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یک سامانه مدیریت دسترسی برای کاهش تهدیدهای عملیاتی در سامانه اسکادا An Access Management System to Mitigate Operational Threats in SCADA System 1 1 حمله به سامانه‌های اسکادا در زیرساخت‌های حیاتی خسارت‌های جبران‌ناپذیری به همراه دارد. در سامانه اسکادا اپراتورها نقش کلیدی داشته، و چنانچه وظایف خود را به‌درستی انجام ندهند در فرآیندهای جاری شبکه اختلال به‌وجود می‌آید. در این مقاله عملکرد نامناسب اپراتورها در کنترل فرآیندها به‌عنوان تهدیدهای عملیاتی شناخته می‌شوند. یک تهدید عملیاتی هنگامی است که اپراتورهای مجاز با انجام‌ندادن وظایف سازمانی و یا سوءاستفاده از مجوزهای قانونی سعی در ایجاد اختلال در پست‌های راه دور می‌کنند. در این مقاله یک سامانه مدیریت دسترسی مبتنی بر اعتماد برای کاهش تهدیدهای عملیاتی ارائه ‌شده است. در این سامانه سطح دسترسی اپراتور به پست‌ها با توجه به مقدار اعتماد اپراتور و سطح بحرانی‌بودن پست تعیین می‌شود. مقدار اعتماد اپراتور در فواصل زمانی معین و یا هنگام مشاهده ناهنجاری در شبکه به‌روزرسانی می‌شود. سامانه پیشنهادی قادر است تا ناهنجاری‌های به‌وجود‌آمده را شناسایی کند. نتایج شبیه‌سازی با استفاده از داده‌های شبکه برق ایران نشان می‌دهد که سامانه پیشنهادی از کارایی مناسبی برخوردار است. 2 One of the most dangerous insider threats in a supervisory control and data acquisition (SCADA) system is the operational threat. An operational threat occurs when an authorized operator misuses the permissions, and brings catastrophic damages by sending legitimate control commands. Providing too many permissions may backfire, when an operator wrongly or deliberately abuses the privileges. Therefore, an access management system is required to provide necessary permissions and prevent malicious usage.  An operational threat on a critical infrastructure has the potential to cause large financial losses and irreparable damages at the national level. In this paper, we propose a new alarm-trust based access management system reducing the potential of operational threats in SCADA system.  In the proposed system, the accessibility of a remote substation will be determined based on the operator trust and the criticality level of the substation. The trust value of the operator is calculated using the performance of the operator, periodically or in emergencies, when an anomaly is detected. The criticality level of the substation is computed using its properties. Our system is able to detect anomalies that may result from the operational threats. The simulation results in the SCADA power system of Iran show effectiveness of our system.   3 18 2015/10/12 1394/7/20 2017/10/25 1396/8/3 پیام محمودی نصر Payam Mahmoudi Nasr دانشگاه مازندران ایران P.mahmoudi@umz.ac.ir علی یزدیان ورجانی Ali Yazdian Varjani دانشگاه تربیت مدرس ایران Yazdian@modares.ac.ir Access control trust insider threat anomaly detection SCADA کنترل دسترسی اعتماد تهدید خودی تشخیص ناهنجاری اسکادا [1] D. Kushner, "The real story of stuxnet," ieee Spectrum, vol. 50, pp. 48-53, 2013.##[2] Matthew L. Collins, Michael C. Theis, Randall F. Trzeciak, Jeremy R. Strozer, Jason W. Clark, Daniel L. Costa, et al., "Common sense guide to mitigating insider threats 5th edition," CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST, 2016.##[3] N. Baracaldo and J. Joshi, "An adaptive risk management and access control framework to mitigate insider threats," Computers & Security, vol. 39, pp. 237-254, 2013.##[4] P. Legg, N. Moffat, J. R. Nurse, J. Happa, I. Agrafiotis, M. Goldsmith, et al., "Towards a conceptual model and reasoning structure for insider threat detection," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 4, pp. 20-37, 2013.##[5] H. Shey, K. Mak, S. Balaouras, and B. Luu, "Understand the state of data security and privacy: 2015 to 2016," Forrester Research Inc, vol. 1, 2013.##[6] N. Baracaldo, B. Palanisamy, and J. Joshi, "G-SIR: An Insider Attack Resilient Geo-Social Access Control Framework," IEEE Transactions on Dependable and Secure Computing, 2017.##[7] M. Warkentin, A. C. Johnston, J. Shropshire, and W. D. Barnett, "Continuance of protective security behavior: A longitudinal study," Decision Support Systems, vol. 92, pp. 25-35, 2016.##[8] M. Asgarkhani and E. Sitnikova, "A strategic approach to managing security in SCADA systems," in Proceedings of the 13th European Conference on Cyber warefare and Security, 2014, pp. 23-32.##[9] A. Nicholson, S. Webber, S. Dyer, T. Patel, and H. Janicke, "SCADA security in the light of Cyber-Warfare," Computers & Security, vol. 31, pp. 418-436, 2012.##[10] H. Bao, R. Lu, B. Li, and R. Deng, "BLITHE: Behavior rule-based insider threat detection for smart grid," IEEE Internet of Things Journal, vol. 3, pp. 190-205, 2016.##[11] S. Board, "Pipeline Accident Report," 2010.##[12] 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.##[13] T. Sasaki, "A Framework for Detecting Insider Threats using Psychological Triggers," JoWUA, vol. 3, pp. 99-119, 2012.##[14] M.-K. Yoon and G. F. Ciocarlie, "Communication pattern monitoring: Improving the utility of anomaly detection for industrial control systems," in NDSS Workshop on Security of Emerging Networking Technologies, 2014.##[15] I. Garitano, R. Uribeetxeberria, and U. Zurutuza, "A review of SCADA anomaly detection systems," in Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, 2011, pp. 357-366.##[16] M. Bishop, H. M. Conboy, H. Phan, B. I. Simidchieva, G. S. Avrunin, L. A. Clarke, et al., "Insider threat identification by process analysis," in Security and Privacy Workshops (SPW), 2014 IEEE, 2014, pp. 251-264.##[17] D. Hadžiosmanović, R. Sommer, E. Zambon, and P. H. Hartel, "Through the eye of the PLC: semantic security monitoring for industrial processes," in Proceedings of the 30th Annual Computer Security Applications Conference, 2014, pp. 126-135.##[18] N. Baracaldo and J. Joshi, "Beyond accountability: using obligations to reduce risk exposure and deter insider attacks," in Proceedings of the 18th ACM symposium on Access control models and technologies, 2013, pp. 213-224.##[19] J.-H. Cho, A. Swami, and R. Chen, "A survey on trust management for mobile ad hoc networks," IEEE Communications Surveys & Tutorials, vol. 13, pp. 562-583, 2011.##[20] S.-P. Hong, G.-J. Ahn, and W. Xu, "Access control management for SCADA systems," IEICE TRANSACTIONS on Information and Systems, vol. 91, pp. 2449-2457, 2008.##[21] O. Rysavy, J. Rab, P. Halfar, and M. Sveda, "A formal authorization framework for networked SCADA systems," in Engineering of Computer Based Systems (ECBS), 2012 IEEE 19th International Conference and Workshops on, 2012, pp. 298-302.##[22](NRI), "Substation Automation Systems standard (Transmission and Subtransmission Substations)," Ministry of Energy of Iran, 2008.##[23] B. Zhu, A. Joseph, and S. Sastry, "A taxonomy of cyber attacks on SCADA systems," in Internet of things (iThings/CPSCom), 2011 international conference on and 4th international conference on cyber, physical and social computing, 2011, pp. 380-388.