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مدل‌سازی چرخه عملیاتی سامانه اسکادا با استفاده از شبکه‌ پتری A Petri-net Model for Operational Cycle in SCADA Systems 1 1 سامانه‌های اسکادا وظیفه کنترل زیرساخت‌های حیاتی را به عهده داشته و مطالعه بر روی آن‌ها از اهمیت فراوانی برخوردار است. در سامانه‌ اسکادا وضعیت سلامت تمامی فرآیندهای صنعتی و تجهیزات میدانی در پست‌های راه دور به‌وسیله رویدادها و هشدارهای دریافتی در مرکز کنترل مورد پایش لحظه‌ای قرار می‌گیرند. اپراتورها با مشاهده رویدادها و هشدارها، دستورهای لازم را به‌منظور مدیریت و حفظ پایداری شبکه صادر می‌کنند. هرگونه تصمیم اشتباه اپراتور برای برطرف‌شدن هشدار می‌تواند موجب ایجاد هشدارهای جدید در شبکه شود. ازآنجاکه هشدارها نقش کلیدی در سامانه اسکادا دارند، مدل‌سازی چرخه عملیاتی سامانه اسکادا برای مدیریت هشدارها تأثیر فراوانی در تحلیل عملکرد اپراتور و بررسی ناهنجاری در شبکه می‌تواند داشته باشد. در این مقاله یک مدل جدید از چرخه عملیاتی سامانه اسکادا از مرحله ایجاد هشدار تا برطرف‌شدن آن توسط اپراتور با استفاده از شبکه‌های پتری رنگی ارائه‌ شده است. به‌منظور ارزیابی مدل پیشنهادی، چند سناریو در چند موردکاوی مختلف بررسی‌شده، و نتایج به‌دست‌آمده نشان می‌دهد که مدل ارائه‌شده از کارایی لازم در شبیه‌سازی رفتارهای پست، شبکه و اپراتور برخوردار است. 2 Supervisory control and data acquisition (SCADA) system monitors and controls industrial processes in critical infrastructures (CIs) and plays the vital role in maintaining the reliability of CIs such as power, oil, and gas system. In fact, SCADA system refers to the set of control process, which measures and monitors sensors in remote substations from a control center. These sensors usually have a type of automated response capability when a certain criteria is met. When an abnormal system status occurs, an alarm signal is raised in control center and as a result the operator will be notified. In this way, all normal and abnormal system statuses are monitored in control center. In CI’s application, since several substation resources and their related sensors are too high (because the CI’s grid is often large, complex and wide), the number of alarms is very high. It gets worse when the operator mistakes and as a result, cascading alarms are flooded. In this condition, the rate of raising alarms may be more than clearing them. In SCADA system, alarm clearing is one of the main duties of operators. When an alarm is raised in control center, the operator should clear it as soon as possible. However, the recent reports confirm the poor alarm clearing causes accidents in the SCADA system. As any operator mistake can increase the number of alarms and jeopardize the system reliability, alarms processing and decision-making for clearing them are a stressful and time-consuming for the SCADA operators. In a large and complex CI such as power system, when operators are overwhelmed by the system alarms, they may take wrong decisions and even ignore alarms. Alarm flooding, lots of operator’s workload and his/her fatigue as a result, are the main causes of operator’s mistake. If generating of an alarm in a remote substation is denoted as an operational cycle in an SCADA system until clearing it by the operator in control center, the aim of this paper is modeling the operational cycle by using colored petri nets. The proposed model is based on a general approach which alarm messages are integrated with the operator’s commands. Of course, the model focuses on generating of alarms by substation resources. To verify the proposed model, a real data set of power system of Iran is used and to demonstrate the potential of the proposed model some scenarios about operator’ workload and alarm flooding are simulated. 14 3 2018/09/15 1397/6/24 2019/09/2 1398/6/11 پیام محمودی نصر Payam Mahmoudi Nasr دانشگاه مازندران ایران p.mahmoudi@umz.ac.ir Alarm Modeling Operational cycle Petri nets SCADA چرخه عملیاتی هشدار مدل‌سازی اسکادا شبکه‌های پتری [1] J. Wang, F. Yang, T. Chen, and S. L. Shah, "An overview of industrial alarm systems: main causes for alarm overloading, research status, and open problems," IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 1045-1061, 2016.##[2] D. Li, J. Hu, H. Wang, and W. Huang, "A distributed parallel alarm management strategy for alarm reduction in chemical plants," Journal of Process Control, vol. 34, pp. 117-125, 2015.##[3] Y. Wu, M. Kezunovic, and T. Kostic, "An advanced alarm processor using two-level processing structure," in Power Tech, 2007 IEEE Lausanne, 2007, pp. 125-130.##[4] P. Mahmoudi-Nasr and A. Yazdian-Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System", JSDP, vol. 14 (4), pp. 3-18, 2018.##[5] P. Mahmoudi-Nasr and A. Yazdian-Varjani, "Toward Operator Access Management in SCADA System: Deontological Threat Mitigation," IEEE Transactions on Industrial Informatics, vol. 14, pp. 3314-3324, 2018.##[6] P. T. Bullemer, M. Tolsma, D. Reising, and J. Laberge, "Towards improving operator alarm flood responses: alternative alarm presentation techniques," Abnormal Situation Management Consortium, 2011.##[7] S. Charbonnier, N. Bouchair, and P. Gayet, "Fault template extraction to assist operators during industrial alarm floods," Engineering Applications of Artificial Intelligence, vol. 50, pp. 32-44, 2016.##[8] R. R. R. Barbosa, R. Sadre, and A. Pras, "Exploiting traffic periodicity in industrial control networks," International journal of critical infrastructure protection, vol. 13, pp. 52-62, 2016.##[9] M. Kezunovic and Y. Guan, "Intelligent alarm processing: From data intensive to information rich," in System Sciences, 2009. HICSS'09. 42nd Hawaii International Conference on, 2009, pp. 1-8.##[10] S. Khanmohammadi, K. Rezaie, J. Jassbi, and S. Tadayon, "A model of the failure detection based on fuzzy inference system for the control center of a power system," Appl. Math. Sci, vol. 6, pp. 1747-1758, 2012.##[11] V. Calderaro, C. N. Hadjicostis, A. Piccolo, and P. Siano, "Failure identification in smart grids based on petri net modeling," IEEE Transactions on Industrial Electronics, vol. 58, pp. 4613-4623, 2011.##[12] Y. Guan and M. Kezunovic, "Contingency-based nodal market operation using intelligent economic alarm processor," IEEE Transactions on Smart Grid, vol. 4, pp. 540-548, 2013.##[13] D. Hadžiosmanović, D. Bolzoni, and P. H. Hartel, "A log mining approach for process monitoring in SCADA," International Journal of Information Security, pp. 1-21, 2012.##[14] Niroo Research Institue, "Substation automation systems standard (transmission and subtransmission substations), Ministry of Energy of Iran, 2008.##[15] T. M. Chen, J. C. Sanchez-Aarnoutse, and J. Buford, "Petri net modeling of cyber-physical attacks on smart grid," IEEE Transactions on Smart Grid, vol. 2, pp. 741-749, 2011.##[16] C. Fecarotti, J. Andrews, and R. Remenyte-Prescott, "Analysis of the Design, Operation and Maintenance Options to Provide a Fault Tolerant Railway System," in Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment, 2014.##[17] D. C. Montgomery, Introduction to statistical quality control: John Wiley & Sons (New York), 2009.##[18] X. Dong, K. Hopkinson, X. Tong, X. Wang, and J. Thorp, "IP-based communication systems for wide-area frequency stability predictive control," in Critical Infrastructure (CRIS), 2010 5th International Conference on, 2010, pp. 1-7.##[19] J. Zhao, Y. Xu, F. Luo, Z. Dong, and Y. Peng, "Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation," Information Sciences, vol. 275, pp. 13-29, 2014.##[20] V. Rodrigo, M. Chioua, T. Hagglund, and M. Hollender, "Causal analysis for alarm flood reduction," IFAC-PapersOnLine, vol. 49, pp. 723-728, 2016.##[21] (2017). http://CPNTools.org##[1] J. Wang, F. Yang, T. Chen, and S. L. Shah, "An overview of industrial alarm systems: main causes for alarm overloading, research status, and open problems," IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 1045-1061, 2016.##[2] D. Li, J. Hu, H. Wang, and W. Huang, "A distributed parallel alarm management strategy for alarm reduction in chemical plants," Journal of Process Control, vol. 34, pp. 117-125, 2015.##[3] Y. Wu, M. Kezunovic, and T. Kostic, "An advanced alarm processor using two-level processing structure," in Power Tech, 2007 IEEE Lausanne, 2007, pp. 125-130.##[4] محمودی نصر. پیام، یزدیان ورجانی. علی, "یک سامانه مدیریت دسترسی برای کاهش تهدیدهای عملیاتی در سامانه اسکادا", پردازش علائم و داده‌ها، دوره 14، شماره 4 - (12-1396).##[4] P. Mahmoudi-Nasr and A. Yazdian-Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System", JSDP, vol. 14 (4), pp. 3-18, 2018.##[5] P. Mahmoudi-Nasr and A. Yazdian-Varjani, "Toward Operator Access Management in SCADA System: Deontological Threat Mitigation," IEEE Transactions on Industrial Informatics, vol. 14, pp. 3314-3324, 2018.##[6] P. T. Bullemer, M. Tolsma, D. Reising, and J. Laberge, "Towards improving operator alarm flood responses: alternative alarm presentation techniques," Abnormal Situation Management Consortium, 2011.##[7] S. Charbonnier, N. Bouchair, and P. Gayet, "Fault template extraction to assist operators during industrial alarm floods," Engineering Applications of Artificial Intelligence, vol. 50, pp. 32-44, 2016.##[8] R. R. R. Barbosa, R. Sadre, and A. Pras, "Exploiting traffic periodicity in industrial control networks," International journal of critical infrastructure protection, vol. 13, pp. 52-62, 2016.##[9] M. Kezunovic and Y. Guan, "Intelligent alarm processing: From data intensive to information rich," in System Sciences, 2009. HICSS'09. 42nd Hawaii International Conference on, 2009, pp. 1-8.##[10] S. Khanmohammadi, K. Rezaie, J. Jassbi, and S. Tadayon, "A model of the failure detection based on fuzzy inference system for the control center of a power system," Appl. Math. Sci, vol. 6, pp. 1747-1758, 2012.##[11] V. Calderaro, C. N. Hadjicostis, A. Piccolo, and P. Siano, "Failure identification in smart grids based on petri net modeling," IEEE Transactions on Industrial Electronics, vol. 58, pp. 4613-4623, 2011.##[12] Y. Guan and M. Kezunovic, "Contingency-based nodal market operation using intelligent economic alarm processor," IEEE Transactions on Smart Grid, vol. 4, pp. 540-548, 2013.##[13] D. Hadžiosmanović, D. Bolzoni, and P. H. Hartel, "A log mining approach for process monitoring in SCADA," International Journal of Information Security, pp. 1-21, 2012.##[14] پژوهشگاه نیرو, "استاندارد سیستم های اتوماسیون پست‌های انتقال و فوق توزیع," شرکت توانیر, 1386.##[14] Niroo Research Institue, "Substation automation systems standard (transmission and subtransmission substations), Ministry of Energy of Iran, 2008.##[15] T. M. Chen, J. C. Sanchez-Aarnoutse, and J. Buford, "Petri net modeling of cyber-physical attacks on smart grid," IEEE Transactions on Smart Grid, vol. 2, pp. 741-749, 2011.##[16] C. Fecarotti, J. Andrews, and R. Remenyte-Prescott, "Analysis of the Design, Operation and Maintenance Options to Provide a Fault Tolerant Railway System," in Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment, 2014.##[17] D. C. Montgomery, Introduction to statistical quality control: John Wiley & Sons (New York), 2009.##[18] X. Dong, K. Hopkinson, X. Tong, X. Wang, and J. Thorp, "IP-based communication systems for wide-area frequency stability predictive control," in Critical Infrastructure (CRIS), 2010 5th International Conference on, 2010, pp. 1-7.##[19] J. Zhao, Y. Xu, F. Luo, Z. Dong, and Y. Peng, "Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation," Information Sciences, vol. 275, pp. 13-29, 2014.##[20] V. Rodrigo, M. Chioua, T. Hagglund, and M. Hollender, "Causal analysis for alarm flood reduction," IFAC-PapersOnLine, vol. 49, pp. 723-728, 2016.##[21] (2017). http://CPNTools.org## ##

