Segment Representations in Named Entity Recognition”. International Conference on Text, Speech, and Dialogue (TSD), Lecture Notes in Computer Science, vol 9302. Springer, 2015.##[37] S. Momtazi and O. Moradiannasab, “A Statistical Approach for Knowledge Discovery: Bootstrapped Analysis of Language Models for Knowledge base Population from Unstructured Text”. Scientia Iranica 26 (Special Issue on: Socio-Cognitive Engineering), pp. 26-39, 2019.##[38] S. Momtazi and D. Kalkow, “Bridging the Vocabulary Gap between Questions and Answer Sentences”. Information Processing & Management, 51 (5), 2015.##[39] https://fa.wikipedia.org,96.05.25.##[40] A. Hadifar, S. Momtazi, “The Impact of Corpus Domain on Word Representation: a Study on Persian Word Embeddings”, Lang Resources & Evaluation, 52(4), pp. 997–1019, 2018.##[41] Z. Bairong, W. Wenbo, L. Zhiyu, Z. Chonghui, T. Shinozaki, “Comparative Analysis of Word Embedding Methodsfor DSTC6 End-to-End Conversation Modeling Track”, Proceddings of International Conference of Dialog System Technology Challenges, 2017.##[42] R.Rehurek and S.Petr, “Software Framework for Topic Modelling with Large Corpora”, In Proceedings of LREC2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50, 2010. ##

نقش روش‌های ابتکاری با طول متغیر در طراحی و آموزش بهینه شبکه‌های ANFIS Role of Heuristic Methods with variable Lengths In ANFIS Networks Optimum Design and Training 1 1 سامانه‌‌های ANFIS به‌دلیل عملکرد قابل قبولی که در زمینه ایجاد و آموزش طبقه‌بند فازی داده دارند، بسیار موردتوجه واقع‌شده‌اند. یک چالش اصلی در طراحی یک سامانه ANFIS رسیدن به یک روش کارآمد، با دقت بالا و قابلیت تفسیر مناسب است. بدون تردید نوع و مکان توابع عضویت و همچنین نحوه آموزش یک شبکه ANFIS تأثیر به‌سزایی در عملکرد آن دارد. تاکنون پژوهش‌های مرتبط تنها به یافتن نوع و مکان توابع عضویت و یا پیشنهاد روشی برای آموزش این شبکه‌ها بسنده کرده‌اند. علت اصلی عدم به‌کارگیری هم‌زمان تعیین نوع و مکان توابع عضویت و آموزش یک شبکه ANFIS در ثابت بودن طول نسخه‌های استاندارد روش‌های ابتکاری است. در این مقاله، ابتدا نسخه جدیدی از روش بهینه‌سازی صفحات شیب‌دار با قابلیت متغیر‌بودن عوامل جستجو در آن، معرفی می‌شود؛ سپس قابلیت به‌وجود‌آمده، برای تعیین نوع و مکان توابع عضویت و آموزش هم‌زمان یک طبقه‌بند مبتنی بر سامانه استنتاج عصبی–فازی تطبیقی به‌کار بسته می‌شود. نتایج  بر روی  چند پایگاه داده مشهور با تعداد رده‌‌های مرجع متفاوت و طول بردارهای ویژگی مختلف مورد آزمایش قرار گرفته و با نتایج روش پیشنهادی به‌صورت مقایسه‌ای گزارش شده است، این آزمایشات نشان‌دهنده عملکرد بهتر روش پیشنهادی است. 2 ANFIS systems have been much considered due to their acceptable performance in terms of creation of fuzzy classifier and training. One main challenge in designing an ANFIS system is to achieve an efficient method with high accuracy and appropriate interpreting capability. Undoubtedly, type and location of membership functions and the way an ANFIS network is trained are of considerable effect on its performance. Up to present time, related researches have just found type and location of membership functions, and or suggested methods to train these networks. Main reason for lack of simultaneous determination of type and location of membership functions and training an ANFIS network is the length of standard versions of Heuristic methods being fixed. In this paper, a new version of optimization method of inclined planes will be introduced, primarily; while search factors could be variable. Then, achieved capability will be used for specifying type and location of membership functions and simultaneous training of a classifier based on adaptive neuro-fuzzy inference system (ANFIS). The proposed method on five benchmark datasets iris, Breast Cancer, Bupa Liver, Wine and Pima from the UCI database has been tested, which has different number of reference classes, different length of attribute vectors with appropriate complexity. Initially, the accuracy of the test dataset for each of the selected datasets was compared using the standard 10 folded cross validation method using the standardized version of the standard length.Then the same experiments were repeated by the proposed method and the results of applying the proposed method on the five aforementioned datasets were compared with the results of the heuristic methods with the standard length version. The comparative results show that the optimal and intelligent design of ANFIS classifier by variable length heuristics on five well-known datasets yields good and satisfactory results and in each of the five problems it has provided better answers than other design methods in the ANFIS classification system. 113 134 2018/01/32018/02/272017/11/172018/04/232018/06/132017/12/252018/01/212018/02/7 1396/11/18 2018/05/232019/06/192019/03/132019/07/102019/02/132019/02/232019/06/192019/01/26 1397/11/6 امیر سلطانی محبوب Amir Soltany mahboob دانشگاه بیرجند ایران soltany.mahboob@gmail.com سید حمید ظهیری ممقانی Seyed Hamid Zahiri Mamaghani دانشگاه بیرجند ایران shzahiri@yahoo.com Pattern Recognition Classifier adaptive neuro fuzzy inference system variable Length Inclined Planes System Optimization algorithm بازشناسی الگو طبقه‌بندی سامانه استنتاج عصبی – فازی تطبیقی بهینه‌ساز صفحات شیب‌دار با طول متغیر [1] C. C. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller. II,” IEEE Trans. Syst. Man. Cybern., vol. 20, no. 2, pp. 404–418, 1990.##[2] T. J. Ross, “Fuzzy Logic with Engineering Applications: Third Edition”, vol. 222. New Delhi: Tata McGraw-Hill Publishing Company limited, 2010.##[3] S.