##[24] J. Lopez, C. Alcaraz, and R. Roman, "Smart control of operational threats in control substations," Computers & Security, vol. 38, pp. 14-27, 2013.##[25] A. M. L. da Silva, A. Violin, C. Ferreira, and Z. S. Machado, "Probabilistic evaluation of substation criticality based on static and dynamic system performances," IEEE Transactions on Power Systems, vol. 29, pp. 1410-1418, 2014.##[26] D. C. Montgomery, Introduction to statistical quality control: John Wiley & Sons (New York), 2009.##[27] I. IEC, "62682 Management of Alarm Systems for the Process Industries," ed: Geneva: IEC, 2014.##[28] N. Mayadevi, S. Ushakumari, and S. Vinodchandra, "SCADA-based operator support system for power plant equipment fault forecasting," Journal of the Institution of Engineers (India): Series B, vol. 4, pp. 369-376, 2014.##[29] 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.##[30] T. M. U. SPAMLAB. (2017). SPAMLAB. Available: https://www.irancert.ir##[22] پزوهشگاه نیرو"استاندارد سامانه‌های اتوماسیون پست‌های انتقال و فوق توزیع," وزارت نیرو, 1386.##[1] D. Kushner, "The real story of stuxnet," ieee Spectrum, vol. 50, pp. 48-53, 2013.##[2] Matthew L. Collins, Michael C. Theis, Randall F. Trzeciak, Jeremy R. Strozer, Jason W. Clark, Daniel L. Costa, et al., "Common sense guide to mitigating insider threats 5th edition," CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST, 2016.##[3] N. Baracaldo and J. Joshi, "An adaptive risk management and access control framework to mitigate insider threats," Computers & Security, vol. 39, pp. 237-254, 2013.##[4] P. Legg, N. Moffat, J. R. Nurse, J. Happa, I. Agrafiotis, M. Goldsmith, et al., "Towards a conceptual model and reasoning structure for insider threat detection," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 4, pp. 20-37, 2013.##[5] H. Shey, K. Mak, S. Balaouras, and B. Luu, "Understand the state of data security and privacy: 2015 to 2016," Forrester Research Inc, vol. 1, 2013.##[6] N. Baracaldo, B. Palanisamy, and J. Joshi, "G-SIR: An Insider Attack Resilient Geo-Social Access Control Framework," IEEE Transactions on Dependable and Secure Computing, 2017.##[7] M. Warkentin, A. C. Johnston, J. Shropshire, and W. D. Barnett, "Continuance of protective security behavior: A longitudinal study," Decision Support Systems, vol. 92, pp. 25-35, 2016.##[8] M. Asgarkhani and E. Sitnikova, "A strategic approach to managing security in SCADA systems," in Proceedings of the 13th European Conference on Cyber warefare and Security, 2014, pp. 23-32.##[9] A. Nicholson, S. Webber, S. Dyer, T. Patel, and H. Janicke, "SCADA security in the light of Cyber-Warfare," Computers & Security, vol. 31, pp. 418-436, 2012.##[10] H. Bao, R. Lu, B. Li, and R. Deng, "BLITHE: Behavior rule-based insider threat detection for smart grid," IEEE Internet of Things Journal, vol. 3, pp. 190-205, 2016.##[11] S. Board, "Pipeline Accident Report," 2010.##[12] 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.##[13] T. Sasaki, "A Framework for Detecting Insider Threats using Psychological Triggers," JoWUA, vol. 3, pp. 99-119, 2012.##[14] M.-K. Yoon and G. F. Ciocarlie, "Communication pattern monitoring: Improving the utility of anomaly detection for industrial control systems," in NDSS Workshop on Security of Emerging Networking Technologies, 2014.##[15] I. Garitano, R. Uribeetxeberria, and U. Zurutuza, "A review of SCADA anomaly detection systems," in Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, 2011, pp. 357-366.##[16] M. Bishop, H. M. Conboy, H. Phan, B. I. Simidchieva, G. S. Avrunin, L. A. Clarke, et al., "Insider threat identification by process analysis," in Security and Privacy Workshops (SPW), 2014 IEEE, 2014, pp. 251-264.##[17] D. Hadžiosmanović, R. Sommer, E. Zambon, and P. H. Hartel, "Through the eye of the PLC: semantic security monitoring for industrial processes," in Proceedings of the 30th Annual Computer Security Applications Conference, 2014, pp. 126-135.##[18] N. Baracaldo and J. Joshi, "Beyond accountability: using obligations to reduce risk exposure and deter insider attacks," in Proceedings of the 18th ACM symposium on Access control models and technologies, 2013, pp. 213-224.##[19] J.-H. Cho, A. Swami, and R. Chen, "A survey on trust management for mobile ad hoc networks," IEEE Communications Surveys & Tutorials, vol. 13, pp. 562-583, 2011.##[20] S.-P. Hong, G.-J. Ahn, and W. Xu, "Access control management for SCADA systems," IEICE TRANSACTIONS on Information and Systems, vol. 91, pp. 2449-2457, 2008.##[21] O. Rysavy, J. Rab, P. Halfar, and M. Sveda, "A formal authorization framework for networked SCADA systems," in Engineering of Computer Based Systems (ECBS), 2012 IEEE 19th International Conference and Workshops on, 2012, pp. 298-302.##[22](NRI), "Substation Automation Systems standard (Transmission and Subtransmission Substations)," Ministry of Energy of Iran, 2008.##[23] B. Zhu, A. Joseph, and S. Sastry, "A taxonomy of cyber attacks on SCADA systems," in Internet of things (iThings/CPSCom), 2011 international conference on and 4th international conference on cyber, physical and social computing, 2011, pp. 380-388.##[24] J. Lopez, C. Alcaraz, and R. Roman, "Smart control of operational threats in control substations," Computers & Security, vol. 38, pp. 14-27, 2013.##[25] A. M. L. da Silva, A. Violin, C. Ferreira, and Z. S. Machado, "Probabilistic evaluation of substation criticality based on static and dynamic system performances," IEEE Transactions on Power Systems, vol. 29, pp. 1410-1418, 2014.##[26] D. C. Montgomery, Introduction to statistical quality control: John Wiley & Sons (New York), 2009.##[27] I. IEC, "62682 Management of Alarm Systems for the Process Industries," ed: Geneva: IEC, 2014.##[28] N. Mayadevi, S. Ushakumari, and S. Vinodchandra, "SCADA-based operator support system for power plant equipment fault forecasting," Journal of the Institution of Engineers (India): Series B, vol. 4, pp. 369-376, 2014.##[29] 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.##[30] T. M. U. SPAMLAB. (2017). SPAMLAB. Available: https://www.irancert.ir## ##