ارائه روش‌های جدید الگوی چینش پایلوت به‌منظور بهبود عملکرد سامانه DVB-T Proposed Pilot Pattern Methods for Improvement DVB-T System Performance 1 1 یکی از بلوک­‌های مهم در سامانه DVB-T، بلوک OFDM است. در بلوک OFDM، پایلوت­ها، تخمین کانال و روش‌­های درون‌یابی نقش کلیدی دارند. تعداد پایلوت‌ها در هر سبل OFDM در الگوهای مختلف پایلوت‌ها متفاوت است. در این مقاله روش‌­های الگوی چینش پایلوتی جدیدی ارائه شده تا با استفاده از سه پارامتر احتمال خطا، زمان محاسباتی و تعداد پایلوت‌ها عملکرد سامانه DVB-T بهبود یابد. در این پژوهش بهبود عملکرد با استفاده از روش‌­های مختلف درون‌یابی دوبعدی بررسی شده است. بدیهی است تمام اهداف موردنظر در یک الگو برآورده نمی‌­شود؛ یعنی به‌طور مثال ممکن است، خطا کمتر، اما تعداد پایلوت بیشتر شده باشد؛ بنابراین الگویی را باید پذیرفت که مطابق با هدف مورد نظر باشد. در این پژوهش شش روش درون‌یابی دوبعدیlinear, Nearest-neighbor, spline, Cubic Hermite, cosine  و  low pass  استفاده شده و سه الگوی جدید برای پایلوت‌ها پیشنهاد شده که این سه الگو با الگوهای متداول DVB-T برای چهار کانال مختلف بررسی و برای هر کانال سی روش درون‌یابی آزمایش شده است. چهار کانال استفاده شده عبارتند از کانال سامانه OFDM با نوفه AWGN و سامانه OFDM با نوفه AWGN و محوشدگی، سامانه DVB-T با نوفه AWGN و سامانه DVB-T با نوفه AWGN و محوشدگی. نتایج حاصل از این پژوهش نشان می‌دهد که در بیشتر حالات، روش‌­های درون‌یابی خطی و کسینوسی در بعد دوم بهترین عملکرد را دارند و درون‌یابی نزدیک‌ترین همسایگی در بعد دوم بدترین عملکرد را دارد. درنهایت الگوهای پایلوت پیشنهادی با الگوی پایلوت مرسوم سامانه DVB-T مقایسه و ملاحظه شد الگوهای پایلوت پیشنهادی عملکرد بهتری نسبت به الگوی پایلوت مرسوم سیستم DVB-T دارند. از آن جا که در DVB-T جا‌به‌جایی و سرعت مطرح است، در مرحله دوم این پژوهش روش‌­های درون‌یابی دوبعدی در چند فرکانس داپلر مختلف در سامانه DVB-T با استفاده از الگوی پایلوت آن بررسی شده است. شبیه‌سازی‌ها نشان می‌دهد که در سه فرکانس داپلر صفر، سی و 150 هرتز الگوهای پیشنهادی پایلوت در‌حالی‌که یکی از درون‌یابی‌ها خطی باشد، عملکرد بهتری نسبت به روش متداول در DVB-T دارند. 2 Recently, orthogonal frequency division multiplexing (OFDM) has been extensively used in communications systems to resist channel impairments in frequency selective channels. OFDM is a multicarrier transmission technology in wireless environment that use a large number of orthogonal subcarriers to transmit information. OFDM is one of the most important blocks in digital video broadcast-terrestrial (DVB-T) system. The goal of this paper is comparing the methods of interpolation in OFDM system that not used channel statistics information. Therefore, we used pilots for obtaining the information of channel, and by the method of estimation without use of channel statistics information, the channel primary frequency response estimated in pilot’s frequencies. Pilots, channel estimation and interpolation methods are key roles in the OFDM block. The number of pilots are different in the OFDM symbol for different pilot patterns.  In this article, we proposed three pilot patterns to improve DVB-T system performance. Our criteria for this purpose are error probability, calculation time, and the number of pilots. We have tested the performance improvement by using two-dimensional (2D) interpolation methods. Obviously, we do not obtain all of our requests and requirements via one pilot pattern. For example, the error may be decreases, but the number of pilots is increased. Therefore, we must select the pilot pattern that achieve the most important goal for us. We have applied six interpolation methods, for 2D interpolation, such as linear, nearest-neighbor, spline, cubic Hermite, cosine and low pass interpolations. We have compared three proposed pilot patterns with the conventional DVB-T pilot pattern in four different channels. In each channel, we have tested 30 interpolation methods. The applied channels are OFDM system with AWGN noise, OFDM system with AWGN noise and Rayleigh fading, DVB-T system with AWGN noise and DVB-T system with AWGN noise and Rayleigh fading. We observed that the best performance happens when we use linear interpolation in the first dimension and cosine interpolation in the second dimension of 2D interpolation. In addition, the worst performance will be happened when Nearest-neighbor interpolation is used in the second dimension of 2D interpolation. In the last step, we compared the proposed pilot patterns with the conventional DVB-T pilot pattern in 2D interpolation method that it leads to better performance in DVB-T system. We observed that the proposed pilot patterns have better performance than the conventional DVB-T pilot pattern. In the DVB-T, movement and velocity are very important and considered in this research. In the second step using DVB-T pilot pattern, we compared 2D interpolation methods in some different Doppler frequencies. Simulation results show that at 3 Doppler frequencies, i.e. 0, 30, 150Hz, the proposed schemes with a linear interpolation has better performance than the conventional method in the DVB-T systems. 32 15 2018/09/152017/07/30 1396/5/8 2019/09/22020/06/2 1399/3/13 بهاره خسروانی bahareh Khosravani دانشگاه آزاد اسلامی واحد یادگار امام خمینی (ره) شهرری ایران Bahareh.khosravani@yahoo.com سعید قاضی مغربی saeed ghazi-maghrebi دانشگاه آزاد اسلامی واحد یادگار امام خمینی (ره) شهرری ایران s_ghazi2002@yahoo.com DVB-T Interpolation OFDM Pilot DVB-T OFDM پایلوت و درون‌یابی [1] F. Sanzi and J. Speidel, "An adaptive two-dimensional channel estimator for wireless OFDM with application to mobile DVB-T", IEEE Transactions Broadcasting, Vol. 46, No. 2, pp. 128-133, Jun. 2000.##[2] K. Fazel and J. Kaiser, Multi-Carrier and Spread Spectrum Systems. John Wiley & Sons: England, 2013.##[3] H. Onderj and T. Kratochvil, "DVBT channel coding implementation in MATLAB", Department of Radio Electronics, Brno University of Technology, 2012.##[4] M. Elsharief, M. Abouelatta, Zekry, "Implementing a Standard DVB-T System using MATLAB Simulink", International Journal of Computer Applications (0975 - 8887), Vol. 98, No.5, pp.27-32, July 2014.##[5] F. Kh. Deylamani and S. Gh. Maghrebi," Using WPT as a New Method Instead of FFT for ¬Improving the Performance of OFDM Modu-lation", Journal of Signal and Data Processing, Vol. 16, No. 2, pp. 121-136, 1398.##[6] ETSI EN 300 744, "Digital Video Broadcasting (DVB); Framing structure, channel coding and modulation for digital terrestrial television", 2009.##[7] F. Wang, "Pilot-based channel estimation in OFDM system," University of Toledo, pp. 1-95, May 2011.##[8] J. G. Prokis and M. salehi, "Digital communication," 5th edition 2007, Mc Graw-Hill.##[9] A. Asadi and B. Mozaffari. T, "Channel estimation in OFDM-based systems using wavelet transform", Journal of nonlinear systems in electrical engineering, Vol. 1, No. 1, pp. 22-41, 2013.##[10] https: //en.wikipedia.org / Interpolation.##[11] R. Pulikkoonattu, "Channel estimation in DVB-T and OFDM systems", Mar. 2007.##[12] Zh. IIuaqing and L. Jianbo, "Two-dimension interpolation for channel of DVBT system", International Conference on Computer, Control Engineering, pp.253-256, 2012.##[13] Y. Lee, H. Kim, M. Sung, I. Park, S. Lee, " Noise reduction for channel estimation based on pilot-block averaging in DVB-T receivers ", IEEE, pp. 51-58, Jan. 2006.##[14] P. Dhok, A. Dhanvijay, "A review on digital video broadcasting terrestrial (DVB-T) based OFDM system", International Journal of Engineering and Techniques, Vol. 1, No. 2, Mar - Apr 2015.##[15] A. W. Abobaker, "Study and simulation of DVB-T2 IRD performance for different types of channels", M.S. thesis, Faculty of Electrical and Electronic Engineering University Tun Hussein Onn Malaysia, Jun. 2015.##[16] S. Pathak and H. M. Markandeshwar, "Channel estimation in OFDM systems," International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Vol. 3, No. 3, Mar. 2013.##[17] P. P. Sure, Ch. M. Bhuma, "A survey on OFDM channel estimation techniques based on denoising Filter," International Journal of Elec-tronics & Communication, Vol. 5, No. 5, May 2017.##[18] A. N. Uwaechia and N. M. Mahyuddin, "A Review on Sparse Channel Estimation in OFDM System Using Compressed Sensing," IETE Technical Review, Vol. 34, No. 5, pp. 514-531, 2017.##[19] V. Mathai, K. M. Sagayam, "Comparison and Analysis of channel estimation algorithms in OFDM systems," International Journal of Scientific & technology research, Vol. 2, No. 3, Mar. 2013.##[20] B. Kamislioglu, A. AKBAL, "LSE Channel Estimation and Performance Analysis of OFDM Systems," Turkish Journal of Science & Technology. Vol. 12, No. 2, pp. 53-57, 2017.##[21] A. N. Uwaechia, N. M. Mahyuddin, "A Review on Sparse channel estimation in OFDM system using compressed sensing," IETE Technical Review . Aug. 2016.##[22] P. Sure, Ch. M. Bhuma, "A survey on OFDM channel estimation techniques based on denoising strategies," Engineering Science and Technology, Elsevier, Feb. 2017##[23] S.Z. Seyedsalehi, A.M.Nasrabadi, V.Aboutalebi, "Quadratic b-spline wavelet and committee machine for the p300 detection in Brain computer interface", signal and data processing, Vol.5, No. 2, pp.70-75, 2009.##[24] Y. Asai, J,u. Mashino1, T. Sugiyama1, and M. Katayama, "simple channel tracking scheme using deductive combining for MIMO-OFDM WLANs," IEICE Communications Express, Vol. 6, No.7, pp. 429-434, Jul. 2017.##[25] Y.Kao, H. 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Ghassemian, "Hyperspectral data feature extraction using rational function curve fitting," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 30, No. 01, 1650001, 2016.##[34] M. Beitollahi, SA. Hosseini, "Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression," Iranian Con-ference on Electrical Engineering (ICEE), pp.1967-1972, 2017.##[1] F. Sanzi and J. Speidel, "An adaptive two-dimensional channel estimator for wireless OFDM with application to mobile DVB-T", IEEE Transactions Broadcasting, Vol. 46, No. 2, pp. 128-133, Jun. 2000.##[2] K. Fazel and J. Kaiser, Multi-Carrier and Spread Spectrum Systems. John Wiley & Sons: England, 2013.##[3] H. Onderj and T. Kratochvil, "DVBT channel coding implementation in MATLAB", Department of Radio Electronics, Brno University of Technology, 2012.##[4] M. Elsharief, M. 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نگاشت چرخه McGraw به متدولوژی RUPبرای توسعه نرم‌افزار امن Mapping of McGraw Cycle to RUP Methodology for Secure Software Developing 1 1 امنیت نرم‌افزار از چالش‌های مهم در توسعه نرم‌افزار است. هر روز آسیب‌پذیری‌ها و نفوذهای زیادی در نرم‌افزارهای مشهور گزارش می‌شود. همان‌‌طور که برای حل مشکل بحران نرم‌افزار بحث مهندسی نرم‌افزار مطرح شد، مهندسی نرم‌افزار امن در کاهش چالش­های امنیتی نرم‌افزار مؤثر است. چرخه McGraw  به‌عنوان یکی از ره‌یافت­‌های­ توسعه نرم‌افزار امن‌ تعدادی نقطه تماس امنیت نرم‌افزار را معرفی می­‌کند که شامل مجموعه‌ای از دستورالعمل‌های صریح و مشخص در راستای اِعمال مهندسی امنیت در نیازمندی‌ها، معماری، طراحی، کد‌نویسی، اندازه‌گیری و نگهداری نرم‌افزار است. نقاط تماس امنیت نرم‌افزار برای استفاده در ساخت نرم‌افزار، مستقل از پروسه نرم‌افزاری است و به هر فرآیند تولید نرم‌افزار قابل‌اعمال است. بنابراین، می‌توان با تغییر چرخه توسعه نرم‌افزار مورد نظر و اعمال نقاط تماس، چرخه توسعه نرم‌افزار امن را ایجاد کرد. در این پژوهش، راه‌کاری برای نگاشت چرخه McGraw به متدولوژی RUP؛ به‌عنوان متدولوژی سنگین وزن توسعه نرم‌افزار؛ و تلفیق این دو متدولوژی در راستای ایجاد یک متدولوژی ساده و کارآمد برای توسعه نرم‌افزار امن (که RUPST نام دارد) ارائه و همچنین، فراورده‌های جدید RUP برای توسعه نرم‌افزار امن به تفکیک هر نظم ارائه و چهار نقش جدید نیز برای انجام فعالیت‌های مرتبط با امنیت نرم‌افزار تعریف می‌شود. راه‌کار پیشنهادی در یک پروژه واقعی در شرکت کارخانجات مخابراتی ایران مورد استفاده و ارزیابی قرار گرفت. دست‌آوردها نشان می‌دهد که بهره‌گیری و اجرای صحیح این ره‌یافت توسط توسعه‌دهندگان، به پیاده‌سازی و توسعه امن‌تر و مستحکم­تر نرم‌افزار منجر می‌شود. 2 Designing a secure software is one of the major phases in developing a robust software. The McGraw life cycle, as one of the well-known software security development approaches, implements different touch points as a collection of software security practices. Each touch point includes explicit instructions for applying security in terms of design, coding, measurement, and maintenance of software. Developers are able to provide secure and robust software by applying such touch points. In this paper, we introduce a secure and robust approach to map McGraw cycle to RUP methodology, named RUPST. The traditional form of RUP methodology is revised based on the proposed activities for software security. RUPST adds activities like security requirements analysis, abuse case diagrams, risk-based security testes, code review, penetration testing, and security operations to the RUP disciplines. In this regard, based on RUP disciplines, new touch points of software security are presented as a table. Also, RUPST adds new roles such as security architect and requirement analyzer, security requirement designer, code reviewer and penetration tester which are presented in the form of a table along with responsibilities of each role. This approach introduces new RUP artifacts for disciplines and defines new roles in the process of secure software design. The offered artifacts by RUPST include security requirement management plan, security risk analysis model, secure software architecture document, UMLSec model, secure software deployment model, code review report, security test plan, security testes procedures, security test model, security test data, penetration report, security risks management document, secure installation and configuration document and security audit report. We evaluate the performance of the RUPST in real software design process in comparison to other secure software development approaches for different security aspects. The results demonstrate the efficiency of   the proposed methodology in developing of a secure and robust software. 46 33 2018/09/152017/07/302018/10/22 1397/7/30 2019/09/22020/06/22019/09/2 1398/6/11 کیوان رحیمی زاده Keyvan RahimiZadeh دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر، دانشگاه یاسوج ایران RahimiZadeh@yu.ac.ir محمدعلی ترکمانی MohammadAli Torkamani کارخانجات مخابراتی ایران ایران Torkamani@itmc.ir عباس دهقانی Abbas Dehghani دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر، دانشگاه یاسوج ایران Dehghani@yu.ac.ir Secure software engineering software development lifecycle software design RUP artifact مهندسی نرم‌افزار امن چرخه توسعه نرم‌افزار طراحی نرم‌افزار نقاط تماس فرآورده [1] J. H. Allen, Software security engineering: a guide for project managers. Addison-Wesley, 2008.