-H. Zahiri, “Swarm Intelligence and Fuzzy Systems (Computer Science, Technology and App-lications): Seyed-Hamid Zahiri:March 1, 2011,” 2010.##[4] A. Klose and R. Kruse, “Enabling neuro-fuzzy classification to learn from partially labeled data,” in 2002 IEEE World Congress on Computational Intelli-gence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE’02. Proceedings (Cat. No.02CH-37291), vol. 1, pp. 803–808, 2011.##[5] H. Ishibuchi, T. Nakashima, and T. Murata, “Perfor-mance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems,” IEEE Trans. Syst. Man Cybern. Part B, vol. 29, no. 5, pp. 601–618, 1999.##[6] R.Yuhui Shi, Eberhart, and Ch.Yaobin , “Implementation of evolutionary fuzzy systems,” IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 109–119, Apr. 1999.##[7] M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 509–522, 2000.##[8] Ching-Hung Wang, Tzung-Pei Hong, and Shian-Shyong Tseng, “Integrating fuzzy knowledge by genetic algo-rithms,” IEEE Trans. Evol. Comput., vol. 2, no. 4, pp. 138–149, 1998.##[9] Z. Xing, Y. Hou, Z. Tong, and L. Jia, “Construction of Fuzzy Classification System Based on Multi-objective Genetic Algorithm,” in Sixth International Conference on Intelligent Systems Design and Applications, , vol. 2, 2006, pp. 1029–1034.##[10] C. Rania and S. N. Deepa, “PSo with mutation for fuzzy classifier design,” in Procedia Computer Sci-ence, 2010, vol. 2, pp. 307–313.##[11] C. C. Chen, “Design of PSO-based fuzzy classification systems,” Tamkang J. Sci. Eng., vol. 9, no. 1, pp. 63–70, 2006.##[12] H. Alipour, E. K. Asl, M. Esmaeili, and M. Nourhosseini, “ACO-FCR : Applying ACO-Based Algorithms to Induct FCR,” vol. I, 2008.##[13] S.-H. Zahiri and S.-A. Seyedin, “Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers.” Springer, Berlin, Heidelberg, pp. 65–92, 2009.##[14] J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.##[15] S. Kar, S. Das, and P. K. Ghosh, “Applications of neuro fuzzy systems: A brief review and future outline,” Appl. Soft Comput. J., vol. 15, pp. 243–259, 2014.##[16] A. Z. Zangeneh, M. Mansouri, M. Teshnehlab, and A. K. Sedigh, “Training ANFIS system with DE algorithm,” in The Fourth International Workshop on Advanced Computational Intell-igence, 2011, pp. 308–314.##[17] M. Nasiri and K. Faez, “Extracting fetal electro-cardiogram signal using ANFIS trained by genetic algorithm,” in 2012 International Con-ference on Bio-medical Engineering, ICoBE 2012, 2012, pp. 197–202.##[18] A. Sarkheyli, A. M. Zain, and S. Sharif, “Robust optimization of ANFIS based on a new modified GA,” Neurocomputing, vol. 166, no. October, pp. 357–366, 2015.##[19] D. P. Rini, S. M. Shamsuddin, and S. S. Yuhaniz, “Particle swarm optimization for ANFIS inter-pretability and accuracy,” Soft Comput., vol. 20, no. 1, pp. 251–262, Jan. 2016.##[20] D. Karaboga and E. Kaya, “An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training,” Appl. Soft Comput., vol. 49, pp. 423–436, 2016.##[21] K. Thangavel and A. Kaja Mohideen, “Mammogram Classification Using ANFIS with Ant Colony Optimization Based Learning,” Spri-nger Singapore, 2016, pp. 141–152.##[22] E. Ghasemi, H. Kalhori, and R. Bagherpour, “A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting,” Eng. Comput., vol. 32, no. 4, pp. 607–614, 2016.##[23] H. Marzi, A. Haj Darwish, and H. Helfawi, “Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation,” Intell. Autom. Soft Comput., vol. 23, no. 2, pp. 227–234, 2017.##[24] D. Karaboga and E. Kaya, “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey,” Artif. Intell. Rev., pp. 1–31, Jan. 2018.##[25] M. H. Mozaffari, H. Abdy, and S. H. Zahiri, “IPO: An inclined planes system optimization algorithm,” Comput. Informatics, vol. 35, no. 1, pp. 222–240, 2016.##[26] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948, 1995.##[27] T. Bäck, Evolutionary algorithms in theory and practice : evolution strategies, evolutionary pro-gramming, genetic algorithms. Oxford Uni-versity Press, 1996.##[28] R. Chelouah and P. Siarry, “A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions,” J. Heuristics, vol. 6, no. 2, pp. 191–213, 2000.##[29] K. Storn, R.; Price, “Differential evolution - a simple and efficient heuristic for global opti-mization over continuous spaces,” J. Glob. Op-tim. 11 - 1223 731–752.##[30] K. Socha and M. Dorigo, “Ant colony opti-mization for continuous domains,” Eur. J. Oper. Res., vol. 185, no. 3, pp. 1155–1173, 2008.##[31] K. sugun. S.Eswari, P.N.Raghunath, “Ductility perfo-rmance of HyFRC,” Am. J. Appl. Sci., vol. 5, no. 9, pp. 1257–1262, 2008.##[32] K. Bache and M. Lichman, “UCI Machine Learning Repository,” University of California Irvine School of Information, vol. 2008, no. 14/8. 2013.##[33] R. A. FISHER, “THE USE OF MULTIPLE MEA-SUREMENTS IN TAXONOMIC PROB-LEMS,” Ann. Eugen., vol. 7, no. 2, pp. 179–188, 1936.##[34] A. Golgouneh and B. Tarvirdizadeh, “Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals,” Signal Data Process., vol. 15, no. 3, 2018.##[35] J. Mohebbi, M. Moradi, and B. Salami, “Proposed Feature Selection for Dynamic Thermal Management in Multicore Systems,” Signal Data Process., vol. 16, no. 1, 2019.##[1] C. C. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller. II,” IEEE Trans. Syst. Man. Cybern., vol. 20, no. 2, pp. 404–418, 1990.##[2] T. J. Ross, “Fuzzy Logic with Engineering Applications: Third Edition”, vol. 222. New Delhi: Tata McGraw-Hill Publishing Company limited, 2010.##[3] S.-H. Zahiri, “Swarm Intelligence and Fuzzy Systems (Computer Science, Technology and App-lications): Seyed-Hamid Zahiri:March 1, 2011,” 2010.