آشکارسازی سیگنال بر اساس پردازش موازی مبتنی بر جی‌پی‌یو در شبکه‌های حس‌گری صوتی دارای زیرساخت Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks 1 1 الگوریتم فیشر، یکی از معروف‌ترین و پرکاربردترین روش‌های آشکارسازی آرایه‌ای سیگنال‌های صوتی بسامدِ پایین در شبکه‌های حس‌گری دارای زیرساخت است؛ اما یکی از مشکلات عمده در به‌کارگیری این الگوریتم، زمان طولانی انجام پردازش در آن است که در عمل، پیاده‌سازی بلادرنگ آشکارساز را با مشکل مواجه می‌سازد. در این مقاله، چگونگی پیاده‌سازی الگوریتم فیشر را با استفاده از واحد پردازش گرافیک (جی‌پی‌یو) به‌منظور تحقق محاسبات سریع و انجام پردازش‌های نزدیک به زمانِ  واقعی، ارائه می‌کنیم. به‌خصوص به‌منظور بهبود هرچه بیشتر سرعت محاسبات، الگوریتم آشکارسازی با استفاده از روش پردازش موازی (مبتنی بر جی‌پی‌یو) پیاده‌سازی شده است. نتایج شبیه‌سازی‌ها، ارتقای قابل ملاحظه سرعت آشکارساز فیشر را نشان می‌دهند که باعث بهبود کارآیی شبکه حس‌گری صوتی خواهد شد.   2 Nowadays, several infrastructure-based low-frequency acoustical sensor networks are employed in different applications to monitor the activity of diverse natural and man-made phenomena, such as avalanches, earthquakes, volcanic eruptions, severe storms, super-sonic aircraft flights, etc. Two signal detection methods are usually implemented in these networks for the purpose of event occurrence identification, which are the progressive multi-channel correlator (PMCC) and the so-called Fisher detector. But, the Fisher method is more important and applicable in low signal-to-noise (SNR) ratio conditions, which is of a special interest in acoustical monitoring networks. Unfortunately, an important disadvantage of this algorithm is its relative high detection-time; which limits its application for real-time detection scenarios. This disadvantage is fundamentally due to a beam forming process in Fisher algorithm, which requires doing complete search in a slowness-network, constructed from possible incoming wave front directions and speeds. To address this issue, we propose a method for implementation of this beam forming on a graphics processing unit (GPU), in order to realize a fast-computing and/or near real-time signal processing technique. In addition, we also propose a parallel-processing algorithm for further enhancement of the performance of this GPU-based Fisher detector. Simulation results confirm the performance improvement of Fisher detector, in terms of required processing time for acoustical signal detection applications.   19 30 2015/10/122015/09/21 1394/6/30 2017/10/252017/12/2 1396/9/11 حامد صادقی hamed sadeghi دانشگاه تربیت مدرس ایران h.sadeghi@mail.ru امیر اخوان بی‌تقصیر Amir Akhavan Bitaghsir دانشگاه صنعتی همدان ایران amir.akhavan@aut.ac.ir Sensor network array processing beamforming parallel processing GPU شبکه حس‌گری پردازش آرایه‌ای شکل‌دهی پرتو پردازش موازی جی‌پی‌یو [1] V. L. Zimmer, N. Sitar, "Detection and location of rock falls using seismic and infrasound sensors," Engineering Geology, vol. 193, pp. 49-60, Apr. 2015.##[2] R.D. Costley, W. G. Frazier, et al., "Frequency-wavenumber processing for infrasound distributed arrays," Journal of Acoustical Society of America, vol. 134, no. 4, pp.EL307-EL311, 2013.##[3] S. Havens, H.-P. Marshall, et al., Real-Time Avalanche Detection for High Risk Areas, Research Report, Transportation Department, Idaho University, Dec. 2014, available online at: https://itd.idaho.gov/highways/research/archived/reports/RP219Final12312014.pdf.##[4] W. W. Arrasmith, E. R. Coots, J. V. Olson, and E. A. Skowbo, "Analyzing infrasound and seismic signals emanating from a waterborne system using canonical modeling and analysis methods", International Journal of Modeling and Optimization, vol. 4, no. 3, pp. 176-181, Jun. 2014.##[5] M. Charbit, I Che, and A Le Pichon, "Asymptotic distribution of GLRT versus Fisher distribution for infrasonic detection," Geophysical Research Abstracts, vol. 15, EGU2013-3690, 2013.##[6] J. Park, B. W. Stump, C. Hayward, Detection of regional infrasound signals using array data: testing, tuning and physical interpretation, Journal of Acoustical Society of America, vol. 140, no. 1, pp. 240-259, Jul. 2016.##[7] S. J. Arrowsmith, R. Whitaker, S. R. Taylor, "Regional monitoring of infrasound events using multiple arrays: application to Utah and Washington State", Geophysical Journal International, pp. 291–300, Jul. 2008.##[8] S. J. Arrowsmith, R. Whitaker, C. Katz, C. Hayward, "The F-detector revisited: An improved strategy for signal detection at seismic and infrasound arrays", Seismological Society of America, vol. 99, no. 1, pp. 449–453, 2009.##[9] B. Melton, and L. Baily, "Multiple signal correlators," Geophysics, XXII (3), pp. 565-588, 1957.##[10] R. R. Blandford, "An automatic event detector at the Tonto Forest seismic observatory", Geophysics, vol. 39, pp. 633, 1974.##[11] S. Angelis et al., "Detecting hidden volcanic explosions from Mt. Cleveland Volcano, Alaska with infrasound and ground-coupled airwaves," Geophysical Research Letters, vol. 39, 2012.##[12] L. G. Evers and H. W. Haak, "Tracing a meteoric trajectory with infrasound", Geophysical Research Letters, vol. 30, no. 24, Dec. 2003.##[13] Y. Cansi, "An automatic seismic event processing for detection and location: the PMCC method", Geophysical Research Letters, vol. 22, pp. 1021-1024,1995.##[14] J. Nickolls, W.J. Dally, "The GPU computing era," IEEE Micro Magazine, vol. 30, pp. 56-69, 2010.##[15] H. Chen, S. Saïghi, L. Buhry, and S. Renaud, "Real-time simulation of biologically realistic stochastic neurons in VLSI," IEEE Transactions on Neural Networks, vol. 21, no. 9, pp. 1511–1517, Sep. 2010.##[16] S. U. Gjerald, R. Brekken, T. Hergum, J. D'hoog "Real-time ultrasound simulation using the GPU," IEEE Transactions on Ultrasonic Ferroelectrics and Frequency Control, vol. 59, pp. 885-892, 2012.##[17] Y. Dai, et al., "Real-time visualized freehand 3D ultrasound reconstruction based on GPU," IEEE Transactions on Information Technology in Biomedicine, vol. 14, pp. 1338-1345, 2010.##[18] C. Richter, S. Schops, and M. Clemens, "GPU acceleration of finite difference schemes used in coupled electromagnetic/ thermal field simulations", IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 1649-1652, May 2013.##[19] W. Rodrigues, et al., "Accelerating atomistic calculation of quantum energy eigenstates on graphic cards", Computer physics Communications, pp. 2510-2518, May 2014.##[20] A. Artu, "Parallel wavelet-based clustering algorithm on GPUs using CUDA", Procedia Computer Science, vol. 3, pp. 396-400, 2011.##[21] L. Mussi, F. Daolio, S. Cagnoni, "Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture", Information Sciences, pp. 4642-4657, 2011.##[22] D. B. Kirk, W.H. Wen-Mei, Programming Massively Parallel Processors: A Hands-on Approach, 2nd Edition, Morgan Kaufmann, 2011.##[1] V. L. Zimmer, N. Sitar, "Detection and location of rock falls using seismic and infrasound sensors," Engineering Geology, vol. 193, pp. 49-60, Apr. 2015.##[2] R.D. Costley, W. G. Frazier, et al., "Frequency-wavenumber processing for infrasound distributed arrays," Journal of Acoustical Society of America, vol. 134, no. 4, pp.EL307-EL311, 2013.##[3] S. Havens, H.-P. Marshall, et al., Real-Time Avalanche Detection for High Risk Areas, Research Report, Transportation Department, Idaho University, Dec. 2014, available online at: https://itd.idaho.gov/highways/research/archived/reports/RP219Final12312014.pdf.##[4] W. W. Arrasmith, E. R. Coots, J. V. Olson, and E. A. Skowbo, "Analyzing infrasound and seismic signals emanating from a waterborne system using canonical modeling and analysis methods", International Journal of Modeling and Optimization, vol. 4, no. 3, pp. 176-181, Jun. 2014.##[5] M. Charbit, I Che, and A Le Pichon, "Asymptotic distribution of GLRT versus Fisher distribution for infrasonic detection," Geophysical Research Abstracts, vol. 15, EGU2013-3690, 2013.