##[2] M. Eftekhari, M.M. Momenabadi, M. Khamar, "Proposing an evolutionary-fuzzy method for software defects detection", JSDP. Vol. 15, NO. 4, pp.3-16, 2009.##[3] G. McGraw, "Software Security: Building Security in," in 2006 17th International Symposium on Software Reliability Engineer-ing, 2006, pp. 6-16.##[4] M. Howard and S. Lipner, "The security development lifecycle : SDL, a process for developing demonstrably more secure soft-ware", Microsoft Press, 2006.##[5] "Microsoft SDL Process Guidance updates, version 5.2 - Microsoft Security." [Online]. Available: https://www.microsoft.com/security/blog/2012/05/23/now-available-microsoft-sdl-process-guidance-updates-version-5-2/. [Accessed: 14-May-2019].##[6] J. Jürjens, "UMLsec: Extending UML for Secure Systems Development," Springer, Berlin, Heidelberg, 2002, pp. 412-425. DOI:##[7] J. Jürjens, Secure Systems Development with UML. Springer-Verlag Berlin, Heidelberg, 2010.##[8] N. R. Mead, T. Stehney, N. R. Mead, and T. Stehney, "Security quality requirements engineering (SQUARE) methodology," in Proceedings of the 2005 workshop on Software engineering for secure systems building trustworthy applications - SESS '05, 2005, vol. 30, no. 4, pp. 1-7. ##DOI:##[9] N. R Mead, V. Viswanathan, and J. Zhan, "Incorporating security requirements engineering into standard lifecycle processes," International Journal of Security and Its Applications, vol. 2, no. 4, pp. 67-79, 2008. DOI: 10.1109/COMPSAC.2008.85##[10] H. Assal and S. Chiasson, "Security in the Software Development Lifecycle," in Four-teenth Symposium on Usable Privacy and Security, 2018, pp. 281-296.##[11] P. Jaferian, G. Elahi, M. R. A. Shirazi, and B. Sadeghian, "RUPSec: extending business modeling and requirements disciplines of RUP for developing secure systems," in 31st EUROMICRO Conference on Software Engineering and Advanced Applications, 2005, pp.232-239. DOI: 10.1109/EUROMICRO.2005.51##[12] H. Mohd and et al., "A secured e-tendering model based on rational unified process (RUP) approach: inception and elaboration phases," International Journal of Supply Chain Management. Vol. 5, no 4, pp. 114-120, 2016.##[13] H. Belani, Z. Car, and A. Caric, "RUP-based process model for security requirements engineering in value-added service develop-ment," in 2009 ICSE Workshop on Software Engineering for Secure Systems, 2009, pp.54-60.##DOI: 10.1109/IWSESS.2009.5068459##[14] "Microsoft Attack Surface Analyzer. "[Online]. Available: https://www.microsoft.com/en-us/-download/details.aspx?id=24487. [Accessed: 15-May-2019].##[15] "FxCop | Microsoft Docs." [Online]. Available: https://docs.microsoft.com/en-us/previous-versions/dotnet/netframework-3.0/bb429476(v=vs.80). [Accessed: 15-May-2019].##[16] "Microsoft Code Analysis." [Online]. Available: http://microsoft.github.io/CodeAna-lysis/. [Accessed: 15-May-2019].##[17] "Microsoft Anti-Cross Site Scripting Library V4.3 from Official Microsoft Download Center." [Online]. Available: https://www.mic-rosoft.com/en-us/download/details.aspx-?id=43126. [Accessed: 15-May-2019].##[18] "Kali Linux | Penetration Testing and Ethical Hacking Linux Distribution." [Online]. Available: https://www.kali.org/. [Accessed: 15-May-2019].##[19] C. E. de Barros Paes and C. M. Hirata, "RUP Extension for the Development of Secure Systems," in Fourth International Conference on Information Technology (ITNG'07), 2007, pp. 643-652. DOI: 10.1109/ITNG.2007.171##[20] R. Kneuper, "Software Processes in the Software Product Life Cycle," in Software Processes and Life Cycle Models, Cham: Springer International Publishing, 2018, pp. 69-157.##DOI:##[21] Y. Mufti, M. Niazi, M. Alshayeb, and S. Mahmood, "A Readiness Model for Security Requirements Engineering," IEEE Access, vol. 6,pp.28611-28631,2018.##DOI: 10.1109/ACCESS.2018.2840322##[22] C. Gonzalez and E. Liñan, "A Software Engineering Methodology for Developing Secure Obfuscated Software," Springer, Cham, 2020, pp. 1069-1078. DOI: https://doi.or-g/10.1007/978-3-030-12385-7_72##[23] S. K. Jha and R. K. Mishra, "Predicting and Accessing Security Features into Component-Based Software Development: A Critical Survey," Springer, Singapore, 2019, pp. 287-294. DOI:##[24] P. Morrison, D. Moye, R. Pandita, and L. Williams, "Mapping the field of software life cycle security metrics," Information and Software Technology, vol. 102, pp. 146-159, Oct. 2018. DOI: ##https://doi.org/10.1016/j.infsof.2018.05.011##[25] H. Maleki, A. Jamshidi, and M. Mohammadi, "A Framework for Effective Exception Handling in Software Requirements Phase," Springer, Singapore, 2019, pp. 397-411. DOI: ##https://doi.org/10.1007/978-981-10-8672-4_30##[1] J. H. Allen, Software security engineering: a guide for project managers. Addison-Wesley, 2008.##[2] مهدی افتخاری، مریم مجیدی مومن آبادی، مجتبی خمر، "ارائه یک روش فازی-تکاملی برای تشخیص خطاهای نرم‌افزار"، پردازش علائم و داده‌ها. 1397، دوره 15،شماره 4، صفحات 16-3.##[2] M. Eftekhari, M.M. Momenabadi, M. Khamar, "Proposing an evolutionary-fuzzy method for software defects detection", JSDP. Vol. 15, NO. 4, pp.3-16, 2009.##[3] G. McGraw, "Software Security: Building Security in," in 2006 17th International Symposium on Software Reliability Engineer-ing, 2006, pp. 6-16.##[4] M. Howard and S. Lipner, "The security development lifecycle : SDL, a process for developing demonstrably more secure soft-ware", Microsoft Press, 2006.##[5] "Microsoft SDL Process Guidance updates, version 5.2 - Microsoft Security." [Online]. Available: https://www.microsoft.com/security/blog/2012/05/23/now-available-microsoft-sdl-process-guidance-updates-version-5-2/. [Accessed: 14-May-2019].##[6] J. Jürjens, "UMLsec: Extending UML for Secure Systems Development," Springer, Berlin, Heidelberg, 2002, pp. 412-425. DOI:##[7] J. Jürjens, Secure Systems Development with UML. Springer-Verlag Berlin, Heidelberg, 2010.##[8] N. R. Mead, T. Stehney, N. R. Mead, and T. Stehney, "Security quality requirements engineering (SQUARE) methodology," in Proceedings of the 2005 workshop on Software engineering for secure systems building trustworthy applications - SESS '05, 2005, vol. 30, no. 4, pp. 1-7. ##DOI:##[9] N. R Mead, V. Viswanathan, and J. Zhan, "Incorporating security requirements engineering into standard lifecycle processes," International Journal of Security and Its Applications, vol. 2, no. 4, pp. 67-79, 2008. DOI: 10.1109/COMPSAC.2008.85##[10] H. Assal and S. Chiasson, "Security in the Software Development Lifecycle," in Four-teenth Symposium on Usable Privacy and Security, 2018, pp. 281-296.##[11] P. Jaferian, G. Elahi, M. R. A. Shirazi, and B. Sadeghian, "RUPSec: extending business modeling and requirements disciplines of RUP for developing secure systems," in 31st EUROMICRO Conference on Software Engineering and Advanced Applications, 2005, pp.232-239. DOI: 10.1109/EUROMICRO.2005.51##[12] H. Mohd and et al., "A secured e-tendering model based on rational unified process (RUP) approach: inception and elaboration phases," International Journal of Supply Chain Management. Vol. 5, no 4, pp. 114-120, 2016.##[13] H. Belani, Z. Car, and A. Caric, "RUP-based process model for security requirements engineering in value-added service develop-ment," in 2009 ICSE Workshop on Software Engineering for Secure Systems, 2009, pp.54-60.##DOI: 10.1109/IWSESS.2009.5068459##[14] "Microsoft Attack Surface Analyzer. "[Online]. Available: https://www.microsoft.com/en-us/-download/details.aspx?id=24487. [Accessed: 15-May-2019].##[15] "FxCop | Microsoft Docs." [Online]. Available: https://docs.microsoft.com/en-us/previous-versions/dotnet/netframework-3.0/bb429476(v=vs.80). [Accessed: 15-May-2019].##[16] "Microsoft Code Analysis." [Online]. Available: http://microsoft.github.io/CodeAna-lysis/. [Accessed: 15-May-2019].##[17] "Microsoft Anti-Cross Site Scripting Library V4.3 from Official Microsoft Download Center." [Online]. Available: https://www.mic-rosoft.com/en-us/download/details.aspx-?id=43126. [Accessed: 15-May-2019].##[18] "Kali Linux | Penetration Testing and Ethical Hacking Linux Distribution." [Online]. Available: https://www.kali.org/. [Accessed: 15-May-2019].##[19] C. E. de Barros Paes and C. M. Hirata, "RUP Extension for the Development of Secure Systems," in Fourth International Conference on Information Technology (ITNG'07), 2007, pp. 643-652. DOI: 10.1109/ITNG.2007.171##[20] R. Kneuper, "Software Processes in the Software Product Life Cycle," in Software Processes and Life Cycle Models, Cham: Springer International Publishing, 2018, pp. 69-157.##DOI:##[21] Y. Mufti, M. Niazi, M. Alshayeb, and S. Mahmood, "A Readiness Model for Security Requirements Engineering," IEEE Access, vol. 6,pp.28611-28631,2018.##DOI: 10.1109/ACCESS.2018.2840322##[22] C. Gonzalez and E. Liñan, "A Software Engineering Methodology for Developing Secure Obfuscated Software," Springer, Cham, 2020, pp. 1069-1078. DOI: https://doi.or-g/10.1007/978-3-030-12385-7_72##[23] S. K. Jha and R. K. Mishra, "Predicting and Accessing Security Features into Component-Based Software Development: A Critical Survey," Springer, Singapore, 2019, pp. 287-294. DOI:##[24] P. Morrison, D. Moye, R. Pandita, and L. Williams, "Mapping the field of software life cycle security metrics," Information and Software Technology, vol. 102, pp. 146-159, Oct. 2018. DOI: ##https://doi.org/10.1016/j.infsof.2018.05.011##[25] H. Maleki, A. Jamshidi, and M. Mohammadi, "A Framework for Effective Exception Handling in Software Requirements Phase," Springer, Singapore, 2019, pp. 397-411. DOI: ##https://doi.org/10.1007/978-981-10-8672-4_30## ##

طبقه‌بندی تصاویر با استفاده از نمایش تُنُک و تطبیق زیرفضا Image Classification via Sparse Representation and Subspace Alignment 1 1 در بیش‌تر الگوریتم‌های یادگیری ماشین و پردازش تصویر، فرض اولیه بر این است که توزیع احتمال داده‌های آموزشی (دامنه منبع) و آزمایش (دامنه هدف) یکسان است؛ اما در کاربردهای دنیای واقعی، برخی معیارها نظیر حالت تصویر، روشنایی یا کیفیت تصویر، موجب ایجاد اختلاف قابل‌توجهی بین دو مجموعه آموزشی و آزمایش می‌شود. به همین دلیل، اغلب مدل‌های ایجاد‌شده بر روی داده‌های آموزشی عملکرد ضعیفی بر روی داده‌های آزمایش خواهند داشت؛ بااین‌حال‌، روش‌های تطبیق دامنه، راه‌حل بسیار مؤثری برای کاهش اختلاف توزیع بین دامنه‌های آموزشی و آزمایش هستند. در این مقاله یک روش تطبیق دامنه با عنوان نمایش تُنُک و تطبیق زیرفضا (SRSA) پیشنهاد شده است، که با وزن‌دهی مجدد نمونه‌های آزمایش و نگاشت داده‌ها به یک زیرفضای جدید مشکل اختلاف توزیع داده‌ها را به‌خوبی مرتفع می‌سازد. SRSA با استفاده از یک نمایش تُنُک، بخشی از مجموعه داده‌های هدف را که ارتباط قوی‌تری با داده‌های منبع دارند، انتخاب می‌کند؛ علاوه‌بر آن، SRSA با نگاشت داده‌های تُنُک هدف و داده‌های منبع به زیرفضاهای مستقل، اختلاف توزیع آنها را درفضای به‌دست‌آمده کاهش می‌دهد؛ درنهایت با برروی‌هم‌گذاری زیرفضاهای نگاشت‌شده، SRSA اختلاف توزیع بین داده‌های آموزشی و آزمایش را به کمینه می‌رساند. ما روش پیشنهادی خود را با ترتیب‌دادن چهارده آزمایش بر روی پایگاه داده‌های‌ بصری مختلف مورد ارزیابی قرار‌داده و با مقایسه نتایج به‌دست‌آمده، نشان داده‌ایم که SRSA عملکرد بهتری در مقایسه با جدیدترین روش‌های یادگیری ماشین و تطبیق دامنه دارد. 2 Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to describe the hidden semantic information in images, where they assume that the training and test sets are from same distribution. However, due to the considerable difference across the source and target domains result in environmental or device parameters, the traditional machine learning algorithms may fail. Transfer learning is a promising solution to deal with above problem, where the source and target data obey from different distributions. For enhancing the performance of model, transfer learning sends the knowledge from the source to target domain. Transfer learning benefits from sample reweighting of source data or feature projection of domains to reduce the divergence across domains. Sparse coding joint with transfer learning has received more attention in many research fields, such as signal processing and machine learning where it makes the representation more concise and easier to manipulate. Moreover, sparse coding facilitates an efficient content-based image indexing and retrieval. In this paper, we propose image classification via Sparse Representation and Subspace Alignment (SRSA) to deal with distribution mismatch across domains in low-level image representation. Our approach is a novel image optimization algorithm based on the combination of instance-based and feature-based techniques. Under this framework, we reweight the source samples that are relevant to target samples using sparse representation. Then, we map the source and target data into their respective and independent subspaces. Moreover, we align the mapped subspaces to reduce the distribution mismatch across domains. The proposed approach is evaluated on various visual benchmark datasets with 14 experiments. Comprehensive experiments demonstrate that SRSA outperforms other latest machine learning and domain adaptation methods with significant difference. 