##[4] A. Klose and R. Kruse, “Enabling neuro-fuzzy classification to learn from partially labeled data,” in 2002 IEEE World Congress on Computational Intelli-gence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE’02. Proceedings (Cat. No.02CH-37291), vol. 1, pp. 803–808, 2011.##[5] H. Ishibuchi, T. Nakashima, and T. Murata, “Perfor-mance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems,” IEEE Trans. Syst. Man Cybern. Part B, vol. 29, no. 5, pp. 601–618, 1999.##[6] R.Yuhui Shi, Eberhart, and Ch.Yaobin , “Implementation of evolutionary fuzzy systems,” IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 109–119, Apr. 1999.##[7] M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 509–522, 2000.##[8] Ching-Hung Wang, Tzung-Pei Hong, and Shian-Shyong Tseng, “Integrating fuzzy knowledge by genetic algo-rithms,” IEEE Trans. Evol. Comput., vol. 2, no. 4, pp. 138–149, 1998.##[9] Z. Xing, Y. Hou, Z. Tong, and L. Jia, “Construction of Fuzzy Classification System Based on Multi-objective Genetic Algorithm,” in Sixth International Conference on Intelligent Systems Design and Applications, , vol. 2, 2006, pp. 1029–1034.##[10] C. Rania and S. N. Deepa, “PSo with mutation for fuzzy classifier design,” in Procedia Computer Sci-ence, 2010, vol. 2, pp. 307–313.##[11] C. C. Chen, “Design of PSO-based fuzzy classification systems,” Tamkang J. Sci. Eng., vol. 9, no. 1, pp. 63–70, 2006.##[12] H. Alipour, E. K. Asl, M. Esmaeili, and M. Nourhosseini, “ACO-FCR : Applying ACO-Based Algorithms to Induct FCR,” vol. I, 2008.##[13] S.-H. Zahiri and S.-A. Seyedin, “Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers.” Springer, Berlin, Heidelberg, pp. 65–92, 2009.##[14] J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.##[15] S. Kar, S. Das, and P. K. Ghosh, “Applications of neuro fuzzy systems: A brief review and future outline,” Appl. Soft Comput. J., vol. 15, pp. 243–259, 2014.##[16] A. Z. Zangeneh, M. Mansouri, M. Teshnehlab, and A. K. Sedigh, “Training ANFIS system with DE algorithm,” in The Fourth International Workshop on Advanced Computational Intell-igence, 2011, pp. 308–314.##[17] M. Nasiri and K. Faez, “Extracting fetal electro-cardiogram signal using ANFIS trained by genetic algorithm,” in 2012 International Con-ference on Bio-medical Engineering, ICoBE 2012, 2012, pp. 197–202.##[18] A. Sarkheyli, A. M. Zain, and S. Sharif, “Robust optimization of ANFIS based on a new modified GA,” Neurocomputing, vol. 166, no. October, pp. 357–366, 2015.##[19] D. P. Rini, S. M. Shamsuddin, and S. S. Yuhaniz, “Particle swarm optimization for ANFIS inter-pretability and accuracy,” Soft Comput., vol. 20, no. 1, pp. 251–262, Jan. 2016.##[20] D. Karaboga and E. Kaya, “An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training,” Appl. Soft Comput., vol. 49, pp. 423–436, 2016.##[21] K. Thangavel and A. Kaja Mohideen, “Mammogram Classification Using ANFIS with Ant Colony Optimization Based Learning,” Spri-nger Singapore, 2016, pp. 141–152.##[22] E. Ghasemi, H. Kalhori, and R. Bagherpour, “A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting,” Eng. Comput., vol. 32, no. 4, pp. 607–614, 2016.##[23] H. Marzi, A. Haj Darwish, and H. Helfawi, “Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation,” Intell. Autom. Soft Comput., vol. 23, no. 2, pp. 227–234, 2017.##[24] D. Karaboga and E. Kaya, “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey,” Artif. Intell. Rev., pp. 1–31, Jan. 2018.##[25] M. H. Mozaffari, H. Abdy, and S. H. Zahiri, “IPO: An inclined planes system optimization algorithm,” Comput. Informatics, vol. 35, no. 1, pp. 222–240, 2016.##[26] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948, 1995.##[27] T. Bäck, Evolutionary algorithms in theory and practice : evolution strategies, evolutionary pro-gramming, genetic algorithms. Oxford Uni-versity Press, 1996.##[28] R. Chelouah and P. Siarry, “A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions,” J. Heuristics, vol. 6, no. 2, pp. 191–213, 2000.##[29] K. Storn, R.; Price, “Differential evolution - a simple and efficient heuristic for global opti-mization over continuous spaces,” J. Glob. Op-tim. 11 - 1223 731–752.##[30] K. Socha and M. Dorigo, “Ant colony opti-mization for continuous domains,” Eur. J. Oper. Res., vol. 185, no. 3, pp. 1155–1173, 2008.##[31] K. sugun. S.Eswari, P.N.Raghunath, “Ductility perfo-rmance of HyFRC,” Am. J. Appl. Sci., vol. 5, no. 9, pp. 1257–1262, 2008.##[32] K. Bache and M. Lichman, “UCI Machine Learning Repository,” University of California Irvine School of Information, vol. 2008, no. 14/8. 2013.##[33] R. A. FISHER, “THE USE OF MULTIPLE MEA-SUREMENTS IN TAXONOMIC PROB-LEMS,” Ann. Eugen., vol. 7, no. 2, pp. 179–188, 1936.##[34] گل‌گونه علیرضا، تارویردی‌زاده بهرام. توسعه سامانه مکاترونیکی بلادرنگ سنجش استرس، مبتنی بر سیگنال‌های حیاتی. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۳) :۵۹-۷۴ ##[34] A. Golgouneh and B. Tarvirdizadeh, “Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals,” Signal Data Process., vol. 15, no. 3, 2018.##[35] محبی نجم‌آباد جواد، مرادی مرتضی، سلامی باقر. انتخاب ویژگی پیشنهادی برای مدیریت دمای پویا در سیستم‌های چندهسته‌ای. پردازش علائم و داده‌ها. ۱۳۹۸; ۱۶ (۱) :۱۲۵-۱۴۲##[35] J. Mohebbi, M. Moradi, and B. Salami, “Proposed Feature Selection for Dynamic Thermal Management in Multicore Systems,” Signal Data Process., vol. 16, no. 1, 2019. ##