##[6] J. Park, B. W. Stump, C. Hayward, Detection of regional infrasound signals using array data: testing, tuning and physical interpretation, Journal of Acoustical Society of America, vol. 140, no. 1, pp. 240-259, Jul. 2016.##[7] S. J. Arrowsmith, R. Whitaker, S. R. Taylor, "Regional monitoring of infrasound events using multiple arrays: application to Utah and Washington State", Geophysical Journal International, pp. 291–300, Jul. 2008.##[8] S. J. Arrowsmith, R. Whitaker, C. Katz, C. Hayward, "The F-detector revisited: An improved strategy for signal detection at seismic and infrasound arrays", Seismological Society of America, vol. 99, no. 1, pp. 449–453, 2009.##[9] B. Melton, and L. Baily, "Multiple signal correlators," Geophysics, XXII (3), pp. 565-588, 1957.##[10] R. R. Blandford, "An automatic event detector at the Tonto Forest seismic observatory", Geophysics, vol. 39, pp. 633, 1974.##[11] S. Angelis et al., "Detecting hidden volcanic explosions from Mt. Cleveland Volcano, Alaska with infrasound and ground-coupled airwaves," Geophysical Research Letters, vol. 39, 2012.##[12] L. G. Evers and H. W. Haak, "Tracing a meteoric trajectory with infrasound", Geophysical Research Letters, vol. 30, no. 24, Dec. 2003.##[13] Y. Cansi, "An automatic seismic event processing for detection and location: the PMCC method", Geophysical Research Letters, vol. 22, pp. 1021-1024,1995.##[14] J. Nickolls, W.J. Dally, "The GPU computing era," IEEE Micro Magazine, vol. 30, pp. 56-69, 2010.##[15] H. Chen, S. Saïghi, L. Buhry, and S. Renaud, "Real-time simulation of biologically realistic stochastic neurons in VLSI," IEEE Transactions on Neural Networks, vol. 21, no. 9, pp. 1511–1517, Sep. 2010.##[16] S. U. Gjerald, R. Brekken, T. Hergum, J. D'hoog "Real-time ultrasound simulation using the GPU," IEEE Transactions on Ultrasonic Ferroelectrics and Frequency Control, vol. 59, pp. 885-892, 2012.##[17] Y. Dai, et al., "Real-time visualized freehand 3D ultrasound reconstruction based on GPU," IEEE Transactions on Information Technology in Biomedicine, vol. 14, pp. 1338-1345, 2010.##[18] C. Richter, S. Schops, and M. Clemens, "GPU acceleration of finite difference schemes used in coupled electromagnetic/ thermal field simulations", IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 1649-1652, May 2013.##[19] W. Rodrigues, et al., "Accelerating atomistic calculation of quantum energy eigenstates on graphic cards", Computer physics Communications, pp. 2510-2518, May 2014.##[20] A. Artu, "Parallel wavelet-based clustering algorithm on GPUs using CUDA", Procedia Computer Science, vol. 3, pp. 396-400, 2011.##[21] L. Mussi, F. Daolio, S. Cagnoni, "Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture", Information Sciences, pp. 4642-4657, 2011.##[22] D. B. Kirk, W.H. Wen-Mei, Programming Massively Parallel Processors: A Hands-on Approach, 2nd Edition, Morgan Kaufmann, 2011.## ##

خوشه‌بندی داده‌ها بر پایه شناسایی کلید Data Clustering Based On Key Identification 1 1 خوشه‌بندی یکی از عناصر اصلی سازنده در بینایی رایانه و یادگیری ماشین است. چالش اصلی، یافتن راهی مناسب برای پیدا‌کردن زیر‌مجموعه‌ای از نمونه‌‌های شاخص و ساختارهای خوشه‌ای مرتبط با آنها، با درنظر‌گرفتن یک معیار فاصله دوبه‌دو، است. در این مقاله شیوه‌ای جدید برای خوشه‌بندی پیشنهاد می‌شود که به‌صورت تکرار‌شونده، عناصر کلیدی یک مجموعه داده‌ای را بر پایه یک تابع هدف مناسب، پیدا می کند. آزمایش‌های تجربی متعدد بیان‌گر برتری روش پیشنهاد‌شده نسبت به روش‌های موجود، هم از نظر بهینگی و هم از نظر مؤثر‌بودن، است. علاوه‌بر‌این، روش پیشنهادی برای خوشه‌بندی داده‌های با مقیاس بالا توسعه داده می‌شود؛ به‌صورتی‌که میلیون‌ها داده را در چند ثانیه می‌توان پردازش کرد.   2 Clustering has been one of the main building blocks in the fields of machine learning and computer vision. Given a pair-wise distance measure, it is challenging to find a proper way to identify a subset of representative exemplars and its associated cluster structures. Recent trend on big data analysis poses a more demanding requirement on new clustering algorithm to be both scalable and accurate. A recent advance in graph-based clustering extends its ability to millions of data points by massive utility of engineering endeavor and parallel optimization. However, most other existing clustering algorithms, though promising in theory, are limited in the scalability issue. In this paper, a novel clustering method is proposed that is both accurate and scalable. Based on a simple criteria, ”key” items that are representative of the whole data set are iteratively selected and thus form associated cluster structures. Taking input of pairwise distance measure between data instances, the proposed method searches centers of clusters by identifying data items far away from selected keys, but representative of unselected data items. Inspired by hierarchical clustering, small clusters are iteratively merged until a desired number of clusters are obtained. To solve the scalability problem, a novel tracking table technique is designed to reduce the time complexity which is capable of clustering millions of data points within a few minutes. To assess the performance of the proposed method, several experiments are conducted. The first experiment tests the ability of our algorithm on different manifold structures and various number of clusters. It is observed that our clustering algorithm outperforms existing alternatives in capturing different shapes of data distributions. In the second experiment, the scalability of our algorithm to large scale data points is assessed by clustering up to one million data points with dimensions of up to 100. It is shown that, even with one million data points, the proposed method only takes a few minutes to perform clustering. The third experiment is conducted on the ORL database, which consists of 400 face images of 40 individuals. The proposed clustering method outperforms the compared alternatives in this experiment as well. In the final experiment, shape clustering is performed on the MPEG-7 dataset, which contains 1400 silhouette images from 70 classes, 20 different shapes for each class. The goal here is to cluster the data items (here the binary shapes) into 70 clusters, so that each cluster only includes shapes that belong to one class. The proposed method outperforms other alternative clustering algorithms on this dataset as well. Extensive empirical experiments demonstrate the superiority of the proposed method over existing alternatives, in terms of both effectiveness and efficiency. Furthermore, our algorithm is capable of large-scale data clustering where millions of data points can be clustered in a few seconds.   31 42 2015/10/122015/09/212016/06/5 1395/3/16 2017/10/252017/12/22017/07/8 1396/4/17 احسان فضل ارثی Ehsan Fazl-Ersi دانشگاه فردوسی مشهد ایران fazlersi@um.ac.ir مسعود کاظمی نوقابی Masoud Kazemi Nooghabi دانشگاه فردوسی مشهد ایران masoud.kazemi@stu.um.ac.ir Clustering Key Identification Large Scale خوشه بندی؛ شناسایی کلید؛ مقیاس بالا [1] چاقری آرش، فیضی درخشی محمدرضا. خوشه‌بندی خودکار داده‌ها با بهره‌گیری از الگوریتم رقابت استعماری بهبودیافته. پردازش علائم و داده‌ها; ۱۴ (۲) :۱۵۹-۱۶۹; 1396##[1] Chaghari A, Feizi-Derakhshi M. Automatic Clustering Using Improved Imperialist Competitive Algorithm. JSDP; 14 (2) :159-169, 2017.