58 47 2018/09/152017/07/302018/10/222018/08/18 1397/5/27 2019/09/22020/06/22019/09/22019/11/3 1398/8/12 فریماه شرافتی Farimah Sherafati دانشگاه صنعتی ارومیه ایران farimah.sherafati@it.uut.ac.ir جعفر طهمورث نژاد Jafar Tahmoresnezhad دانشگاه صنعتی ارومیه ایران j.tahmores@it.uut.ac.ir Image classification Visual domains adaptation Sparse representation Subspace alignment طبقه‌بندی تصویر تطبیق دامنه‌های بصری نمایش تُنُک تطبیق زیرفضا [1] Y. Zhang, T. Liu, M. Long, and M. I. Jordan, "Bridging Theory and Algorithm for Domain Adaptation", arXiv preprint arXiv:1904.05801, 2019.##[2] S. Rezaei and J. Tahmoresnezhad, "Discriminative and domain invariant subspace alignment for visual tasks", Iran Journal of Computer Science, pp. 1-12, 2019.##[3] W. Kumagai and T. Kanamori, "Risk bound of transfer learning using parametric feature mapping and its application to sparse coding", Machine Learning, pp. 1-34, 2019.##[4] I. Jolliffe "Principal component analysis", Wiley, vol. 2, pp. 433-459, 2002.##[5] M. Jing, J. Li, J. Zhao, and K. Lu, "Learning distribution-matched landmarks for unsupervised domain adaptation", In International conference on database systems for advanced applications, pp. 491-508, 2018.##[6] A. Li, D. Chen, Z. Wu, G. Sun, and K. Lin, "Self-supervised sparse coding scheme for image classification based on low rank represent-tation", PloS one, Vol. 13(6), e0199141, 2018.##[7] A. Mirjalili, V. Abootalebi, M. T. Sadeghi, "mproving the performance of sparse representation-based classifier for EEG classification," JSDP , pp. 43-55, 2015.##[8] X. Li, M. Fang, J. J. Zhang and J. Wu, "Sample selection for visual domain adaptation via sparse coding", Signal Processing: Image Communi-cation, vol. 44, pp. 92-100, 2016.##[9] B. Gong, Y. Shi, F. Sha and K. Grauman, "Geodesic flow kernel for unsupervised domain adaptation", Proceedings of the IEEE Con-ference on Computer Vision and Pattern Recog-nition, pp. 2066-2073, 2012.##[10] S. J. Pan, I. W. Tsang, J. T. Kwok and Q. Yang, "Domain adaptation via transfer component analysis", IEEE Trans. Neural Netw, vol. 22, no. 2, pp. 199-210, 2011.##[11] B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, "Unsupervised visual domain adap-tation using subspace alignment", in Proc. IEEE International Conference on Computer vision, pp. 2960-2967, 2013.##[12] M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, "Scatter component analysis: A unified framework for domain adaptation and domain generalizayion", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2016.##[13] B. Sun and K. Saenko, "Subspace distribution alignment for unsupervised domain adaptation", in Proc. British Machine Vision Conference, 2015.##[14] J. Tahmoresnezhad and S. Hashemi, "Visual domain adaptation via transfer feature learning", Knowledge and Information Systems, vol. 50, no. 2, pp. 585-605, 2017.##[15] X. Li, M. Fang, J. J. Zhang and J. Wu, "Sample selection for visual domain adaptation via sparse coding", Signal Processing: Image Communi-cation, vol. 44, pp. 92-100, 2016.##[16] K. Saenko, B. Kulis, M. Fritz and T. Darrell, "Adapting visual category models to new domains", Proceedings of the European Con-ference on Computer Vision, pp. 213-226, 2010.##[17] G.Griffin, A. Holub and P. Perona, "Caltech-256 object category dataset", Technical Report7694, 2007.##[18] J. J. Hull, "A¬", IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550-554, 1994.##[19] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, "Gradient-based learning applied to document recognition", Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.##[20] L. Duan, D. Xu, I.W. Tsang, "Domain adaptation from multiple sources: a domain-dependent regularization approach", IEEE Trans. Neural Netw. Learn. Syst, vol. 23, no. 3, pp. 504-518, 2012.##[1] Y. Zhang, T. Liu, M. Long, and M. I. Jordan, "Bridging Theory and Algorithm for Domain Adaptation", arXiv preprint arXiv:1904.05801, 2019.##[2] S. Rezaei and J. Tahmoresnezhad, "Discriminative and domain invariant subspace alignment for visual tasks", Iran Journal of Computer Science, pp. 1-12, 2019.##[3] W. Kumagai and T. Kanamori, "Risk bound of transfer learning using parametric feature mapping and its application to sparse coding", Machine Learning, pp. 1-34, 2019.##[4] I. Jolliffe "Principal component analysis", Wiley, vol. 2, pp. 433-459, 2002.##[5] M. Jing, J. Li, J. Zhao, and K. Lu, "Learning distribution-matched landmarks for unsupervised domain adaptation", In International conference on database systems for advanced applications, pp. 491-508, 2018.##[6] A. Li, D. Chen, Z. Wu, G. Sun, and K. Lin, "Self-supervised sparse coding scheme for image classification based on low rank represent-tation", PloS one, Vol. 13(6), e0199141, 2018.##[7] A. Mirjalili, V. Abootalebi, M. T. Sadeghi, "mproving the performance of sparse representation-based classifier for EEG classification," JSDP , pp. 43-55, 2015.##[8] X. Li, M. Fang, J. J. Zhang and J. Wu, "Sample selection for visual domain adaptation via sparse coding", Signal Processing: Image Communi-cation, vol. 44, pp. 92-100, 2016.##[9] B. Gong, Y. Shi, F. Sha and K. Grauman, "Geodesic flow kernel for unsupervised domain adaptation", Proceedings of the IEEE Con-ference on Computer Vision and Pattern Recog-nition, pp. 2066-2073, 2012.##[10] S. J. Pan, I. W. Tsang, J. T. Kwok and Q. Yang, "Domain adaptation via transfer component analysis", IEEE Trans. Neural Netw, vol. 22, no. 2, pp. 199-210, 2011.##[11] B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, "Unsupervised visual domain adap-tation using subspace alignment", in Proc. IEEE International Conference on Computer vision, pp. 2960-2967, 2013.##[12] M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, "Scatter component analysis: A unified framework for domain adaptation and domain generalizayion", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2016.##[13] B. Sun and K. Saenko, "Subspace distribution alignment for unsupervised domain adaptation", in Proc. British Machine Vision Conference, 2015.##[14] J. Tahmoresnezhad and S. Hashemi, "Visual domain adaptation via transfer feature learning", Knowledge and Information Systems, vol. 50, no. 2, pp. 585-605, 2017.##[15] X. Li, M. Fang, J. J. Zhang and J. Wu, "Sample selection for visual domain adaptation via sparse coding", Signal Processing: Image Communi-cation, vol. 44, pp. 92-100, 2016.##[16] K. Saenko, B. Kulis, M. Fritz and T. Darrell, "Adapting visual category models to new domains", Proceedings of the European Con-ference on Computer Vision, pp. 213-226, 2010.##[17] G.Griffin, A. Holub and P. Perona, "Caltech-256 object category dataset", Technical Report7694, 2007.##[18] J. J. Hull, "A¬", IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550-554, 1994.##[19] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, "Gradient-based learning applied to document recognition", Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.##[20] L. Duan, D. Xu, I.W. Tsang, "Domain adaptation from multiple sources: a domain-dependent regularization approach", IEEE Trans. Neural Netw. Learn. Syst, vol. 23, no. 3, pp. 504-518, 2012.## ##

تقارن اطلاعات فرکانسی صدای ریه راست و چپ و تشخیص عفونت در بیماران فیبروز کیستیک Symmetry of Frequency information in Right and Left Lung sound and Infection Detection in Cystic Fibrosis Patients 1 1 بیماری فیبروز کیستیک(‏CF‏ یا ‏Cystic fibrosis‏) شایع‌ترین ‏اختلال چند‌سیستمی است که علت اصلی مرگ و میر ناشی از این بیماری مربوط به ‏عفونت مزمن ریوی و عوارض آن ‏است. حدود60-75% بیماران ‏CF‏ به‌صورت مداوم دچار عفونت ‏سودوموناس می‌شوند؛ لذا بیماران ‏CF‏ باید پیوسته تحت مراقبت پزشک ‏باشند تا در‌صورت بروز عفونت به‌سرعت نسبت به درمان آن ‏اقدام شود. اگر چه کشت خلط یا حلق روش استاندارد تشخیص ‏عفونت است، ولی به‌دست‌آوردن نتیجه آن، زمان‌بر بوده و ‏روشی که وجود عفونت را سریع‌تر تشخیص دهد، باعث ‏سهولت در امر تشخیص و شروع درمان با آنتی‌بیوتیک ‏می‌شود. این مطالعه با هدف استفاده از صدای تنفس بیماران ‏CF‏ برای تشخیص وجود عفونت  و موفقیت درمان انجام شد. به این منظور، تقارن اطلاعات سیگنال صدای ریه ‏راست و چپ در بیماران ‏CF‏ در حالت بدون عفونت، دارای عفونت ‏سودوموناس و نیز پس ‏از ‏درمان عفونت سودوموناس بررسی ‏شد. ابتدا صدای تنفس 34 بیمار CF ثبت و پس از انجام پیش پردازش‌های لازم، 15ویژگی از آنها استخراج و با روش الگوریتم ژنتیک بهترین دسته ویژگی از ویژگی‌‌های به‌دست‌آمده استخراج و با روش کنار‌گذاشتن یک شرکت‌کننده به سه طبقه‌بند ماشین بردار پشتیبان، K نزدیک‌ترین همسایگی و بیزین داده شد. همچنین روش ترکیب سه طبقه‌بند نیز بررسی شد. بهترین نتایج توسط روش ترکیب طبقه‌بندها به‌دست آمد که وجود عفونت با صحت %3/91 و موفقیت درمان با صحت %9/90 تشخیص ‏داده شدند. ‏در این مطالعه برای نخستین‌بار از صدای تنفس بیماران CF برای تشخیص عفونت استفاده شده است. روش پیشنهادی، آسان و در دسترس بوده و می‌تواند در شروع به درمان سریع و پیگیری روند درمان به پزشکان کمک کند. 2 Cystic fibrosis (CF) is the most common autosomal recessive disorder in white skinned individuals. Chronic lung infection is the main cause of mortality in this disease. Approximately 60–75 % of adult CF patients frequently suffer from Pseudomonas aeruginosa (PA) infection that is strongly associated with inflammation, lung destruction, and increased mortality. Therefore, CF patients should be followed up by physicians to diagnose infection in the primary stage, start treatment, and reduce the risk of chronic infection. Although sputum culture is the gold standard for diagnosis of PA infections, a rapid and accurate diagnostic method can facilitate early initiation of appropriate therapy and easy monitoring of the condition. The aim of this study was to diagnose CF patients with infection using their lung sound. In this study, the symmetry of frequency information in right and left lung was investigated in CF patients with positive sputum culture results, negative sputum culture results‎, and patients who underwent treatment with antibiotics. Respiretorysounds were acquired from 34 CF patients (16 female, 18 male) who were being ‎followed-up at the Pediatric Respiratory and Sleep Medicine Research Center of Children's ‎Medical Center. The patient selection was based on their sputum microbiology culture. The selection ‎category was as follows: 12 patients with normal flora culture results and 11 patients with PA ‎infection. Also, respiratory sounds of 11 patients were recorded one month after antibiotic treatment and they used to investigate the effectiveness of the proposed method. In the preprocessing step, cardiac sound was removed, respiratory sound cycles were separated and the signals were divided into 64 milisecond frame and 15 features were extracted from each frame. Differences between these features were computed between right and left lungs for early, middle and late section of the respiratory cycle using the new proposed feature. Then, the best group of features was selected by applying Genetic Algorithm. The selected group of features was fed into Support Vector Machine, K Nearest Neighbor and Naïve Bayesian classifier. Also, an Ensemble classifier was examined. The best result was obtained by Ensemble classifier that diagnosed infection by the accuracy of 91.3% and differentiates a group of CF patients with infection from CF patients who underwent treatment with an accuracy of 90.9%. This study describes a novel method of infection detection in CF patients based only on respiratory sound analysis. The proposed method is a simple and available way for early diagnosis of infection and initiating therapeutic strategies. 70 59 2018/09/152017/07/302018/10/222018/08/182018/09/2 1397/6/11 2019/09/22020/06/22019/09/22019/11/32019/05/7 1398/2/17 آرزو کریمی زاده Arezoo Karimizadeh دانشگاه صنعتی خواجه نصیرالدین طوسی ایران a.karimizadeh@ee.kntu.ac.ir منصور ولی Mansour Vali دانشگاه صنعتی خواجه نصیرالدین طوسی ایران mansour.vali@eetd.kntu.ac.ir محمدرضا مدرسی mohammadreza modaresi دانشگاه علوم پزشکی تهران ایران mr-modaresi@sina.tums.ac.ir Cystic Fibrosis Respiratory Sound Information Symmetry Ensemble classifier فیبروز کیستیک صدای تنفس تقارن اطلاعاتی ترکیب طبقه‌بندها [1] M. A. Koda-Kimble, Koda-Kimble and Young's applied therapeutics: the clinical use of drugs: Lippincott Williams & Wilkins, 2012.##[2] M. modaresi, J. faghihinia, and F baharzadeh, "Cystic Fibrosis Prevalence among a Group of High-Risk Iranian Children ," Journal of Isfahan Medical School, vol. 30, 2012.##[3] N. 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تخمین کمترین تفاوت قابل درک با استفاده از برجستگی بصری در تصاویر Just Noticeable Difference Estimation Using Visual Saliency in Images 1 1 به‌علت وجود برخی محدودیت­های فیزیولوژیکی و فیزیکی مختلف در مغز و چشم، دستگاه بینایی انسان (HVS) قادر به درک برخی تغییرات سیگنال بصری که دامنه آن­ها از یک حد آستانه مشخص (موسوم به آستانه JND) پایین­تر باشند، نیست. در بیش‌تر پژوهش‌های موجود جهت تخمین آستانه JND، حساسیت HVS در تمام صحنه یکسان در نظر گرفته شده و تأثیرات توجه بصری (VA) ناشی از برجستگی بصری (VS) در این پژوهش‌ها لحاظ نشده است. مطالعات مختلف نشان داده­اند که حساسیت بصری در نواحی برجسته که توجه بصری بیشتری را جلب می­‌کنند بیشتر بوده و لذا در آن نقاط آستانه JND پایین­تر است و بالعکس. در این مقاله مدلی محاسباتی برای تخمین JND پیشنهاد می­‌شود که از رابطه بین JND و برجستگی بصری برای بهبود تخمین آستانه JND استفاده می‌­کند. این مدل با استفاده از یک مدل JND یکنواخت کارآمد و با به­‌کارگیری یک تابع مدولاسیون غیر خطی مناسب، آستانه­‌های JND پیکسل­های مختلف در یک تصویر را با توجه به برجستگی بصری آن­ها بهبود می‌دهد. تعیین پارامترهای تابع غیرخطی مدولاسیون در قالب یک مسأله بهینه‌­سازی، مدل‌­سازی می­‌شود که حل آن منجر به یافتن مدل JND بهبودیافته می‌شود. کلید کارآمدی روش پیشنهادی به‌کارگیری سازوکاری است که منجر به استفاده کارآمدتر از برجستگی بصری می­‌شود.