بازشناسی مرز واژگان در گفتار پیوسته فارسی از روی منحنی زیروبمی Word segmentation in Persian continuous speech using F0 contour 1 1 یکی از فعالیت‌های شناختی پیچیده در چارچوب نظام آوایی زبان این است که اهل زبان قادرند گفتار پیوسته را به‌صورت زنجیره واژگان گسسته درک کنند. یافته‌های پیشین مطالعات آزمایشگاهی بر روی زبان فارسی و دیگر زبان‌ها نشان داده است، در زبان­‌هایی که در آنها تکیه به‌طور ثابت (یا با فراوانی وقوع زیاد) در مرز آغازی یا پایانی واژه قرار می­‌گیرد، شنونده­‌ها از نشانه‌­های آکوستیکی تکیه برای تقطیع گفتار پیوسته به واژگان سازنده آن استفاده می‌­کنند. همچنین، این‌گونه فرض شده است که حضور تکیه در جایگاهی غیر از مرز آغازی یا پایانی واژه مانع از کارکرد مرزنمایی این عامل نوایی می‌شود. در زبان فارسی حضور واژه‌­بست در واژه باعث می­‌شود که تکیه در جایگاهی غیر از پایان واژه واقع شود. پژوهش حاضر با هدف پاسخ‌گویی به یک سؤال اساسی درباره نحوه پردازش درکی گفتار پیوسته فارسی انجام شد: آیا مرز پایانی واژگان (اعم از واژگان حاوی واژه‌­بست و واژگان فاقد واژه‌­بست) با توجه به ساخت نواختی واژگان در دستور واجی آهنگ فارسی برای شنونده قابل شناسایی است؟ برای این منظور دوآزمایش شنیداری انجام شد. نتایج این آزمایش‌­ها نشان داد که شنونده هر نقطه پایانی H (در یک گستره نواختی H) در منحنی آهنگ گفتار فارسی را به‌صورت مرز پایانی یک واژه شناسایی می­‌کند. همچنین نتایج به‌دست‌آمده نشان داد که درک شنیداری الگوی برجستگی نوایی وابسته به محل وقوع قله H تکیه زیروبمی است.   2 Word segmentation in continuous speech is a complex cognitive process. Previous research on spoken word segmentation has revealed that in fixed-stress languages, listeners use acoustic cues to stress to de-segment speech into words. It has been further assumed that stress in non-final or non-initial position hinders the demarcative function of this prosodic factor. In Persian, stress is retracted to a non-final position in words containing enclitic affixes. The present research explores the question as to whether Persian listeners are able to identify word boundaries given the tonal structure of words in Persian phonology or not. The paper was also intended to investigate to what extent Persian native speakers use H peaks to identify word stress pattern. Two perceptual experiments were conducted in this regard. Given the tonal structure of words in utterance non-final position in Persian, it was hypothesized that listeners are likely to identify the end of a high plateau as a cue to word boundary. In addition, given that peaks in utterance non-final position are delayed, it was further hypothesized that perceived prominent is likely to be attributed to a syllable that precedes another syllable carrying a pitch peak. The basic stimulus for the first experiment was a nonsense sequence of nine “dA” syllables with equal duration ([dA1.dA2.dA3.dA4.dA5.dA6.dA7.dA8.dA9]) across the syllables. The peak was located at the beginning of the consonant in [dA4] in the stimulus. The duration of the H plateau following the H peak was varied continuously to create 6 different stimuli with varying temporal plateau. The stimuli were presented randomly to 10 native speakers of Persian. The participants were asked to chunk the sequence of identical syllables they hear into two parts as if they were two independent words. They were also asked to identify the most prominent syllable in a separate identification test. The results showed that the ending point of a high H plateau acts as a prosodic cue to word boundary detection in Persian. For example, when the end of the H plateau was located on the end of the vowel in dA4, listeners identified the end of dA4 as boundary between two hypothetical words. However, when the end of the plateau was located on the end of the vowel in dA5 or the beginning of the consonants in .dA6 listeners identified the end of dA5 as the word final boundary. The results of this experiment further revealed that listeners are sensitive to the position of H peaks to identify within-word position of prominence in Persian. Listeners consistently identified dA3 as the most prominent syllable as this syllable preceded dA4 on which the peak was located, and the rate of their identification was not affected by the duration of H plateau following the pitch peak. In the second experiment, listeners’ ability to use F0 contour as a cue to word boundary was tested on resynthesized speech in which the spectral properties of the signals were intentionally deformed. The results replicated the findings previously obtained for the first experiment, indicating that the end of a high plateau acts as a robust cue to word boundary detection in Persian. 135 150 2018/01/32018/02/272017/11/172018/04/232018/06/132017/12/252018/01/212018/02/72018/04/4 1397/1/15 2018/05/232019/06/192019/03/132019/07/102019/02/132019/02/232019/06/192019/01/262020/01/22 1398/11/2 وحید صادقی Vahid sadeghi دانشگاه بین‌المللی امام خمینی ایران vsadeghi5603@gmail.com word boundary intonation prosodic prominence H plateau position of H peaks مرز واژه آهنگ گفتار برجستگی نوایی گستره نواختی H محل وقوع قله [1] V. Sadeghi, “The role of prosodic cues in disam-biguting Persian expressions”, Journal of Lingu-siatic Researches, 1(2), 2012.