##[2] Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & Vassilvitskii, S. Scalable k-means++. Proceedings of the VLDB Endowment, 5(7), 622-633, 2012.##[3] Belongie, S., Malik, J., & Puzicha, J. Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence, 24(4), 509-522, 2002.##[4] Comaniciu, D., & Meer, P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5), 603-619, 2002.##[5] El-Naqa, I., Yang, Y., Galatsanos, N. P., Nishikawa, R. M., & Wernick, M. N. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE transactions on medical imaging, 23(10), 1233-1244, 2004.##[6] Frey, B. J., & Dueck, D. Clustering by passing messages between data points. Science, 315(5814), 972-976, 2007.##[7] Huang, G., Song, S., Gupta, J. N., & Wu, C. Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44(12), 2405-2417, 2014.##[8] Jain, A. K., Murty, M. N., & Flynn, P. J. Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323, 1999.##[9] Jain, A. K. Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666, 2010.##[10] Johnson, S. C. Hierarchical clustering schemes. Psychometrika, 32(3), 241-254, 1967.##[11] Koyuturk, M., Grama, A., & Ramakrishnan, N. Compression, clustering, and pattern discovery in very high-dimensional discrete-attribute data sets. IEEE Transactions on Knowledge and Data Engineering, 17(4), 447-461, 2005.##[12] Latecki, L. J., Lakamper, R., & Eckhardt, T. Shape descriptors for non-rigid shapes with a single closed contour. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on (Vol. 1, pp. 424-429). IEEE, 2000.##[13] Ling, H., & Jacobs, D. W. Shape classification using the inner-distance. IEEE transactions on pattern analysis and machine intelligence, 29(2), 2007.##[14] Liu, H., Liu, T., Wu, J., Tao, D., & Fu, Y. Spectral ensemble clustering. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 715-724). ACM, 2015, August.##[15] Liu, H., Shao, M., Li, S., & Fu, Y. Infinite ensemble for image clustering. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, August.##[16] Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. Multimodality image registration by maximization of mutual information. IEEE transactions on medical imaging, 16(2), 187-198, 1997.##[17] Peng, Y., Zheng, W. L., & Lu, B. L. An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing, 174, 250-264, 2016.##https://doi.org/10.1016/j.neucom.2014.11.097##[18] Reynolds, D. Gaussian mixture models. Encyclopedia of biometrics, 827-832, 2015.##[19] Samaria, F. S., & Harter, A. C. Parameterisation of a stochastic model for human face identification. In Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on (pp. 138-142). IEEE, 1994, December.##[20] Sculley, D. Web-scale k-means clustering. In Proceedings of the 19th international conference on World Wide Web (pp. 1177-1178). ACM, 2010, April.##[21] Soheily-Khah, S., Douzal-Chouakria, A., & Gaussier, E. Generalized k-means-based clustering for temporal data under weighted and kernel time warp. Pattern Recognition Letters, 75, 63-69, 2016.##[22] Steinbach, M., Karypis, G., & Kumar, V. A comparison of document clustering techniques. In KDD workshop on text mining (Vol. 400, No. 1, pp. 525-526), 2000, August.##[23] Tuma, M. N., Scholz, S. W., & Decker, R. THE APPLICATION OF CLUSTER ANALYSIS IN MARKETING RESEARCH: A LITERATURE ANALYSIS. B> Quest, 2009.##[24] Von Luxburg, U. A tutorial on spectral clustering. Statistics and computing, 17(4), 395-416, 2007.##[25] Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., & Goldenberg, A. Similarity network fusion for aggregating data types on a genomic scale. Nature methods, 11(3), 333-337, [26] Wu, J., Liu, H., Xiong, H., Cao, J., & Chen, J. K-means-based consensus clustering: A unified view. IEEE Transactions on Knowledge and Data Engineering, 27(1), 155-169, 2015.##[27] Zhang, Z., Pati, D., & Srivastava, A. Bayesian clustering of shapes of curves. Journal of Statistical Planning and Inference, 166, 171-186, 2015.##[1] چاقری آرش، فیضی درخشی محمدرضا. خوشه‌بندی خودکار داده‌ها با بهره‌گیری از الگوریتم رقابت استعماری بهبودیافته. پردازش علائم و داده‌ها; ۱۴ (۲) :۱۵۹-۱۶۹; 1396##[1] Chaghari A, Feizi-Derakhshi M. Automatic Clustering Using Improved Imperialist Competitive Algorithm. JSDP; 14 (2) :159-169, 2017.##[2] Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & Vassilvitskii, S. Scalable k-means++. Proceedings of the VLDB Endowment, 5(7), 622-633, 2012.##[3] Belongie, S., Malik, J., & Puzicha, J. Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence, 24(4), 509-522, 2002.##[4] Comaniciu, D., & Meer, P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5), 603-619, 2002.##[5] El-Naqa, I., Yang, Y., Galatsanos, N. P., Nishikawa, R. M., & Wernick, M. N. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE transactions on medical imaging, 23(10), 1233-1244, 2004.##[6] Frey, B. J., & Dueck, D. Clustering by passing messages between data points. Science, 315(5814), 972-976, 2007.##[7] Huang, G., Song, S., Gupta, J. N., & Wu, C. Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44(12), 2405-2417, 2014.##[8] Jain, A. K., Murty, M. N., & Flynn, P. J. Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323, 1999.##[9] Jain, A. K. Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666, 2010.##[10] Johnson, S. C. Hierarchical clustering schemes. Psychometrika, 32(3), 241-254, 1967.##[11] Koyuturk, M., Grama, A., & Ramakrishnan, N. Compression, clustering, and pattern discovery in very high-dimensional discrete-attribute data sets. IEEE Transactions on Knowledge and Data Engineering, 17(4), 447-461, 2005.##[12] Latecki, L. J., Lakamper, R., & Eckhardt, T. Shape descriptors for non-rigid shapes with a single closed contour. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on (Vol. 1, pp. 424-429). IEEE, 2000.##[13] Ling, H., & Jacobs, D. W. Shape classification using the inner-distance. IEEE transactions on pattern analysis and machine intelligence, 29(2), 2007.##[14] Liu, H., Liu, T., Wu, J., Tao, D., & Fu, Y. Spectral ensemble clustering. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 715-724). ACM, 2015, August.##[15] Liu, H., Shao, M., Li, S., & Fu, Y. Infinite ensemble for image clustering. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, August.##[16] Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. Multimodality image registration by maximization of mutual information. IEEE transactions on medical imaging, 16(2), 187-198, 1997.##[17] Peng, Y., Zheng, W. L., & Lu, B. L. An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing, 174, 250-264, 2016.##https://doi.org/10.1016/j.neucom.2014.11.097##[18] Reynolds, D. Gaussian mixture models. Encyclopedia of biometrics, 827-832, 2015.