آزمایش­‌های انجام‌گرفته نشان‌­دهنده برتری قابل ملاحظه روش پیشنهادی نسبت به روش‌­های مشابه موجود است. 2 Due to some physiological and physical limitations in the brain and the eye, the human visual system (HVS) is unable to perceive some changes in the visual signal whose range is lower than a certain threshold so-called just-noticeable distortion (JND) threshold. Visual attention (VA) provides a mechanism for selection of particular aspects of a visual scene so as to reduce the computational load on the brain. According to the current knowledge, it is believed that VA is driven by “visual saliency”. In a visual scene, a region is said to be visually salient if it possess certain characteristics, which make it stand out from its surrounding regions and draw our attention to it. In most existing researches for estimating the JND threshold, the sensitivity of the HVS has been consider the same throughout the scene and the effects of visual attention (caused by visual saliency) which have been ignored. Several studies have shown that in salient areas that attract more visual attention, visual sensitivity is higher, and therefore the JND thresholds are lower in those points and vice versa. In other words, visual saliency modulates JND thresholds. Therefore, considering the effects of visual saliency on the JND threshold seems not only logical but also necessary. In this paper, we present an improved non-uniform model for estimating the JND threshold of images by considering the mechanism of visual attention and taking advantage of visual saliency that leads to non-uniformity of importance of different parts of an image. The proposed model, which has the ability to use any existing uniform JND model, improves the JND threshold of different pixels in an image according to the visual saliency and by using a non-linear modulation function. Obtaining the parameters of the nonlinear function through an optimization procedure leads to an improved JND model. What make the proposed model efficient, both in terms of computational simplicity, accuracy, and applicability, are: choosing nonlinear modulation function with minimum computational complexity, choosing appropriate JND base model based on simplicity and accuracy and also Computational model for estimating visual saliency  that accurately determines salient areas, Finally, determine the Efficient cost function and solve it by determining the appropriate  objective Image Quality Assessment. To evaluate the proposed model, a set of objective and subjective experiments were performed on 10 selected images from the MIT database. For subjective experiment, A Two Alternative Forced Choice (2AFC) method was used to compare subjective image quality and for objective experiment SSIM and IWSSIM was used. The obtained experimental results demonstrated that in subjective experiment the proposed model achieves significant superiority than other existing models and in objective experiment, on average, outperforms the compared models. The computational complexity of proposed model is also analyzed and shows that it has faster speed than compared models. 84 71 2018/09/152017/07/302018/10/222018/08/182018/09/22018/09/13 1397/6/22 2019/09/22020/06/22019/09/22019/11/32019/05/72019/09/2 1398/6/11 فائزه نعمتی خلیل‌آباد Faezeh Nemati Khalil Abad دانشگاه فردوسی مشهد ایران fa.nematykh@mail.um.ac.ir هادی هادی‌زاده Hadi Hadizadeh دانشگاه صنعتی قوچان ایران h.hadizadeh@qiet.ac.ir عباس ابراهیمی مقدم Abbas Ebrahimi Moghadam دانشگاه فردوسی مشهد ایران a.ebrahimi@um.ac.ir مرتضی خادمی درح Morteza Khademi Darah دانشگاه فردوسی مشهد ایران khademi@um.ac.ir Visual Saliency (VS) Visual Attention (VA) Human Visual System (HVS) Just Noticeable Difference (JND) برجستگی بصری (VS) توجه بصری (VA) دستگاه بینایی مغز (HVS) کمترین تفاوت قابل درک (JND) [1] A. B. Watson, Digital Images and Human Vision. The MIT press, 1993.##[2] F. A. A. Kingdom, Psychophysics: A Practical Introduction. Academic press, 2009.##[3] X. K. Yang, W. S. Lin, Z. K. Lu, E. P. 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ترکیب وزن‌دار خوشه‌بندی‌ها با هدف افزایش صحّت خوشه‌بندی نهایی Weighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering 1 1 با توجه به ماهیت بدون ناظر مسائل خوشه‌بندی و تأثیرگذاری مؤلفه‌های مختلف از جمله تعداد خوشه‌ها، معیار فاصله و الگوریتم انتخابی، ترکیب خوشه‌بندی‌ها برای کاهش تأثیر این مؤلفه‌ها و افزایش صحت خوشه‌بندی نهایی معرفی شده است. در این مقاله، روشی برای ترکیب وزن‌دار خوشه‌بندی‌های پایه با وزن‌دهی به خوشه‌بندی‌ها بر اساس روش AD ارائه شده است. روش AD برای برآورد صحّت انسان‌ها در مسائل جمع­سپاری از هماهنگی یا تضاد بین آرای آنها استفاده می‌کند و با پیشنهاد مدلی احتمالاتی، فرآیند برآورد صحّت را به‌کمک یک فرآیند بهینه‌سازی انجام می‌دهد. نوآوری اصلی این مقاله، تخمین صحت خوشه‌بندی‌های پایه با استفاده از روش AD و استفاده از صحت‌های تخمین زده‌شده در وزن‌دهی به خوشه‌بندی‌های پایه در فرآیند ترکیب است. نحوه تطبیق مسأله خوشه‌بندی به روش برآورد صحّت AD و نحوه استفاده از صحّت‌های برآورد‌شده در فرآیند ترکیب نهایی خوشه‌ها، از چالش‌هایی است که در این پژوهش به آنها پرداخته شده است. چهار روش برای تولید خوشه‌بندی‌های پایه شامل الگوریتم‌های متفاوت، معیارهای فاصله‌ی متفاوت در اجرای k-means، ویژگی‌های توزیع‌شده و تعداد خوشه‌های متفاوت بررسی شده است. در فرآیند ترکیب، قابلیت وزن‌‌دهی به الگوریتم‌های خوشه‌بندی ترکیبی CSPA و HGPA اضافه شده است. نتایج روش پیشنهادی روی سیزده مجموعه داده مصنوعی و واقعی مختلف و بر اساس نُه معیار ارزیابی متفاوت نشان می‌دهد که روش ترکیب وزن‌دار ارائه‌شده در بیش‌تر موارد بهتر از روش ترکیب خوشه‌بندی بدون وزن عمل می‌کند که این بهبود برای روش HGPA نسبت به CSPA بیشتر است. 2 Clustering algorithms are highly dependent on different factors such as the number of clusters, the specific clustering algorithm, and the used distance measure. Inspired from ensemble classification, one approach to reduce the effect of these factors on the final clustering is ensemble clustering. Since weighting the base classifiers has been a successful idea in ensemble classification, in this paper we propose a method to use weighting in the ensemble clustering problem. The accuracies of base clusterings are estimated using an algorithm from crowdsourcing literature called agreement/disagreement method (AD). This method exploits the agreements or disagreements between different labelers for estimating their accuracies. It assumes different labelers have labeled a set of samples, so each two persons have an agreement ratio in their labeled samples. Under some independence assumptions, there is a closed-form formula for the agreement ratio between two labelers based on their accuracies. The AD method estimates the labelers’ accuracies by minimizing the difference between the parametric agreement ratio from the closed-form formula and the agreement ratio from the labels provided by labelers. To adapt the AD method to the clustering problem, an agreement between two clusterings are defined as having the same opinion about a pair of samples. This agreement can be as either being in the same cluster or being in different clusters. In other words, if two clusterings agree that two samples should be in the same or different clusters, this is considered as an agreement. Then, an optimization problem is solved to obtain the base clusterings’ accuracies such that the difference between their available agreement ratios and the expected agreements based on their accuracies is minimized. To generate the base clusterings, we use four different settings including different clustering algorithms, different distance measures, distributed features, and different number of clusters. The used clustering algorithms are mean shift, k-means, mini-batch k-means, affinity propagation, DBSCAN, spectral, BIRCH, and agglomerative clustering with average and ward metrics. For distance measures, we use correlation, city block, cosine, and Euclidean measures. In distributed features setting, the k-means algorithm is performed for 40%, 50%,…, and 100% of randomly selected features. Finally, for different number of clusters, we run the k-means algorithm by k equals to 2 and also 50%, 75%, 100%, 150%, and 200% of true number of clusters. We add the estimated weights by the AD algorithm to two famous ensemble clustering methods, i.e., Cluster-based Similarity Partitioning Algorithm (CSPA) and Hyper Graph Partitioning Algorithm (HGPA). In CSPA, the similarity matrix is computed by taking a weighted average of the opinions of different clusterings. In HGPA, we propose to weight the hyperedges by different values such as the estimated clustering accuracies, size of clusters, and the silhouette of clusterings. The experiments are performed on 13 real and artificial datasets. The reported evaluation measures include adjusted rand index, Fowlkes-Mallows, mutual index, adjusted mutual index, normalized mutual index, homogeneity, completeness, v-measure, and purity. The results show that in the majority of cases, the proposed weighted-based method outperforms the unweighted ensemble clustering. In addition, the weighting is more effective in improving the HGPA algorithm than CSPA. For different weighting methods proposed for HGPA algorithm, the best average results are obtained when we use the accuracies estimated by the AD method to weight the hyperedges, and the worst results are obtained when using the normalized silhouette measure for weighting. Finally, among different methods for generating base clusterings, the best results in weighted HGPA are obtained when we use different clustering algorithms to come up with different base clusterings. 100 85 2018/09/152017/07/302018/10/222018/08/182018/09/22018/09/132017/11/30 1396/9/9 2019/09/22020/06/22019/09/22019/11/32019/05/72019/09/22020/06/8 1399/3/19 صدیقه وحیدی فردوسی Sedigheh Vahidi Ferdosi دانشگاه قم ایران s.vahidi@stu.qom.ac.ir حسین امیرخانی Hossein Amirkhani دانشگاه قم ایران amirkhani@qom.ac.ir Weighted Ensemble Clustering Unsupervised Learning HGPA CSPA AD خوشه‌بندی ترکیبی وزندار یادگیری بدون نظارت HGPA CSPA AD [1] J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques. Elsevier, 2011.##[2] S. Vega-Pons and J. Ruiz-Shulcloper, "a Survey of Clustering Ensemble Algorithms," Int. J. Pattern Recognit. Artif. Intell., vol. 25, no. 03, pp. 337-372, 2011.##[3] J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, "On combining classifiers," IEEE Trans. Pattern Anal. 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Pei, Data mining: concepts and techniques. Elsevier, 2011.##[2] S. Vega-Pons and J. Ruiz-Shulcloper, "a Survey of Clustering Ensemble Algorithms," Int. J. Pattern Recognit. Artif. Intell., vol. 25, no. 03, pp. 337-372, 2011.##[3] J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, "On combining classifiers," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226-239, 1998.##[4] S.-B. Cho and J. H. Kim, "Combining multiple neural networks by fuzzy integral for robust classification," Syst. Man Cybern. IEEE Trans., vol. 25, no. 2, pp. 380-384, 1995.##[5] J. Franke and E. Mandler, "A comparison of two approaches for combining the votes of cooperating classifiers," in Pattern Recognition, 1992. Vol. II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on, 1992, pp. 611-614.##[6] L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Mach. Intell., no. 10, pp. 993-1001, 1990.##[7] S. Hashem and B. Schmeiser, "Improving model accuracy using optimal linear combinations of trained neural networks," Neural Networks, IEEE Trans., vol. 6, no. 3, pp. 792-794, 1995.##[8] J. Kittler, "Improving recognition rates by classifier combination: A theoretical framework," DAC and IS, editors, Progress in Handwriting Recognition, pp. 231-248, 1997.##[9] D. J. Miller and L. Yan, "Critic-driven ensemble classification," Signal Process. IEEE Trans., vol. 47, no. 10, pp. 2833-2844, 1999.##[10] K. W. De Bock, K. Coussement, and D. Van den Poel, "Ensemble classification based on generalized additive models," Comput. Stat. Data Anal., vol. 54, no. 6, pp. 1535-1546, 2010.##[11] C. Domeniconi and M. Al-Razgan, "Weighted cluster ensembles: Methods and analysis," ACM Trans. Knowl. Discov. from Data, vol. 2, no. 4, pp. 17, 2009.##[12] A. Strehl and J. Ghosh, "Cluster ensembles---a knowledge reuse framework for combining multiple partitions," J. Mach. Learn. Res., vol. 3, no. Dec, pp. 583-617, 2002.##[13] H. Amirkhani and M. Rahmati, "Agreement/disagreement based crowd labeling," Appl. Intell., vol. 41, no. 1, pp. 212-222, Jul. 2014.##[14] N. Littlestone and M. K. Warmuth, "The weighted majority algorithm," in Foundations of Computer Science, 1989., 30th Annual Symposium on, 1989, pp. 256-261.##[15] S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised machine learning: A review of classification techniques." Emerging Artificial Intelligence Applications in Computer Engineering, vol. 160, pp. 3-24, 2007.