##[2] V. Sadeghi, “Design and Evaluation of a Persian TTS system using prosodically-sensitive conca-tenative units”, Journal of Signal and Data Processing, vol.2, no.14, pp.69-84, 2010. ##[3] V. Sadeghi, Persian prosodic structure, The Iranian Center for Research and Development in the Humanities, 2018. ##[4] M. Mohammadi and M. Bijankhan, “The study of children’s cognitive processes in spoken word recognition”, Recent Findings in Cognitive Sci-ences, vol.10, 2001, pp.15-20. ##[5] P. Barbosa, “Prominence- and boundary-related acoustic correlations in Brazilian Portuguese read and spontaneous speech”, Proceedings of Speech Prosody, pp. 257-260, 2008. Campinas, Brazil. ##[6] P. Boersma, & D. Weenink, “Praat: doing phonetics by computer [Computer program]”, Version 4.3.01, Retrived from http://www.pra-at.org/, 2010. ##[7] A. Cutler, “Lexical stress”, In D. B. Pisoni & R. E. Remez (Eds.), The handbook of speech perce-ption (pp. 264-289). Oxford, UK: Blackwell, 2005. ##[8] A. Cutler and S. Butterfield, “Rhythmic cues to speech segmentation”, Journal of Memory and Language, vol. 31, pp.218-236, 1992 DOI: 10.1016/0749-596x(92)90012-m ##[9] A. Cutler and D. M. Carter, “The predominance of strong syllables in English vocabulary” Com-puter Speech and Language, vol.2, pp.133-142, 1987. ##[10] A. Cutler and J. M. McQueen, “How prosody is both mandatory and optional,” in Above and Beyond the Segments: Experimental Lingui-stics and Phonetics, J. Casper, Y. Chen, W. Heeren, J. Pacilly, N. O. Schiller, and E. van Zanten (eds.). Amsterdam: Benjamins, pp. 71–82, 2014. ##[11] A. Golard, E. Sommerfield, and F. Kugler, “Prosodic cue weighting in disambiguation: Case Ambiguity in German”, The Fifth Inter-national Conference on Speech Prosody. Michigan, USA, pp.165-169, 2010.##[12] L. M. Hyman, “Do all languages have word accent?” In van der Hulst, H. (ed.) Word Str-ess: Theoretical and Typological Issues. Cam-bridge: Cambridge University Press, 2014. DOI: 10.1017/cbo9781139600408.004 ##[13] M. H. K. Ip and A. Cutler, “Crosslanguage data on five types of prosodic focus,” Proceedings of Speech Prosody, 2016.##[14] M. H. K. Ip and A. Cutler, “Intonation facilitates prediction of focus even in the pre-sence of lexical tones,” Proceedings of Inter-speech, pp. 1218-1222, 2017.##[15] M. Moneglia, & T. Raso, “Notes on Language into Act Theory (L-AcT)”. In T. Raso & H.R. Mello (eds), Spoken Corpora and Linguistic Studies, Amsterdam/Philadelphia: John Ben-jamins, pp.468–494, 2014.##[16] M. M. Mittman and P. Barbosa, “An automatic speech segmentation tool based on multiple acoustic parameters”, Romance Corpora and Linguistic Studies 3.2, pp.133-147, 2016.##[17] B. Mahjani, “An instrumental study of prosodic features and intonation in Modern Farsi (Persian)”. MS thesis, 2003. Retrieved from: http://www.ling.ed.ac.uk/teaching/postgrad/mscslp/archive/dissertations/20023/behzad_mahjani.pdf.##[18] S. L. Mattys, “Stress versus co-articulation: towards an integrated approach to explicit speech segmentation” Journal of Experimental Psychology, vol.30, pp.397-40, 2004. DOI: 10.1037/0096-1523.30.2.397 ##[19] T. Raso, & H. Mello, “Description, Metho-dology and Theoretical Framework”. In T. Berber Sardinha & T. L. São Bento (eds), Working with Portuguese Corpora. London: Bloomsbury, pp.257–278, 2014.##[20] T. Raso, M. Mittmann, & M. Mendes, “O papel da pausa na segmentação prosódica de corpora de fala. Revista de Estudos da Linguagem” vol.23 (3), pp.883–922, 2015.##[21] N. Sadat-Tehrani, “The Intonational Grammar of Persian”, PhD thesis. University of Mani-toba, 2007.##[22] N. Sadat-Tehrani, “The alignment of L + H* pitch accents in Persian intonation”, Journal of the International Phonetic Association, 39, 205-230., 2009. DOI: 10.1017/s002510030900-3892 ##[23] V. Sadeghi, “The timing of pre-nuclear pitch accents in Persian”, Journal of the Inter-national Phonetic Association, 2017. ##[24] S. M. Spitzer, J. Liss and S. L. Mattys, “Acoustic cues to lexical segmentation: A study of resynthesized speech”, Journal of the Acoustical Society of America, vol.122, pp. 3678- 3687, 2007. DOI: 10.1121/1.2801545 .##[25] J. Vroomen, M. van Zon & B. de Gelder, “Cues to speech segmentation: Evidence from jun-cture misperceptions and word spotting”, Me-mory & Cognition, vol.24, pp.744-55 1996. DOI: 10.3758/bf03201099.##[1] صادقی، وحید، "نقش نشانه های نوایی در ابهام‌زدایی از عبارات مبهم فارسی"، مجله پژوهش‌های زبان‌شناسی، سال چهارم، شماره اول، بهار و تابستان، 1391.##[1] V. Sadeghi, “The role of prosodic cues in disam-biguting Persian expressions”, Journal of Lingu-siatic Researches, 1(2), 2012.##[2] صادقی، وحید، "طراحی و ارزیابی یک سامانۀ بازسازی گفتار به روش هم‌گذاری واحدهای حساس با بافت نوایی"، پردازش علائم و داده‌ها، 2، 14، 69-84، 1389. ##[2] V. Sadeghi, “Design and Evaluation of a Persian TTS system using prosodically-sensitive conca-tenative units”, Journal of Signal and Data Processing, vol.2, no.14, pp.69-84, 2010. ##[3] صادقی، وحید، ساخت نوایی زبان فارسی، سازمان مطالعه و تدوین کتب علوم انسانی دانشگاه‌ها (سمت)، مرکز پژوهش و توسعه علوم انسانی، 1397.##[3] V. Sadeghi, Persian prosodic structure, The Iranian Center for Research and Development in the Humanities, 2018. ##[4] محمدی، مینا و بی‌جن‌خان، محمود، "بررسی فرایندهای شناختی کودکان فارسی زبان در بازشناسی واژگان گفتار"، تازه‌های علوم شناختی، 10، 20-15، 1380.