##[19] Samaria, F. S., & Harter, A. C. Parameterisation of a stochastic model for human face identification. In Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on (pp. 138-142). IEEE, 1994, December.##[20] Sculley, D. Web-scale k-means clustering. In Proceedings of the 19th international conference on World Wide Web (pp. 1177-1178). ACM, 2010, April.##[21] Soheily-Khah, S., Douzal-Chouakria, A., & Gaussier, E. Generalized k-means-based clustering for temporal data under weighted and kernel time warp. Pattern Recognition Letters, 75, 63-69, 2016.##[22] Steinbach, M., Karypis, G., & Kumar, V. A comparison of document clustering techniques. In KDD workshop on text mining (Vol. 400, No. 1, pp. 525-526), 2000, August.##[23] Tuma, M. N., Scholz, S. W., & Decker, R. THE APPLICATION OF CLUSTER ANALYSIS IN MARKETING RESEARCH: A LITERATURE ANALYSIS. B> Quest, 2009.##[24] Von Luxburg, U. A tutorial on spectral clustering. Statistics and computing, 17(4), 395-416, 2007.##[25] Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., & Goldenberg, A. Similarity network fusion for aggregating data types on a genomic scale. Nature methods, 11(3), 333-337, [26] Wu, J., Liu, H., Xiong, H., Cao, J., & Chen, J. K-means-based consensus clustering: A unified view. IEEE Transactions on Knowledge and Data Engineering, 27(1), 155-169, 2015.##[27] Zhang, Z., Pati, D., & Srivastava, A. Bayesian clustering of shapes of curves. Journal of Statistical Planning and Inference, 166, 171-186, 2015.## ##

بهبود شناسایی موجودیت‌های نامدار فارسی با استفاده از کسره اضافه Improving Named Entity Recognition Using Izafe in Farsi 1 1 تشخیص موجودیت‌های نامدار فرآیندی است که در آن اسامی اشخاص، مکان‌ها(شهرها، کشورها، دریاها و غیره)، سازمان‌ها(شرکت‌های خصوصی و دولتی، نهادهای بین‌المللی و غیره)، تاریخ، واحدهای پولی و درصدها در یک متن شناسایی می‌شوند. تشخیص موجودیت‌های نامدار نقشی اساسی در سامانه‌های پرسش و پاسخ، خلاصه‌سازی، ترجمه ماشینی، برچسب‌زن نقش معنایی، جستجوی معنایی، استخراج رابطه و شناسایی نقل قول دارند. در این مقاله ابتدا فرهنگ واژگان موجودیت‌های سازمان، مکان و اشخاص با استفاده از محتوای ویکی‌پدیای فارسی استخراج شد؛ سپس با استفاده از قواعد، سامانه پیشنهادی توسعه یافت. در ادامه دقت شناسایی موجودیت‌های نامدار با استفاده از کسره اضافه که یکی از ویژگی‌های مهم زبان فارسی است، بهبود داده شد. جهت ارزیابی سامانه تعداد 42 هزار کلمه از پیکره بی‌ جن‌خان به‌صورت دستی برچسب زده شدند و معیار F 92/81 درصد به‌دست آمد. نتایج حاکی از آن است که با استفاده از کسره اضافه در سامانه‌های تشخیص موجودیت  دقت آن‌ها به‌طور قابل ملاحظه‌ای افزایش می‌یابد.   2 Named entity recognition is a process in which the people’s names, name of places (cities, countries, seas, etc.) and organizations (public and private companies, international institutions, etc.), date, currency and percentages in a text are identified. Named entity recognition plays an important role in many NLP tasks such as semantic role labeling, question answering, summarization, machine translation, semantic search, and relation extraction and quotation recognition systems. Named entity recognition in the Persian language is far more complex and more difficult than English. In English texts usually proper nouns begin with capital letters and this feature makes it easy to identify named entities, but this feature is absent in Persian language texts. To create a named entity recognition system, generally three methods are being used which include rule-based, machine-learning-based and hybrid methods. Each of these methods has its own advantages and disadvantages. Lack of named entity labeled data is the greatest challenge in Persian text. Because of this problem usually rule-based methods used to extract entities. In this paper firstly, the dictionary of organizations, places and people were extracted from Wikipedia. Wikipedia is one of the best sources for extracting entities in which more than 200000 Farsi-named entities are known to exist. The proposed algorithm classify each Wikipedia article title by using its categories. Each of Wikipedia titles has several categories that can be used to partially identify the named entity type. Then named entity recognition accuracy (precision) was increased using the rules. These rules can be divided into 3 categories that include morphological rules, adjacency and text patterns. The most important rules are adjacency rules. By using these rules the type of entity with the word nearby each entity (like Mr, Mrs , …) can be identified. To evaluate the system, 42000 tokens of BijanKhan corpus were manually annotated (labeled). Early F-measure was calculated 78.79 percent. In continue, named entity recognition accuracy (precision) improved using izāfe which is one of the important Persian language features and 81.94 percent for F-measure was achieved. The results showed that using izāfe in named entity recognition systems significantly increases their accuracy.   43 54 2015/10/122015/09/212016/06/52016/03/1 1394/12/11 2017/10/252017/12/22017/07/82017/05/5 1396/2/15 محمد عبدوس mohammad َAbdoos دانشگاه علم و صنعت ایران و آزمایشگاه پردازش و تحلیل متن شرکت آرمان رایان شریف ایران mohammadabdous@comp.iust.ac.ir بهروز مینایی بیدگلی behrooz manaei دانشگاه علم و صنعت ایران ایران B_minaei@iust.ac.ir Named Entity Recognition Natural Language Processing Rule Based Wikipedia Izafe تشخیص موجودیت‌های نامدار پردازش زبان طبیعی مبتنی بر قاعده ویکی‌پدیا کسره اضافه [1] Esfahani.A, Rahati.S, Jahangiri.N. "Identification and classification names in Persian texts ." Signal and Data Processing Journal ,No 13,78-77, 1389##[2] Mortazavi.P, Shamsfard.M."Named Entity Recognition In Persian Texts". 15nd National Computer Society of Iran Conference.tehran. Power Technology Development Center.Tehran. 1388##[3] Bijankhan.M, Sheykhzadegan.J, Bahrani.M and Ghayoomi.M. "Lessons from Building a Persian Written Corpus:Peykare." Language Resources and Evaluation.2011. pp. 143-164.##[4] Chieu, Hai Leong, and Hwee Tou Ng. "Named entity recognition: a maximum entropy approach using global information." Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 2002.##[5] Das, Arjun, Debasis Ganguly, and Utpal Garain. "Named Entity Recognition with Word Embeddings and Wikipedia Categories for a Low-Resource Language." ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 16.3 (2017): 18.##[6] Elsebai, Ali. "Arabic Proper Names Recognition Using Heuristics." Proceeding of the 9th Annual Post Graduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting (PGNET), ISBN. 2008.##[7] B. Farber, D. Freitag et al."Improving NER in Arabic Using a Morphological Tageer". the 6th International Conference on Language Resources and Evaluation,LREC. 2008.##[8] Farmakiotou, Dimitra, et al. "Rule-based named entity recognition for Greek financial texts." Proceedings of the Workshop on Computational lexicography and Multimedia Dictionaries (COMLEX 2000). 2000.