##[16] T. G. Dietterich, "Ensemble methods in machine learning," in International workshop on multiple classifier systems, 2000, pp. 1-15.##[17] T. Windeatt, "Vote counting measures for ensemble classifiers," Pattern Recognit., vol. 36, no. 12, pp. 2743-2756, 2003.##[18] S. Wang, A. Mathew, Y. Chen, L. Xi, L. Ma, and J. Lee, "Empirical analysis of support vector machine ensemble classifiers," Expert Syst. 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مدل‌‌سازی شبکه‌‌های تنظیم ژنی: مدل‌های کلاسیک، اختلال بهینه برای شناسایی شبکه Modeling gene regulatory networks: Classical models, optimal perturbation for identification of network 1 1 ارتقای عمق و گستره درک ما از دانش زیست‏شناسی ملکولی، از یک سو امکان بهره‏‌برداری از آن را در توسعه فناوری­هایی مانند رمزگشایی فراهم ساخته است و از سوی دیگر، مداخله در سیستم ژنتیکی را امکان‏‌پذیر می‏سازد که نویدبخش آینده‏ای روشن برای علوم زیستی و پزشکی است. دست‌‌یابی به این هدف با مداخله در شبکه تنظیم ژنی (GRN) امکان‏پذیر می­شود؛ زیرا GRN کنترل‌کننده فعالیت‏های زیستی در سطح ملکولی است. در این مسیر، شناسایی GRN، شامل شناسایی مرز، ساختار و گره‌­های شبکه اهمیت به‏سزایی دارد. در این مقاله به دو جنبه ساختار و گره در مدل‏سازی و شناسایی GRN در شبکه‏های بزرگ (با بیش از پنجاه گره) پرداخته می‏شود. نخست محدودیت‏‌های کاربست مدل‌های احتمالاتی برای گره (ژن) مورد بررسی قرار می‏‌گیرد. همچنین محدودیت‏‌های کاربست مدل چند-درختی برای ساختار GRN مورد بررسی قرار می‏‌گیرد. در بخش اصلی مقاله، مسأله شناسایی GRN با مدل بولی مورد بحث قرار گرفته و نشان داده می‏‌شود که بر‌خلاف تصور معمول، آزمایش بهینه از دید شناسایی ساختار GRN، آزمایش تک‌اختلال است. 2 Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption.  On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications.  This is not an unrealistic goal since genes which are regulated by gene regulatory networks (GRNs) are the core governors of life processes at the molecular level. In fact, manipulation of GRNs would be the ultimate strategy for optimal purposeful control of cell’s life.  GRNs are in charge of regulating the amounts of all the inter-cellular as well as intra-cellular molecular species produced all the time in all living organisms.  Manipulation of a GRN requires comprehensive knowledge about nodes and interconnections.  This paper deals with both aspects in networks having more than fifty nodes.  In the first part of the paper, restrictions of probabilistic models in modeling node behavior are discussed, i.e.: 1) unfeasibility of reliably predicting the next state of GRN based on its current state, 2) impossibility of modelling logical relations among genes, and 3) scarcity of biological data needed for model identification.  These findings which are supported by arguments from probability theory suggest that probabilistic models should not be used for analysis and prediction of node behavior in GRNs.  Next part of the paper focuses on models of GRN structure.  It is shown that the use of multi-tree models for structure for GRN poses severe limitations on network behavior, i.e. 1) increase in signal entropy while passing through the network, 2) decrease in signal bandwidth while passing through the network, and 3) lack of feedback as a key element for oscillatory and/or autonomous behavior (a requirement for any biological network).  To demonstrate that, these restrictions are consequences of model selection, we use information theoretic arguments.  At the last and the most important part of the paper we look into the gene perturbation experiments from a network-theoretic perspective to show that multi-perturbation experiments are not as informative as assumed so far.  A generally accepted belief among researches states that multi-perturbation experiments are more informative than single-perturbation ones, i.e., multiple simultaneously applied perturbations provide more information than a single perturbation.  It is shown that single-perturbation experiments are optimal for identification of network structure, provided the ultimate goal is to discover correct subnet structures.  112 101 2018/09/152017/07/302018/10/222018/08/182018/09/22018/09/132017/11/302018/10/23 1397/8/1 2019/09/22020/06/22019/09/22019/11/32019/05/72019/09/22020/06/82019/01/26 1397/11/6 رضا بیات Reza Bayat دانشگاه یزد ایران Bayatr@semnan.ac.ir مهدی صادقی Mehdi Sadeghi پژوهشکده زیست‌فناوری پزشکی، پژوهشگاه ملی مهندسی ژنتیک و زیست‌فناوری ایران Sadeghi@nigeb.ac.ir محمد رضا عارف Mohammad Reza Aref دانشگاه صنعتی شریف و دانشگاه یزد ایران Aref@sharif.ac.ir gene regulatory network (GRN) probabilistic model of gene multi-tree model of GRN structure Boolean model of gene optimal perturbation experiment شبکه تنظیم ژنی مدل احتمالاتیِ ژن مدل چند‌درختیِ ساختار مدل بولیِ ژن آزمایش بهینه اختلال [1]G. Battail, "Heredity as an encoded communication process," IEEE Trans. Information Theory, vol. 56, no. 2, pp. 678-687, Feb. 2010.##[2] L. M. Adleman, "Molecular computation of solutions to combinatorial problems," Science, vol. 266, no. 11, pp. 1021-1025, Nov. 1994.##[3] S. A. Salehi, et al, "Computing mathematical functions using DNA via fractional coding," Nature Genetics, May 2018.##[4] S. M. H. Tabatabaei Yazdi, et al, "Mutually uncorrelated primers for DNA-based data storage," IEEE Trans. Information Theory, vol. 64, no. 9, pp. 6283-6296, Sept. 2018.##[5] M. K. Gupta, "Quest for error correction in biology," IEEE Eng. in Medicine and Biology Mag, vol. 26, no. 1, Jan. 2006.##[6] B. Alberts, et al, Molecular biology of the cell, 6th edition, Garland Science, New York, 2014.##[7] S. Das, et al, Handbook of research on computational methodologies in gene regulatory networks, Hershey, New York, 2010.##[8] J. J. Pasternak, An introduction to human molecular genetics: Mechanisms of inherited diseases, 2nd edition, Wiley, New York, 2005.##[9] Beom S. Lee, et al, "A computational algorithm for personalized medicine in schizophrenia," Schizophrenia Research, vol. 192, pp. 131-136, Feb. 2018.##[10] N. Kornienko, et al, "Interfacing nature's catalytic machinery with synthetic materials for semi-artificial photosynthesis," Nature Nanotechnology, vol. 13, pp. 890-899, Oct. 2018.##[11] M.Banf, Seung Y. Rhee, "Computational inference of gene regulatory networks: Approaches, limitations and opportunities," Biochimica et Biophysica Acta, vol. 1860, no. 1, pp. 41-52, Jan 2017.##[12] N. Friedman, et al, "Using Bayesian networks to analyze expression data," J. Computational Biology, vol. 7, no. 3, pp. 601-620, March 2000.##[13] F. Fages, et al, "Influence networks compared with reaction networks: Semantics, expressivity and attractors," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1138 - 1151, July 2018.##[14] Y. Li, "The max-min high-order dynamic Bayesian network for learning gene regulatory networks with time-delayed regulations," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 792-803, July 2016.##[15] H. Chen, et al, "Bayesian data fusion of gene expression and histone modification profiles for inference of gene regulatory network," IEEE/ACM Trans. Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2018.2869590, early access, Sep. 2018.##[16] M. Shi, et al, "Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach," IET Systems Biology, vol. 10, no. 6, pp. 252-59, June 2016.##[17] S. Chan, et al, "Maximum a posteriori probability and time-varying approach for inferring gene regulatory networks from time course gene microarray data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 1, pp. 123 - 135, Jan. 2015.##[18] H. Xu, et al, "Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells," PLoS One Computational Biology, vol. 10, no. 8, pp. 1-14, Aug. 2014.##[19] S. Mehra, W. Hu, G. Karypis, "A Boolean algorithm for reconstructing the structure of regulatory networks," Metabolic Engineering, vol. 6, no. 4, pp. 326-39, Nov. 2004.##[20] S. A. Kauffman, "Metabolic stability and epigenesis in randomly constructed genetic nets, J. of Theoretical Biology, vol. 22, no. 3, pp. 437-67, March 1969.##[21] S. A. Kauffman, "The large-scale structure and dynamics of gene control circuits: an ensemble approach," J. of Theoretical Biology, vol. 44, no. 1, pp. 167-90, March 1974.##[22] J. I. Joo, et al, "Determining relative dynamic stability of cell states using Boolean network mode," Nature Scientific Reports, vol. 8, no. 1, online, Aug. 2018.##[23] www.humancellatlas.org, accessed Sep. 2018.##[24] H. P. Yockey, Information theory, evolution and origin of life, 2nd edition, Cambridge University Press, New York, 2011.##[25] S. L. Salzberg, et al, "Open questions: How many genes do we have?" BMC Biology, vol. 16, no. 1, online, Aug. 2018.##[26] Y. Lee, Qing Zhou, "Co-regulation in embryonic stem cells via context-dependent binding of transcription factors," Bioinformatics, vol. 29, no. 17, pp. 2162-68, Sept. 2013.##[27] A. J. M. Walhout, "What does biologically meaningful mean? A perspective on gene regulatory network validation," Genome Biology, vol. 12, no. 4, online, April 2011.##[28] M. Hecker, et al, "Gene regulatory network inference: Data integration in dynamic models - a review," BioSystems, vol. 96, no. 1, pp. 86-103, April 2009.##[29] W. Lee, W.S. Tzou, "Computational methods for discovering gene networks from expression data," Briefings in Bioinformatics, vol. 10, no. 4, pp. 408-23, July 2009.##[30] Q. Zhang et al, "Using single-index ODEs to study dynamic gene regulatory networks," PLoS One Computational Biology, vol. 13, no. 2, online, Feb. 2018.##[31] T. M. Cover, J. A. Thomas, Elements of information theory, 2nd edition, Wiley, New York, 2006.##[32] K. Do, P. Muller, M. Vannucci, Bayesian inference for gene expression and proteomics, Cambridge University Press, New York, 2006.##[33] P. Lin and S. P. Khatri, "Determining gene function in Boolean networks using Boolean satisfiability," IEEE Int'l Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, Dec. 2012.##[34] T. E. Ideker, V. Thorsson, R. M. Karp, "Discovery of regulatory interactions through perturbation: Inference and experimental design," Proc. Pacific Symposiums on Biocomputing, pp. 305-316, Hawaii, Jan. 2000.##[35] A. Kaufman, M. Kupiec, E. Ruppin, "Multi-knockout genetic network analysis: the Rad6 example, Proc. IEEE Computational Systems and Bioinformatics (CSB) Conference, pp. 332-340, Stanford, California, Feb 2004.##[36] A. Kaufman, et al, "Quantitative analysis of genetic and neuronal multi-perturbation experiments," PLoS One Computational Biology, vol. 1, no. 6, online, Nov. 2005.##[37] R. Dehghannasiri, B. Yoon, Edward R. Dougherty, "Optimal experimental design for gene regulatory networks in the presence of uncertainty," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 4, pp. 938-950, July 2015.##[38] A. R. Alizad-Rahvar, M. Sadeghi, "Integrative perturbation analysis of logic-based models of gene regulatory networks," PLoS One Computational Biology, Accepted, Oct. 2018.##[1]G. Battail, "Heredity as an encoded communication process," IEEE Trans. Information Theory, vol. 56, no. 2, pp. 678-687, Feb. 2010.##[2] L. M. Adleman, "Molecular computation of solutions to combinatorial problems," Science, vol. 266, no. 11, pp. 1021-1025, Nov. 1994.##[3] S. A. Salehi, et al, "Computing mathematical functions using DNA via fractional coding," Nature Genetics, May 2018.##[4] S. M. H. Tabatabaei Yazdi, et al, "Mutually uncorrelated primers for DNA-based data storage," IEEE Trans. Information Theory, vol. 64, no. 9, pp. 6283-6296, Sept. 2018.##[5] M. K. Gupta, "Quest for error correction in biology," IEEE Eng. in Medicine and Biology Mag, vol. 26, no. 1, Jan. 2006.##[6] B. Alberts, et al, Molecular biology of the cell, 6th edition, Garland Science, New York, 2014.##[7] S. Das, et al, Handbook of research on computational methodologies in gene regulatory networks, Hershey, New York, 2010.##[8] J. J. Pasternak, An introduction to human molecular genetics: Mechanisms of inherited diseases, 2nd edition, Wiley, New York, 2005.##[9] Beom S. Lee, et al, "A computational algorithm for personalized medicine in schizophrenia," Schizophrenia Research, vol. 192, pp. 131-136, Feb. 2018.##[10] N. Kornienko, et al, "Interfacing nature's catalytic machinery with synthetic materials for semi-artificial photosynthesis," Nature Nanotechnology, vol. 13, pp. 890-899, Oct. 2018.##[11] M.Banf, Seung Y. Rhee, "Computational inference of gene regulatory networks: Approaches, limitations and opportunities," Biochimica et Biophysica Acta, vol. 1860, no. 1, pp. 41-52, Jan 2017.##[12] N. Friedman, et al, "Using Bayesian networks to analyze expression data," J. Computational Biology, vol. 7, no. 3, pp. 601-620, March 2000.##[13] F. Fages, et al, "Influence networks compared with reaction networks: Semantics, expressivity and attractors," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1138 - 1151, July 2018.##[14] Y. Li, "The max-min high-order dynamic Bayesian network for learning gene regulatory networks with time-delayed regulations," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 792-803, July 2016.