##[4] M. Mohammadi and M. Bijankhan, “The study of children’s cognitive processes in spoken word recognition”, Recent Findings in Cognitive Sci-ences, vol.10, 2001, pp.15-20. ##[5] P. Barbosa, “Prominence- and boundary-related acoustic correlations in Brazilian Portuguese read and spontaneous speech”, Proceedings of Speech Prosody, pp. 257-260, 2008. Campinas, Brazil. ##[6] P. Boersma, & D. Weenink, “Praat: doing phonetics by computer [Computer program]”, Version 4.3.01, Retrived from http://www.pra-at.org/, 2010. ##[7] A. Cutler, “Lexical stress”, In D. B. Pisoni & R. E. Remez (Eds.), The handbook of speech perce-ption (pp. 264-289). Oxford, UK: Blackwell, 2005. ##[8] A. Cutler and S. Butterfield, “Rhythmic cues to speech segmentation”, Journal of Memory and Language, vol. 31, pp.218-236, 1992 DOI: 10.1016/0749-596x(92)90012-m ##[9] A. Cutler and D. M. Carter, “The predominance of strong syllables in English vocabulary” Com-puter Speech and Language, vol.2, pp.133-142, 1987. ##[10] A. Cutler and J. M. McQueen, “How prosody is both mandatory and optional,” in Above and Beyond the Segments: Experimental Lingui-stics and Phonetics, J. Casper, Y. Chen, W. Heeren, J. Pacilly, N. O. Schiller, and E. van Zanten (eds.). Amsterdam: Benjamins, pp. 71–82, 2014. ##[11] A. Golard, E. Sommerfield, and F. Kugler, “Prosodic cue weighting in disambiguation: Case Ambiguity in German”, The Fifth Inter-national Conference on Speech Prosody. Michigan, USA, pp.165-169, 2010.##[12] L. M. Hyman, “Do all languages have word accent?” In van der Hulst, H. (ed.) Word Str-ess: Theoretical and Typological Issues. Cam-bridge: Cambridge University Press, 2014. DOI: 10.1017/cbo9781139600408.004 ##[13] M. H. K. Ip and A. Cutler, “Crosslanguage data on five types of prosodic focus,” Proceedings of Speech Prosody, 2016.##[14] M. H. K. Ip and A. Cutler, “Intonation facilitates prediction of focus even in the pre-sence of lexical tones,” Proceedings of Inter-speech, pp. 1218-1222, 2017.##[15] M. Moneglia, & T. Raso, “Notes on Language into Act Theory (L-AcT)”. In T. Raso & H.R. Mello (eds), Spoken Corpora and Linguistic Studies, Amsterdam/Philadelphia: John Ben-jamins, pp.468–494, 2014.##[16] M. M. Mittman and P. Barbosa, “An automatic speech segmentation tool based on multiple acoustic parameters”, Romance Corpora and Linguistic Studies 3.2, pp.133-147, 2016.##[17] B. Mahjani, “An instrumental study of prosodic features and intonation in Modern Farsi (Persian)”. MS thesis, 2003. Retrieved from: http://www.ling.ed.ac.uk/teaching/postgrad/mscslp/archive/dissertations/20023/behzad_mahjani.pdf.##[18] S. L. Mattys, “Stress versus co-articulation: towards an integrated approach to explicit speech segmentation” Journal of Experimental Psychology, vol.30, pp.397-40, 2004. DOI: 10.1037/0096-1523.30.2.397 ##[19] T. Raso, & H. Mello, “Description, Metho-dology and Theoretical Framework”. In T. Berber Sardinha & T. L. São Bento (eds), Working with Portuguese Corpora. London: Bloomsbury, pp.257–278, 2014.##[20] T. Raso, M. Mittmann, & M. Mendes, “O papel da pausa na segmentação prosódica de corpora de fala. Revista de Estudos da Linguagem” vol.23 (3), pp.883–922, 2015.##[21] N. Sadat-Tehrani, “The Intonational Grammar of Persian”, PhD thesis. University of Mani-toba, 2007.##[22] N. Sadat-Tehrani, “The alignment of L + H* pitch accents in Persian intonation”, Journal of the International Phonetic Association, 39, 205-230., 2009. DOI: 10.1017/s002510030900-3892 ##[23] V. Sadeghi, “The timing of pre-nuclear pitch accents in Persian”, Journal of the Inter-national Phonetic Association, 2017. ##[24] S. M. Spitzer, J. Liss and S. L. Mattys, “Acoustic cues to lexical segmentation: A study of resynthesized speech”, Journal of the Acoustical Society of America, vol.122, pp. 3678- 3687, 2007. DOI: 10.1121/1.2801545 .##[25] J. Vroomen, M. van Zon & B. de Gelder, “Cues to speech segmentation: Evidence from jun-cture misperceptions and word spotting”, Me-mory & Cognition, vol.24, pp.744-55 1996. DOI: 10.3758/bf03201099. ##

طراحی و آزمایش داشبورد بلادرنگ تحلیل متن شبکه اجتماعی توییتر Design and Test of the Real-time Text mining dashboard for Twitter 1 1 یکی‌ از مباحث پژوهشی‌ مهم امروز در حوزه فناوری اطلاعات و فناوری استفاده از دانش نهفته در داده­هایی است که امروزه با سرعت بالا، حجم زیاد و با تنوع فراوان در فرمت داده تولید می­شوند. داده­هایی با چنین ویژگی­هایی را کلان‌داده می­نامند. استخراج، پردازش و بصری‌سازی نتایج حاصل از کلان‌داده امروزه به یکی‌ از دغدغه­های دانشمندان علم داده تبدیل شده است. گفتنی است که امروزه زیر ساخت‍‌ها، روش‌­ها و ابزارهای بسیاری برای تحلیل کلان‌داده توسعه یافته‌­اند. هدف این مقاله ارائه راهکاری برای استخراج و بصری­سازی داده‌های شبکه اجتماعی توییتر به‌صورت بلادرنگ با حذف پایگاه‌­های داده به‌عنوان نمونه‌­ای از تحلیل کلان‌داده است. در این پژوهش یکی‌ از راه حل‌های بصری سازی‌ بلادرنگ، با استفاده از داده­‌های توییتر به‌عنوان جریان ورودی، از آپاچی استورم به‌عنوان پلتفرم پردازشی و از D3.jsبرای نمایش داده­ها ارائه خواهد شد؛ در‌نهایت داشبورد طراحی شده با استفاده از روش­ طراحی آزمایش‌ها و آزمون­‌های آماری از نظر زمان طی‌شده برای پاسخ (Latency). در انواع پیکره­‌بندی‌های مختلف آپاچی استورم مورد ارزیابی قرار گرفته­ و در‌نهایت بلادرنگ‌بودن با میانگین زمان پاسخ برابر یک دقیقه و سی ثانیه تأیید شد. 2 One of today's major research trends in the field of information systems is the discovery of implicit knowledge hidden in dataset that is currently being produced at high speed, large volumes and with a wide variety of formats. Data with such features is called big data. Extracting, processing, and visualizing the huge amount of data, today has become one of the concerns of data science scholars. The impact of big data on information analysis can be traced to four different parts. The first part is data extraction and processing, the second part is data analysis, the third part is data storage, and finally the visualization of the data. In the field of big data processing, in various studies, different categories have been presented. For example, in the studies of Hashim et al., big data processing is divided into two categories. These two types are: batch and real time. These two categories of processing, which nowadays are standard in any