##[9] Grishman R, Sundheim B." Message Understanding Conference-6: A Brief History". InCOLING 1996 Aug 5 (Vol. 96, pp. 466-471).1996##[10] Mansouri, Alireza, Lilly Suriani Affendey, and Ali Mamat. "Named entity recognition approaches." International Journal of Computer Science and Network Security 8.2: 339-344. 2008##[11] Mikheev, Andrei, Marc Moens, and Claire Grover. "Named entity recognition without gazetteers." Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1999.##[12] Rau, Lisa F. "Extracting company names from text." Artificial Intelligence Applications, 1991. Proceedings., Seventh IEEE Conference on. Vol. 1. IEEE, 1991.##[13] Shaalan, Khaled, and Hafsa Raza. "Person name entity recognition for Arabic." Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources. Association for Computational Linguistics, 2007.##[14] Tjong Kim Sang, Erik F., and Fien De Meulder. "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4. Association for Computational Linguistics, 2003.##[۱] اصفهانی سیدعبدالحمید, راحتی قوچانی سعید, جهانگیری نادر.«سیستم شناسایی و طبقه‌بندی اسامی در متون فارسی». فصلنامه پردازش علایم و داده‌ها. شماره 13. 77-78. 1389##[2] سادات مرتضوی پونه و شمس‌فرد مهرنوش. «شناسایی موجودیت‌های نامدار در متون فارسی». پانزدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران. تهران. انجمن کامپیوتر. مرکز توسعه فناوری نیرو. ۱۳۸۸##[1] Esfahani.A, Rahati.S, Jahangiri.N. "Identification and classification names in Persian texts ." Signal and Data Processing Journal ,No 13,78-77, 1389##[2] Mortazavi.P, Shamsfard.M."Named Entity Recognition In Persian Texts". 15nd National Computer Society of Iran Conference.tehran. Power Technology Development Center.Tehran. 1388##[3] Bijankhan.M, Sheykhzadegan.J, Bahrani.M and Ghayoomi.M. "Lessons from Building a Persian Written Corpus:Peykare." Language Resources and Evaluation.2011. pp. 143-164.##[4] Chieu, Hai Leong, and Hwee Tou Ng. "Named entity recognition: a maximum entropy approach using global information." Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 2002.##[5] Das, Arjun, Debasis Ganguly, and Utpal Garain. "Named Entity Recognition with Word Embeddings and Wikipedia Categories for a Low-Resource Language." ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 16.3 (2017): 18.##[6] Elsebai, Ali. "Arabic Proper Names Recognition Using Heuristics." Proceeding of the 9th Annual Post Graduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting (PGNET), ISBN. 2008.##[7] B. Farber, D. Freitag et al."Improving NER in Arabic Using a Morphological Tageer". the 6th International Conference on Language Resources and Evaluation,LREC. 2008.##[8] Farmakiotou, Dimitra, et al. "Rule-based named entity recognition for Greek financial texts." Proceedings of the Workshop on Computational lexicography and Multimedia Dictionaries (COMLEX 2000). 2000.##[9] Grishman R, Sundheim B." Message Understanding Conference-6: A Brief History". InCOLING 1996 Aug 5 (Vol. 96, pp. 466-471).1996##[10] Mansouri, Alireza, Lilly Suriani Affendey, and Ali Mamat. "Named entity recognition approaches." International Journal of Computer Science and Network Security 8.2: 339-344. 2008##[11] Mikheev, Andrei, Marc Moens, and Claire Grover. "Named entity recognition without gazetteers." Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1999.##[12] Rau, Lisa F. "Extracting company names from text." Artificial Intelligence Applications, 1991. Proceedings., Seventh IEEE Conference on. Vol. 1. IEEE, 1991.##[13] Shaalan, Khaled, and Hafsa Raza. "Person name entity recognition for Arabic." Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources. Association for Computational Linguistics, 2007.##[14] Tjong Kim Sang, Erik F., and Fien De Meulder. "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4. Association for Computational Linguistics, 2003.## ##

ارائه روشی برای استخراج کلمات کلیدی و وزن‌دهی کلمات برای بهبود طبقه‌بندی متون فارسی An Approach for Extraction of Keywords and Weighting Words for Improvement Farsi Documents Classification 1 1 با توجه به گسترش روزافزون اطلاعات و وجود حجم انبوه متون غیرساخت ­یافته، استفاده از کلمات کلیدی نقش مهمی در بازیابی اطلاعات دارد. این درحالی است که استخراج کلمات کلیدی به­صورت دستی مشکلات زیادی دارد. بنابرین استخراج کلمات کلیدی به­صورت خودکار از نیازهای ضروری فناوری امروزه است. در این پژوهش سعی شده با استفاده از اصطلاح­نامه که از نظامی ساختارمند برخوردار است، کلمات کلیدی بامعناتری از متون استخراج کرد و با آن­ها طبقه­بندی متون فارسی را بهبود بخشید. مراحلی که برای افزایش جامعیت جستجو باید سپری شود به این صورت است که در مرحله نخست کلمات زائد حذف و باقی کلمات ریشه­یابی می­شود؛ سپس به کمک اصطلاح­‌نامه کلمات هم­معنی، اعم­ها و اخص­ها و همچنین وابسته­ها پیدا و در ادامه برای مشخص‌شدن اهمیت نسبی کلمات یک وزن عددی به هر کلمه منسوب می‌شود که بیان­گر میزان تأثیر کلمه در ارتباط با موضوع متن و درمقایسه با سایر کلمات به­کار‌رفته در متن است‌. با توجه به مراحل بالا و به کمک اصطلاح­نامه، طبقه­بندی متون دقیق­تر انجام می­گیرد. در این روش از الگوریتم نزدیکترین همسایه (KNN) برای طبقه­بندی استفاده می­شود. الگوریتم KNN به­خاطر سادگی و مؤثر‌بودن آن در طبقه­بندی متون بسیار به­کار برده می­شود. مبنای کار این الگوریتم، مقایسه متن آزمایش داده‌شده با متون آموزشی داده‌شده و به­دست‌آوردن میزان شباهت بین آن­ها است. نتایج آزمایش‌ها برروی چندین متن در موضوع­های مختلف، نشان­دهنده دقت و توانایی روش پیشنهادی در استخراج کلمات کلیدی منطبق با خواست کاربر و در‌نتیجه طبقه­بندی دقیق­تر متون  است.   2 Due to ever-increasing information expansion and existing huge amount of unstructured documents, usage of keywords plays a very important role in information retrieval. Because of a manually-extraction of keywords faces various challenges, their automated extraction seems inevitable. In this research, it has been tried to use a thesaurus, (a structured word-net) to automatically extract them. Authors claim that extraction of more meaningful keywords out of documents can be attained via employment of a thesaurus. The keywords extracted by applying thesaurus, can improve the document classification. The steps to be taken to increase the comprehensiveness of search should be such that in the first step the stop words are removed and the remaining words are stemmed. Then, with the help of a thesaurus are found words equivalent, hierarchical and dependent. Then, to determine the relative importance of words, a numerical weight is assigned to each word, which represents effect of the word on the subject matter and in comparison with other words used in the text. According to the steps above and with the help of a thesaurus, an accurate text classification is performed. In this method, the KNN algorithm is used for the classification. Due to the simplicity and effectiveness of this algorithm (KNN), there is a great deal of use in the classification of texts. The cornerstone of KNN is to compare with the text trained and text tested to determine their similarity between. The empirical results show the quality and accuracy of extracted keywords are satisfiable for users. They also confirm that the document classification has been enhanced. In this research, it has been tried to extract more meaningful keywords out of texts using thesaurus (which is a structured word-net) rather than not using it.   55 78 2015/10/122015/09/212016/06/52016/03/12015/10/30 1394/8/8 2017/10/252017/12/22017/07/82017/05/52017/10/25 1396/8/3 وحیده رضائی vahideh rezaie دانشگاه آزاد اسلامی واحد یاسوج ایران vahidehrezaie@gmail.com مجید محمدپور mahid mohammadpour دانشگاه آزاد اسلامی واحد یاسوج ایران m.mohammadpour@iauyasooj.ac.ir حمید پروین hamid parvin دانشگاه آزاد اسلامی واحد نورآباد ممسنی ایران parvin@iust.ac.ir صمد نجاتیان samad nejatian دانشگاه آزاد اسلامی واحد یاسوج ایران samad.nej.2007@gmail.com thesaurus information retrieval extraction of keywords weight اصطلاح‌نامه بازیابی اطلاعات استخراج کلمات کلیدی وزن‌دهی [1] F. Rad, H. Parvin, A. Dehbashi, B. Minaei, "A New Method for Automatic Indexing and Extract-ing Keywords for Information Retrieval and Clustering of Texts", Journal of Signal Processing and Data, Volume 13, No. 1, page 87-100, 2017.