##[15] H. Chen, et al, "Bayesian data fusion of gene expression and histone modification profiles for inference of gene regulatory network," IEEE/ACM Trans. Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2018.2869590, early access, Sep. 2018.##[16] M. Shi, et al, "Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach," IET Systems Biology, vol. 10, no. 6, pp. 252-59, June 2016.##[17] S. Chan, et al, "Maximum a posteriori probability and time-varying approach for inferring gene regulatory networks from time course gene microarray data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 1, pp. 123 - 135, Jan. 2015.##[18] H. Xu, et al, "Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells," PLoS One Computational Biology, vol. 10, no. 8, pp. 1-14, Aug. 2014.##[19] S. Mehra, W. Hu, G. Karypis, "A Boolean algorithm for reconstructing the structure of regulatory networks," Metabolic Engineering, vol. 6, no. 4, pp. 326-39, Nov. 2004.##[20] S. A. Kauffman, "Metabolic stability and epigenesis in randomly constructed genetic nets, J. of Theoretical Biology, vol. 22, no. 3, pp. 437-67, March 1969.##[21] S. A. Kauffman, "The large-scale structure and dynamics of gene control circuits: an ensemble approach," J. of Theoretical Biology, vol. 44, no. 1, pp. 167-90, March 1974.##[22] J. I. Joo, et al, "Determining relative dynamic stability of cell states using Boolean network mode," Nature Scientific Reports, vol. 8, no. 1, online, Aug. 2018.##[23] www.humancellatlas.org, accessed Sep. 2018.##[24] H. P. Yockey, Information theory, evolution and origin of life, 2nd edition, Cambridge University Press, New York, 2011.##[25] S. L. Salzberg, et al, "Open questions: How many genes do we have?" BMC Biology, vol. 16, no. 1, online, Aug. 2018.##[26] Y. Lee, Qing Zhou, "Co-regulation in embryonic stem cells via context-dependent binding of transcription factors," Bioinformatics, vol. 29, no. 17, pp. 2162-68, Sept. 2013.##[27] A. J. M. Walhout, "What does biologically meaningful mean? A perspective on gene regulatory network validation," Genome Biology, vol. 12, no. 4, online, April 2011.##[28] M. Hecker, et al, "Gene regulatory network inference: Data integration in dynamic models - a review," BioSystems, vol. 96, no. 1, pp. 86-103, April 2009.##[29] W. Lee, W.S. Tzou, "Computational methods for discovering gene networks from expression data," Briefings in Bioinformatics, vol. 10, no. 4, pp. 408-23, July 2009.##[30] Q. Zhang et al, "Using single-index ODEs to study dynamic gene regulatory networks," PLoS One Computational Biology, vol. 13, no. 2, online, Feb. 2018.##[31] T. M. Cover, J. A. Thomas, Elements of information theory, 2nd edition, Wiley, New York, 2006.##[32] K. Do, P. Muller, M. Vannucci, Bayesian inference for gene expression and proteomics, Cambridge University Press, New York, 2006.##[33] P. Lin and S. P. Khatri, "Determining gene function in Boolean networks using Boolean satisfiability," IEEE Int'l Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, Dec. 2012.##[34] T. E. Ideker, V. Thorsson, R. M. Karp, "Discovery of regulatory interactions through perturbation: Inference and experimental design," Proc. Pacific Symposiums on Biocomputing, pp. 305-316, Hawaii, Jan. 2000.##[35] A. Kaufman, M. Kupiec, E. Ruppin, "Multi-knockout genetic network analysis: the Rad6 example, Proc. IEEE Computational Systems and Bioinformatics (CSB) Conference, pp. 332-340, Stanford, California, Feb 2004.##[36] A. Kaufman, et al, "Quantitative analysis of genetic and neuronal multi-perturbation experiments," PLoS One Computational Biology, vol. 1, no. 6, online, Nov. 2005.##[37] R. Dehghannasiri, B. Yoon, Edward R. Dougherty, "Optimal experimental design for gene regulatory networks in the presence of uncertainty," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 4, pp. 938-950, July 2015.##[38] A. R. Alizad-Rahvar, M. Sadeghi, "Integrative perturbation analysis of logic-based models of gene regulatory networks," PLoS One Computational Biology, Accepted, Oct. 2018.## ##

طبقه‌‎بندی سیگنال‎های مغزی EEG برای تشخیص بین دو واژه در گفتار خاموش Classification of EEG Signals for Discrimination of Two Imagined Words 1 1 در این پژوهش، یک رابط مغز-رایانه در کاربرد مکالمه خاموش برای شناسایی و تفکیک بین دو واژه پیاده‎سازی شده ‎است. در طی آزمایش، بر اساس یک زمان‌بندی مشخص، افراد یکی از دو واژه یا سکوت را  که به‌صورت تصادفی انتخاب شده ‎است، بدون آن‌که برزبان آورند؛ در ذهن خود تکرار می‎کنند و سیگنال‎های مغزی آنان توسط یک دستگاه ثبت EEG آزمایشگاهی چهارده کاناله ثبت می‎شود. پس از پیش‎پردازش و حذف داده‎های مخدوش، ویژگی‎های مناسب از این سیگنال‎ها استخراج و برای شناسایی به یک رده‎بند داده می‎شود. دو ترکیب برای استخراج ویژگی و رده‌بندی انتخاب و بررسی شدند: استخراج ضرایب ویولت همراه با رده‌‎بند SVM و ویژگی حاصل از تحلیل مؤلفه‎های اساسی همراه با رده‌‎بند کمینه فاصله که ترکیب نخست عملکرد بهتری از خود نشان داد. تعداد کل رده‌‎ها در این آزمایش سه عدد بوده که شامل دو واژه منتخب و سکوت می‎باشد. نتایج حاصل، نشان‌دهنده امکان تفکیک واژگان با دقت متوسط 8/56 درصد (بیش از 7/1 برابر نرخ تصادف) است که در سازگاری با نتایج گزارش‌شده در فعالیت‎های مشابه است؛ اما هنوز دقت کافی برای کاربردهای واقعی ندارد. 2 In this study, a Brain-Computer Interface (BCI) in Silent-Talk application was implemented. The goal was an electroencephalograph (EEG) classifier for three different classes including two imagined words (Man and Red) and the silence. During the experiment, subjects were requested to silently repeat one of the two words or do nothing in a pre-selected random order. EEG signals were recorded by a 14 channel EMOTIV wireless headset. Two combinations of features and classifiers were used: Discrete Wavelet Transform (DWT) features with Support Vector Machine (SVM) classifier and Principle Component Analysis (PCA) features with a Minimum-Distance classifier. Both combinations were capable of discriminating between the three classes much better than the chance level (33.3%), none of them was reliable and accurate enough for a real application though. The first method (DWT+SVM) showed better results. In this case, feature set was D2, D3, D4 and A4 coefficients of 4-level DWT decomposition of the EEG signals, roughly corresponding to major frequency bands (Delta, Theta, Alpha and Beta) of these signals. Three binary SVM machines were used. Each machine was trained to classify between two of the three classes, namely Man/Red, Man/Silence or Red/Silence. Majority Selection Rule was used to determine final class. Once two of these classifiers presented the true class, a win (correct classification) was counted, otherwise a loss (false classification) was considered. Finally, Monte-Carlo Cross Validation showed an overall performance of about 56.8% correct classification which is comparable with the results reported for similar experiments. 120 113 2018/09/152017/07/302018/10/222018/08/182018/09/22018/09/132017/11/302018/10/232018/05/6 1397/2/16 2019/09/22020/06/22019/09/22019/11/32019/05/72019/09/22020/06/82019/01/262020/05/13 1399/2/24 محمدرضا اصغری بجستانی Mohammad Reza Asghari Bejestani پژوهشکده برق و فناوری ‎اطلاعات، سازمان پژوهش‌های علمی و صنعتی ایران ایران bejestani@irost.ir غلامرضا محمدخانی Gholam Reza Mohammadkhani پژوهشکده برق و فناوری ‎اطلاعات، سازمان پژوهش‌های علمی و صنعتی ایران ایران mohammadkhani@irost.org سعید گرگین Saeed Gorgin پژوهشکده برق و فناوری ‎اطلاعات، سازمان پژوهش‌های علمی و صنعتی ایران ایران gorgin@irost.ir وحیدرضا نفیسی Vahid Reza Nafisi پژوهشکده برق و فناوری ‎اطلاعات، سازمان پژوهش‌های علمی و صنعتی ایران ایران vr_nafisi@irost.org غلامرضا فراهانی Ghaolam Reza Farahani پژوهشکده برق و فناوری ‎اطلاعات، سازمان پژوهش‌های علمی و صنعتی ایران ایران farahani.gh@irost.org Silent Talk Imagined Speech EEG signals Classification Brain-Computer interface مکالمه خاموش رابط‌ ‎مغز-رایانه تصور گفتار سیگنال‎های مغزی [1] N. Birbaumer and L. G. Cohen, "Brain-computer interfaces: communication and resto-ration of movement in paralysis", The Journal of physiology, vol. 579, no. 3, pp. 621-636, 2007.##[2] Ivan S. Kotchetkov, Brian Y. Hwang and et. al., "Brain-computer Interfaces: military, neuro-surgical and ethical prespective", Neurosurg Focus, vol. 28, no. 5, pp. 1-6, 2010.##[3] M. A. Lebedev and M. A. L. Nicolelis, "Brain-Machine interfaces: past, present and future", TRENDS in Neurosciences, vol. 29, no. 9, pp. 536-546, 2006.##[4] Defense Advanced Research Projects Agency: Department of Defense Fiscal Year 2010 Budget Estimates Washington, DC, Department of Defense, 2009##[5] F. Nijboer, E. W. Sellers, J. Mellinger, M. A. Jordan, T. Matuz, A. Furdea, S. Halder, U. Mochty, D. J. Krusienski, and T. M. Vaughan, "A P300-based brain-computer interface for people with amyotrophic lateral sclerosis", Clinical neurophysiology, vol. 119, no. 8, pp. 1909-1916, 2008.##[6] E. Donchin and Y. Arbel, "P300 based brain computer interfaces: a progress report", Foundations of Augmented Cognition, Neuroer-gonomics and Operational Neuroscience, pp. 724-731, 2009.##[7] L. A.Farwell, E.Donchin, "Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials", Electroenceph clin Neurophysiol, Vol. 70, pp. 510-523, 1988.##[8] S. Iqbal , Y.U. Khan , O. Farooq ," EEG based classification of imagined vowel sounds", 2nd International Conference on Computing for Sustainable Global Development (INDIACom),pp. 1591-1594, IEEE 2015.##[9] K. Brigham, B.V.K.V Kumar, " Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy", 4th International Con-ference on Bioinformatics and Biomedical Engineering (iCBBE), pp. 1-4, IEEE 2010.##[10] R. Kamalakkannan, R. Rajkumar, R. M. Madan, D. S. Shenbaga, "Imagined Speech Classi-fication using EEG", Advances in Biomedical Science and Engineering, Vol. 1, No. 2, pp.20-32, 2014.##[11] X. Chi, J. B. Hagedorna, D. Schoonovera, and M. D'Zmuraa, "EEG-based discrimination of imagined speech phonemes", International Journal of Bioelectromagnetism, vol. 13, no. 4, 2011.##[12] T. Kim, J. Lee, H. Choi, H. Lee et al., "Meaning based covert speech classification for brain-computer interface based on electroencephalo-graphy", 6th International IEEE/EMBS Con-ference on Neural Engineering (NER), pp. 53-56, 2013.##[13] K. Brigham , B.V.K.V. Kumar, "Subject identification from electroencephalogram (EEG) signals during imagined speech", Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS) ,pp. 1-8, 2010.##[14] M. D'Zmura, S. Deng, T. Lappas, S. Thorpe, and R. Srinivasan, "Toward EEG sensing of imagined speech", Human-Computer Inter-action New Trends, pp. 40-48, 2009.##[15] S. Deng, R. Srinivasan, T. Lappas, and M. D'Zmura, "EEG classification of imagined syllable rhythm using Hilbert spectrum methods", Journal of neural engineering, vol. 7, no. 4,pp. 046006, 2010.##[16] K. Yaser Arafat, S. S. Kanade, "Imagined Speech EEG Signal Processing For Brain Computer Interface", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol. 3, No. 7, pp. 123, 2014.##[17] A. Shahbahrami, K. Nadjafi, T.Nadjafi, "Different Application Fields of Brain Signal Processing", Quarterly Journal of Signal and Data Processing(JSDP), Vol.13, No.3, pp129-154, 2016.##[18] C. S. DaSalla, H. Kambara, M. Sato, and Y. Koike, "Single-trial classification of vowel speech imagery using common spatial patterns", Neural Networks, vol. 22, no. 9, pp. 1334-1339, 2009.##[1] N. Birbaumer and L. G. Cohen, "Brain-computer interfaces: communication and resto-ration of movement in paralysis", The Journal of physiology, vol. 579, no. 3, pp. 621-636, 2007.##[2] Ivan S. Kotchetkov, Brian Y. Hwang and et. al., "Brain-computer Interfaces: military, neuro-surgical and ethical prespective", Neurosurg Focus, vol. 28, no. 5, pp. 1-6, 2010.##[3] M. A. Lebedev and M. A. L. Nicolelis, "Brain-Machine interfaces: past, present and future", TRENDS in Neurosciences, vol. 29, no. 9, pp. 536-546, 2006.##[4] Defense Advanced Research Projects Agency: Department of Defense Fiscal Year 2010 Budget Estimates Washington, DC, Department of Defense, 2009##[5] F. Nijboer, E. W. Sellers, J. Mellinger, M. A. Jordan, T. Matuz, A. Furdea, S. Halder, U. Mochty, D. J. Krusienski, and T. M. Vaughan, "A P300-based brain-computer interface for people with amyotrophic lateral sclerosis", Clinical neurophysiology, vol. 119, no. 8, pp. 1909-1916, 2008.##[6] E. Donchin and Y. Arbel, "P300 based brain computer interfaces: a progress report", Foundations of Augmented Cognition, Neuroer-gonomics and Operational Neuroscience, pp. 724-731, 2009.##[7] L. A.Farwell, E.Donchin, "Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials", Electroenceph clin Neurophysiol, Vol. 70, pp. 510-523, 1988.##[8] S. Iqbal , Y.U. Khan , O. Farooq ," EEG based classification of imagined vowel sounds", 2nd International Conference on Computing for Sustainable Global Development (INDIACom),pp. 1591-1594, IEEE 2015.##[9] K. Brigham, B.V.K.V Kumar, " Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy", 4th International Con-ference on Bioinformatics and Biomedical Engineering (iCBBE), pp. 1-4, IEEE 2010.##[10] R. Kamalakkannan, R. Rajkumar, R. M. Madan, D. S. Shenbaga, "Imagined Speech Classi-fication using EEG", Advances in Biomedical Science and Engineering, Vol. 1, No. 2, pp.20-32, 2014.##[11] X. Chi, J. B. Hagedorna, D. Schoonovera, and M. D'Zmuraa, "EEG-based discrimination of imagined speech phonemes", International Journal of Bioelectromagnetism, vol. 13, no. 4, 2011.##[12] T. Kim, J. Lee, H. Choi, H. Lee et al., "Meaning based covert speech classification for brain-computer interface based on electroencephalo-graphy", 6th International IEEE/EMBS Con-ference on Neural Engineering (NER), pp. 53-56, 2013.##[13] K. Brigham , B.V.K.V. Kumar, "Subject identification from electroencephalogram (EEG) signals during imagined speech", Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS) ,pp. 1-8, 2010.##[14] M. D'Zmura, S. Deng, T. Lappas, S. Thorpe, and R. Srinivasan, "Toward EEG sensing of imagined speech", Human-Computer Inter-action New Trends, pp. 40-48, 2009.##[15] S. Deng, R. Srinivasan, T. Lappas, and M. D'Zmura, "EEG classification of imagined syllable rhythm using Hilbert spectrum methods", Journal of neural engineering, vol. 7, no. 4,pp. 046006, 2010.##[16] K. Yaser Arafat, S. S. Kanade, "Imagined Speech EEG Signal Processing For Brain Computer Interface", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol. 3, No. 7, pp. 123, 2014.##[17] ا. شاه بهرامی، ک. نجفی، ط. نجفی. "حوزه‌های مختلف کاربردی پردازش سیگنال مغزی در ایران ". فصل‌نامه پردازش علائم و داده‌ها، جلد ۱۳، شماره ۳، صفحات ۱۲۹-۱۵۴، 1395.##[17] A. Shahbahrami, K. Nadjafi, T.Nadjafi, "Different Application Fields of Brain Signal Processing", Quarterly Journal of Signal and Data Processing(JSDP), Vol.13, No.3, pp129-154, 2016.##[18] C. S. DaSalla, H. Kambara, M. Sato, and Y. Koike, "Single-trial classification of vowel speech imagery using common spatial patterns", Neural Networks, vol. 22, no. 9, pp. 1334-1339, 2009.## ##