comprehensive big data solution, also have been introduced in Abawajy studies: batch processing is related to offline processing, and real-time processing is usually used to analyze the streaming data without any need to storage of data on disk. As data flows from various sources, the data is analyzed and processed real time, for immediate insight. As today's world is rapidly changing and survival in today's competitive world requires instant decision-making based on flows of data, streaming data analysis is becoming increasingly important. On the other hand, one of the great valuable sources of streaming data is the data generated by social networks’ users such as Twitter. Social networks data sources are very rich sources for analysis as they come from the opinions and opinions of their users. As discussed earlier, and since previous studies such as Flash's studies have focused more on batch analysis (offline data), this study has attempted to investigate a variety of tools and infrastructures related to big streaming data, and finally design a real-time dashboard based on Twitter social network streaming data. The following article addresses two research questions: 1) How to design and implement a real-time dashboard based on social networks data? 2) Which different configurations are best suited for real-time dashboard analysis and visualization? In other words, the purpose of this article is to provide a solution for extracting and visualizing Twitter's social network streaming data by deleting databases, as an examples of big data real time analysis. In this research, we used Twitter streaming data as an input, Apache Storm as a processing platform and D3.js as a visualization tool. Finally, the designed dashboard was evaluated using Design of Experiment method and other statistical tests in various types of Apache Storm configurations and eventually it was proved that the dashboard is real time with an average response time for 1 minute and 30 seconds. 151 164 2018/01/32018/02/272017/11/172018/04/232018/06/132017/12/252018/01/212018/02/72018/04/42018/01/4 1396/10/14 2018/05/232019/06/192019/03/132019/07/102019/02/132019/02/232019/06/192019/01/262020/01/222019/10/5 1398/7/13 سعید روحانی Saeed Rouhani دانشگاه تهران ایران SRouhani@ut.ac.ir طاهره پزشکی Tahereh Pezeshki دانشگاه تهران ایران nasim.pezeshki@gmail.com بابک سهرابی Babak Sohrabi دانشگاه تهران ایران bsohrabi@ut.ac.ir Big data visualization real time dashboard کلان‌داده بصری‌سازی داشبورد بلادرنگ [1] D. Loshin, Big Data Analytics, From Strategic Planning to Enterprise Integration With Tools, Techniques, NoSQL, and Graph, translate by Rouhani, Saeed; Hosseini, Somayeh, 1394. Big data analysis; roadmap for implementation; technology and tools.##[2] T. Kumamoto, H. Wada, T.Suzuki, "Proposal of a system for visualizing temporal changes in impressions from tweets", International Journal of Pervasive Computing and Communications, Vol. 11, Iss .2, pp. 193 – 211, 2015.##[3] X.Wu, X.Zhu, G.Wu, W.Ding, "Data mining with big data." IEEE transactions on knowledge and data engineering, vol.26(1), pp. 97-107, 2014.##[4] C.Snijders, U.Matzat, U. D.Reips, " Big Data: big gaps of knowledge in the field of internet science", International Journal of Internet Sci-ence, vol.7 (1), pp.1-5, 2012.##[5] L.Zhang, A.Stoffel, M.Behrisch, S. Mittelstadt, T. Schreck, R.Pompl, S.Weber, H.Last, D.Keim, "Visual analytics for the big data era—A comparative review of state-of-the-art commercial systems", In Visual Analytics Science and Tech-nology (VAST), IEEE, 2012.##[6] I. Hashem, I.Yaqoob, N. Anuar, S.Mokhtar, A.Gani, K.Khan, "The rise of “big data” on cloud computing: Review and open research issues", Information Systems, 47: 98-115, 2015.##[7] J.Abawajy, "Comprehensive analysis of big data variety landscape", International journal of parallel, emergent and distributed systems, vol.30 (1): pp.5-14, 2015.##[8] M. R.Bendre, V. R.Thool, "Analytics, challenges and applications in big data environment: a survey", Journal of Management Analytics, vol. 3(3), pp.206-239, 2016.##[9] B.Hu, "A Key-Value Based Application Platform for Enterprise Big Data", in IEEE International Congress on Big Data (BigData Congress), 2014.##[10] B. G.Tudorica, C.Bucur, "A comparison between several NoSQL databases with comments and notes", In Roedunet International Conference (RoEduNet), 2011, 10th, pp. 1-5.##[11] R.Cattell, "Scalable SQL and NoSQL data stores," Acm Sigmod Record, 39(4), pp. 12-27, 2011.##[12] R. Hecht, S.Jablonski, "NoSQL evaluation: A use case oriented survey," In Cloud and Service Computing (CSC), 2011 International Con-ference on 2011, 2011,pp. 336-341.##[13] N.Leavitt, "Will NoSQL databases live up to their promise?" Computer, 43(2), 2010.##[14] A.Corbellini, C.Mateos, A.Zunino, D.Godoy, S.Schiaffino, “Persisting big-data: The NoSQL landscape," Information Systems, vol.63, pp. 1-23, 2017.##[15] W.Ding, G.Marchionini, "A comparative study of web search service performance,” In Proceedings of the ASIST Annual Meeting, Vol. 33, pp. 136-140, Learned Information, 1996.##[16] X.Jin, B.W. Wah, X.Cheng, Y.Wang, "Significance and challenges of big data re-search," Big Data Research, vol. 2(2), pp. 59-64, 2015.##[17] R.Agrawal, A.Kadadi, X.Dai, & F.Andres, "Challenges and opportunities with big data visualization." In Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems ,pp. 169-173, ACM, 2015.