مرجع‌گزینی در زبان فارسی با استفاده از شبکه عصبی عمیق Corefrence resolution with deep learning in the Persian Labnguage 1 1 در حال حاضر با توجه به کثرت شبکه‌های اجتماعی و شبکه‌های خبری تلویزیونی، رادیویی، اینترنتی و غیره، خواندن تمام متون مختلف و به‌تبع آن تحلیل آن‌ها و دست‌یابی به ارتباطات این متون نیازمند صرف هزینه زمانی و انسانی بسیار بالا است که در عصر کنونی با استفاده از فن‌های مختلف پردازش زبان طبیعی صورت می‌گیرد، یکی از چالش‌های موجود در این زمینه پایین‌بودن دقت سامانه‌های مرجع‌گزینی است که سبب کشف روابط ناصحیح و یا عدم کشف روابط صحیح می‌شود. مراحل کلی حل مسأله مرجع‌گزینی از سه‌گامِ شناسایی موجودیت­‌های نامدار، استخراج ویژگی‌های موجودیت­‌های نامدار و مرجع‌گزینی آن‌ها تشکیل ‌شده است. موجودیت­های نامدار ویژگی‌های فراوانی دارند، وجود ویژگی‌های مختلف (متناسب و متناقض با مرجع) در گراف‌ها این امکان را می‌دهند که بتوان حد آستانه‌ای را از ترکیب ویژگی‌های مختلف استخراج کرد. در مقاله ارائه‌شده ابتدا پیش‌پردازش‌های مختلف روی پیکره پژوهشگاه خواجه‌نصیر [1] انجام گرفت؛ سپس با استفاده از الگوریتم‌های مبتنی بر شبکه عصبی عمیق داده‌های موجود به بردارهای عددی تبدیل شدند و پس از آن با استفاده از گراف و با ویژگی‌هایی که در متن مقاله عنوان‌شده هرس اولیه انجام گرفت؛ درواقع رویکردهای مبتنی بر گراف، موجودیت‌ها را همچون مجموعه‌ای از عناصر مرتبط با یکدیگر می‌شناسد که تحلیل روابط میان موجودیت‌های اولیه در گراف و وزن‌دهی به این ارتباط‌ها، منجر به استخراج ویژگی‌های سطح بالاتر و مرتبط‌تری می‌‌شود و نیز تناقضات ایجادشده بر اساس کمبود اطلاعات را تا حدودی کاهش می‌دهد. سپس با استفاده از شبکه‌های عصبی، روی پیکره مورداشاره در [30] (پیکره آزمون اپسلا) مرجع‌گزینی انجام گرفت که نتایج حاصل بیان‌گر بهبود روش پیشنهادی (رسیدن به دقت 09/62) است که در متن مقاله به‌طور مشروح بیان‌شده است. 2 Coreference resolution is an advanced issue in natural language processing. Nowadays, due to the extension of social networks, TV channels, news agencies, the Internet, etc. in human life, reading all the contents, analyzing them, and finding a relation between them require time and cost. In the present era, text analysis is performed using various natural language processing techniques, one of the challenges in this field is the low accuracy in detecting name entities' reference, which detection process has been named as coreference resolution. Coreference resolution is finding all expressions that refer to a name entity, and two expressions are coreference together when these expressions located in the same coreference cluster.      Coreference resolution could be used in many natural language processing tasks such as question answering, text summarization, machine translation, information extraction, etc. Coreference resolution methods are into two main categories; machine learning and rule-based approaches. In the rule-based approaches for detecting coreferences, a set of rich rule ordinary which written by a specialist is execued. These methods are quick, but these are language-dependent and necessary written to each language firstly again by a specialist. The machine learning method divides into supervised and unsupervised methods, in a supervised approach, it is require to have data labeled by a specialist. Coreference resolution included three main phases: named entities recognition, features extraction of name entities, and analyzes the coreferences, in which the primary phase is feature extraction. After corpus creation, name entities should be recognized in the corpus. This step depends on a corpus, in some corpora entities named as golden data, in this paper, we used RCDAT corpus, which determined name entities itself. After the name entities recognition phase, the mention pairs are determined, and the features are extracted. The proposed method uses two categories of the features: the first is word embedding vector, the second is handcrafted features, which are the distance between the mentions, head matching, gender matching, etc. This paper used a deep neural network to train the features extracted, in the analyze coreferences phase a Feed Forward Neural Network (FFNN) is trained by the candidate mention pairs (extracted features from them) and their labels (coreference / non-coreference or 1/0) so that the trained FFNN assigns a probability (between 0 and 1) to any given mention pair. Then used the graph technique with a threshold level to determine different or compatible name entities in the coreference resolution cluster.  This step creates the graph by using the extracted mention pairs from the previous step. In this graph, nodes are the mention pairs that are clustered by using the agglomerative hierarchical clustering algorithm inorder to locate similar mention pairs in a group. The resulting clusters are considered as coreference resolution chains. In this paper, RCDAT Persian language corpus is used for training the proposed coreference resolution approach and for testing the Uppsala Persian language dataset which is used and in the calculation of the accurate of system, different tools have been taken for features extraction which each of them effects on the accuracy of the whole system. The corpora, tools, and methods used in the system are standard. They are quite comparable to the ACE and Ontonotes corpora and tools used at the same time in the coreference resolution algorithm.  The results of the improvements proposed method (F1 = 62.09) is expressed in the text of the paper. 138 121 2018/09/152017/07/302018/10/222018/08/182018/09/22018/09/132017/11/302018/10/232018/05/62018/08/21 1397/5/30 2019/09/22020/06/22019/09/22019/11/32019/05/72019/09/22020/06/82019/01/262020/05/132019/09/2 1398/6/11 حسین سهلانی hossein sahlani دانشگاه صنعتی مالک اشتر ایران sahlani_h@yahoo.com مریم حورعلی maryam Hourali دانشگاه صنعتی مالک اشتر ایران hourali@mut.ac.ir بهروز مینایی بیدگلی Behrouz Minaei-Bidgoli دانشگاه علم و صنعت ایران ایران b.minaii@iust.ac.ir Coreference resolution Deep neural networks Graph Named entities ecognition Information extraction مرجع‌گزینی گراف شناسایی موجودیت نامدار استخراج اطلاعات از متن شبکه‌های عصبی عمیق [1] Z. Rahimi, S. HosseinNejad "Corpus based coreference resolution for Farsi text", JSDP, vol. 17 (1), pp. 79-98, 2020.##[2] Y. Shekofteh, T. Emami Azadi, "A'laam Corpus: A Standard Corpus of Named Entity for Persian Language", JSDP, Vol. 14 (3), pp.127-142, 2017.##[3] P. S. Mortazavi and M.Shamsfard "Recognition of named entities in Persian texts," in 15-th annual conference of computer society of Iran, Tehran, 2009.##[4] A. AleAhmad, H. Amiri, E. Darrudi, M. Rahgozar, and F. Oroumchian, "Hamshahri: A Standard Persian text collection", Knowledge-Based Systems, Vol. 22(5), pp.382-387, 2009.##[5] A. Rahman, Ng. Vincent, "Coreference resolution with world knowledge," 49th Annual Meeting of the Association for Computational Linguistics, Vol. 1, 2013.##[6] B. Amit, B. Breck, "Algorithms for scoring coreference chains", In Proceedings of the LREC Workshop on Linguistic Coreference, pp. 563-566, 1998.##[7] M. Bijankhan, J.Sheykhzadegan, M. Bahrani, and M.Ghayoomi, "Lessons from Building a Persian Written Corpus: Peykare", Language Resources and Evaluation, Vol. 45(2), pp.143-164, 2011.##[8] Ch.Prafulla, Ch. Kumar and R. Huang, "Event coreference resolution by iteratively unfolding inter-dependencies among events", In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2124-2133, 2017.##[9] C. Kevin and Ch. D. Manning, "Deep Reinforcement Learning for Mention-Ranking Coreference Models," In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2256-2262, 2016.##[10] C. Kevin and Ch. D. Manning, "Improving Coreference Resolution by Learning Entity-Level Distributed Representations," In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics Vol.1, pp. 643-653. 2016.##[11] C. Nicolae, G. Nicolae, "Bestcut: A graph algorithm for coreference resolution," conference on empirical methods in natural language processing, 2014.##[12] D. Pascal and B. Jason, "Specialized models and ranking for coreference resolution," In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 660-669, 2008.##[13] D. Chase, L. Chan, H. Peng, H. Wu, Sh. Upadhyay, N. Gupta, C. Tsai, M. Sammons, and D. Roth, "UI CCG TAC-KBP2017 submissions: Entity discovery and linking, and event nugget detection and co-reference," In Proceedings of the Text Analysis Conference, 2017.##[14] A. Haghighi, and D. Klein, "Simple coreference resolution with rich syntactic and semantic features," In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Vol.3, pp. 1152-1161, Association for Computational Linguistics, 2009.##[15] Lee. Heeyoung, A. Chang, Y. Peirsman, N. Chambers, M. Surdeanu, and D. Jurafsky, "Deterministic coreference resolution based on entity-centric, precision-ranked rules". Computational Linguistics, 2013.##[16] J.Heng and R. Grishman, "Knowledge base population: Successful approaches and challenges", In Proceedings of the 49th Annual Meeting of the Association for ComProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) putational Linguistics: Human Language Technologies, pp. 1148-1158, 2011.##[17] J. Shanshan, Y. Li, T. Qin, Q. Meng, and B. Dong, "SRCB entity discovery and linking (EDL) and event nugget systems for TAC 2017", In Proceedings of the Text Analysis Conference, 2017.##[18] P. Haoruo, Y. Song, and D. Roth, "Event detection and co-reference with minimal supervision", In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 392-402, 2016.##[19] P. S. Paolo, S. 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Roth, "UI CCG TAC-KBP2017 submissions: Entity discovery and linking, and event nugget detection and co-reference," In Proceedings of the Text Analysis Conference, 2017.##[14] A. Haghighi, and D. Klein, "Simple coreference resolution with rich syntactic and semantic features," In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Vol.3, pp. 1152-1161, Association for Computational Linguistics, 2009.##[15] Lee. Heeyoung, A. Chang, Y. Peirsman, N. Chambers, M. Surdeanu, and D. Jurafsky, "Deterministic coreference resolution based on entity-centric, precision-ranked rules". Computational Linguistics, 2013.##[16] J.Heng and R. Grishman, "Knowledge base population: Successful approaches and challenges", In Proceedings of the 49th Annual Meeting of the Association for ComProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) putational Linguistics: Human Language Technologies, pp. 1148-1158, 2011.##[17] J. 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Jurafsky, "Deterministic coreference resolution based on entity-centric, precision-ranked rules" Computational Linguistics, Vol. 39(4), pp.885- 916, 2013.##[22] L. Zhengzhong, J. Araki, E. Hovy, and T. Mitamura, "Supervised within-document event coreference using information pro-pagation", In Proceedings of the Ninth Lan-guage Resources and Evaluation Conference, pp. 4539- 4544, 2014.##[23] L. Jing and V. Ng, "Joint learning for event coreference resolution", In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol.1, pp. 90-101, 2017.##[24] L. Jing and V. Ng, "Learning antecedent structures for event coreference resolution", In Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, pp. 113-118, 2017.##[25] L. Jing and V. Ng, "UTD's event nugget detection and coreference system at KBP 2017", In Proceedings of the Text Analysis Conference, 2017.##[26] L. 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