##[18] D.Gruhl, M.Nagarajan, J.Pieper , C.Robson, A.Sheth, "Multimodal social intelligence in a real-time dashboard system," The VLDB Journal—The International Journal on Very Large Data Bases, vol.19 (6), pp. 825-848, 2010.##[19] B.Flesch, "Design, Development and Evaluation of a Big Data Analytics Dashboard", M.S. thesis, Copenhagen Business School, Denmark, 2014.##[20] O.Hoeber , M.Meseery , K.Kenneth Odoh , Gopi, "Visual Twitter Analytics (Vista) Temporally changing sentiment and the discovery of emergent themes within sport event tweets." Online Information Review, vol.40 (1), pp.25-41, 2016.##[21] A.Weiler, M.Grossniklaus, M. H. Scholl, "Situation monitoring of urban areas using social media data streams," Information Systems-, vol.57, pp.129-141, 2016.##[22] B.Yadranjiaghdam, "Developing a Real-time Data Analytics Framework For Twitter Streaming Data.", M.S. thesis, Dept.Computer Science, East Carolina Univ., USA, 2016.##[23] B.Yadranjiaghdam, N.Pool, N.Tabrizi, "A Survey on Real-Time Big Data Analytics: Applications and Tools," In Computational Science and Com-putational Intelligence (CSCI), International Conference on, IEEE, 2016.##[24] P.Córdova, "Analysis of real time stream processing systems considering latency", 2015, Available: www.datascienceassn.org##[1] لوشین، دیوید، تحلیل‌های کلان‌داده؛ نقشه راه پیاده‌سازی، فناوری و ابزارها، ترجمه روحانی، سعید؛ حسینی، سمیه، نشر نیاز دانش،1394. ##[1] D. Loshin, Big Data Analytics, From Strategic Planning to Enterprise Integration With Tools, Techniques, NoSQL, and Graph, translate by Rouhani, Saeed; Hosseini, Somayeh, 1394. Big data analysis; roadmap for implementation; technology and tools.##[2] T. Kumamoto, H. Wada, T.Suzuki, "Proposal of a system for visualizing temporal changes in impressions from tweets", International Journal of Pervasive Computing and Communications, Vol. 11, Iss .2, pp. 193 – 211, 2015.##[3] X.Wu, X.Zhu, G.Wu, W.Ding, "Data mining with big data." IEEE transactions on knowledge and data engineering, vol.26(1), pp. 97-107, 2014.##[4] C.Snijders, U.Matzat, U. D.Reips, " Big Data: big gaps of knowledge in the field of internet science", International Journal of Internet Sci-ence, vol.7 (1), pp.1-5, 2012.##[5] L.Zhang, A.Stoffel, M.Behrisch, S. Mittelstadt, T. Schreck, R.Pompl, S.Weber, H.Last, D.Keim, "Visual analytics for the big data era—A comparative review of state-of-the-art commercial systems", In Visual Analytics Science and Tech-nology (VAST), IEEE, 2012.##[6] I. Hashem, I.Yaqoob, N. Anuar, S.Mokhtar, A.Gani, K.Khan, "The rise of “big data” on cloud computing: Review and open research issues", Information Systems, 47: 98-115, 2015.##[7] J.Abawajy, "Comprehensive analysis of big data variety landscape", International journal of parallel, emergent and distributed systems, vol.30 (1): pp.5-14, 2015.##[8] M. R.Bendre, V. R.Thool, "Analytics, challenges and applications in big data environment: a survey", Journal of Management Analytics, vol. 3(3), pp.206-239, 2016.##[9] B.Hu, "A Key-Value Based Application Platform for Enterprise Big Data", in IEEE International Congress on Big Data (BigData Congress), 2014.##[10] B. G.Tudorica, C.Bucur, "A comparison between several NoSQL databases with comments and notes", In Roedunet International Conference (RoEduNet), 2011, 10th, pp. 1-5.##[11] R.Cattell, "Scalable SQL and NoSQL data stores," Acm Sigmod Record, 39(4), pp. 12-27, 2011.##[12] R. Hecht, S.Jablonski, "NoSQL evaluation: A use case oriented survey," In Cloud and Service Computing (CSC), 2011 International Con-ference on 2011, 2011,pp. 336-341.##[13] N.Leavitt, "Will NoSQL databases live up to their promise?" Computer, 43(2), 2010.##[14] A.Corbellini, C.Mateos, A.Zunino, D.Godoy, S.Schiaffino, “Persisting big-data: The NoSQL landscape," Information Systems, vol.63, pp. 1-23, 2017.##[15] W.Ding, G.Marchionini, "A comparative study of web search service performance,” In Proceedings of the ASIST Annual Meeting, Vol. 33, pp. 136-140, Learned Information, 1996.##[16] X.Jin, B.W. Wah, X.Cheng, Y.Wang, "Significance and challenges of big data re-search," Big Data Research, vol. 2(2), pp. 59-64, 2015.##[17] R.Agrawal, A.Kadadi, X.Dai, & F.Andres, "Challenges and opportunities with big data visualization." In Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems ,pp. 169-173, ACM, 2015.##[18] D.Gruhl, M.Nagarajan, J.Pieper , C.Robson, A.Sheth, "Multimodal social intelligence in a real-time dashboard system," The VLDB Journal—The International Journal on Very Large Data Bases, vol.19 (6), pp. 825-848, 2010.##[19] B.Flesch, "Design, Development and Evaluation of a Big Data Analytics Dashboard", M.S. thesis, Copenhagen Business School, Denmark, 2014.##[20] O.Hoeber , M.Meseery , K.Kenneth Odoh , Gopi, "Visual Twitter Analytics (Vista) Temporally changing sentiment and the discovery of emergent themes within sport event tweets." Online Information Review, vol.40 (1), pp.25-41, 2016.##[21] A.Weiler, M.Grossniklaus, M. H. Scholl, "Situation monitoring of urban areas using social media data streams," Information Systems-, vol.57, pp.129-141, 2016.##[22] B.Yadranjiaghdam, "Developing a Real-time Data Analytics Framework For Twitter Streaming Data.", M.S. thesis, Dept.Computer Science, East Carolina Univ., USA, 2016.##[23] B.Yadranjiaghdam, N.Pool, N.Tabrizi, "A Survey on Real-Time Big Data Analytics: Applications and Tools," In Computational Science and Com-putational Intelligence (CSCI), International Conference on, IEEE, 2016.##[24] P.Córdova, "Analysis of real time stream processing systems considering latency", 2015, Available: www.datascienceassn.org ##