##[2] Dehbashi Hashem, Atoosa, "Improved clustering of Persian texts based on keywords using linguis-tic information and thesaurus". Master thesis, 2010.##[3] F. Yaghmaei, S. Tabodi, "Improving the Classification of Persian Texts in Weighted Neighboring Method", The First International Conference on Line Processing and Persian Language, 2012.##[4] M.R., Alagheband, M.R Saeedi Mohammadi, M.H Dezfulian, "clustering of center-based texts using the SVD method and utilizing neighborhoods", the first international conference on processing Persian language and language, 2012.##[5] A.R, Arasteh, M.H, Elahimanesh, A. Sharif, B. Minaei-Bidgoli, "Semantically Clustering of Persian Words", Proceeding of 1st International Conference on Persian Language Processing (ICPLP), Semnan, Iran, Sep. 5-6, 2012.##[6] Berry, W. Michael, and Castellanos, Malu, eds, Survey of text mining. New York: Springer, 2004.##[7] Borko, Harold, and Bernick, Myrna, "Automatic document classification", Journal of the ACM (JACM) 10, no. 2: 151-162, 1963.##[8] Cavnar, B. William, and Trenkle, M. John, "N-gram-based text categorization", Ann Arbor MI 48113, no. 2: 161-175, 1994.##[9] F. Colace, M. D. Santo, L. Greco, P. Napoletano, "Text classification using a few labeled examples", Journal of Computers in Human Behavior, Vol. 30, January 2014, pp. 689-697, 2014.##[10] Cleverdon, Cyril, "Optimizing convenient online access to bibliographic databases', Information services and Use 4, no. 1: 37-47, 1984.##[11] D. Choi, B. Ko, H. Kim, P. Kim, Text analysis for detecting terrorism-related articles on the web, Journal of Network and Computer Applications, Vol. 38, pp. 16-21, 2014.##[12] A. Díaz, M. Buenaga, L. A. Ure-a, and M. García, "Integrating Linguistic Resources in an Uniform Way for Text Classification Tasks", In First International Conference on Language Resources & Evaluation, Granada (Spain), 1998.##[13] M. Deegan, "Keyword Extraction with Thesauri and Content Analysis", URL: http://www.rlg.or-g/en/page.php?Page_ID=17068, 2004.##[14] Escudero, Gerard, Màrquez, Lluís, and Rigau, German, "Boosting applied to word sense disambiguation", Springer Berlin Heidelberg, 2000.##[14] K. Frantzi, S. Ananiadou and H. Mima, Automatic Recognition of Multi-word Terms: the C-value/NC-value Method, Digital Libraries, 3(2), pp. 115-130, 2000.##[15] S. Forsyth, Richard, "New directions in text categorization", In Causal models and intelligent data management, pp. 151-185. Springer Berlin Heidelberg.##[16] N. Freitas, and A. Kaestner, "Automatic text summarization using a machine learning approach", 16th Brazilian Symposium on Artificial Intelligence (SBIA), Brazil. Vol. 398, 2005.##[17] Granitzer, Michael, Hierarchical text classifica-tion using methods from machine learning. Master's Thesis, Graz University of Technology, 2003.##[18] D. Hyun, "Automatic Keyword Extraction Using Category Correlation of Data", Heidelberg, pp. 224-230, 2006.##[19] Harter, Stephen P. "A probabilistic approach to automatic keyword indexing", Part II. An algorithm for probabilistic indexing. Journal of the American Society for Information Science 26, no. 5: 280-289, 1975.##[20] Hassel, Martin, and Mazdak, Nima, FarsiSum: a Persian text summarizer. Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages. Association for Computational Linguistics, 2004.##[21] Huang, Yan. "Support vector machines for text categorization based on latent semantic indexing", Electrical and Computer Engineering Department, The Johns Hopkins University, Tech. Rep, 2003.##[22] Kessler, Brett, Numberg, Geoffrey, and Schütze, Hinrich, Automatic detection of text genre. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 32-38. Association for Computational Linguistics, 1997.##https://doi.org/10.3115/979617.979622##[23] Knight, Kevin, Mining online text. Communica-tions of the ACM 42, no. 11: 58-61, 1999.##[24] Larkey, S, Leah, "Automatic essay grading using text categorization techniques", In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 90-95. ACM, 1998.##[25] Liu, Luying, Kang, Jianchu, Yu, Jing and Wang. Zhongliang, "A comparative study on unsupervised feature selection methods for text clustering. In Natural Language Processing and Knowledge Engineering", 2005. IEEE NLP-KE'05. Proceedings of 2005 IEEE International Conference on, pp. 597-601. IEEE, 2005.##[26] H. P, Luhn, "11 Keyword-in-Context Index for Technical Literature (KWIC Index)", Readings in automatic language processing 1: 159, 1996.##[27] Manning, D. Christopher, "Foundations of statistical natural language processing", Edited by Hinrich Schütze. MIT press, 1999.##[28] Maron, Melvin Earl., "Automatic indexing: an experimental inquiry", Journal of the ACM (JACM) 8, no. 3: 404-417, 1961.##[29] Myers, Kary, Kearns, Michael, Singh, Satinder, and Walker, A. Marilyn, "A boosting approach to topic spotting on subdialogues", Family Life 27, no. 3: 1, 2000.##[30] Moschitti, Alessandro, "Answer filtering via text categorization in question answering systems", In Tools with Artificial Intelligence, Proceed-ings. 15th IEEE International Conference on, pp. 241-248. IEEE, 2003.##[31] H. Parvin, B. Minaei-Bidgoli, and A. Dahbashi, "Improving persian text classification using persian thesaurus", In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391-398. Springer Berlin Heidelberg, 2011.##[32] Sable, L. Carl, and Hatzivassiloglou, Vasileios. "Text-based approaches for non-topical image categorization", International Journal on Digital Libraries 3, no. 3: 261-275, 2000.##[33] Salton, Gerard, and Yang, Chung-Shu, "On the specification of term values in automatic indexing", Journal of documentation 29, no. 4: 351-372, 1973.##[34] Schapire, E. Robert, and Singer, Yoram, "BoosTexter: A boosting-based system for text categorization", Machine learning 39, no. 2-3: 135-168, 2000.##[35] G. Tangil, J. E. Tapiador, P. Peris-Lopez, J. Blasco, Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families, Journal of Expert Systems with Applications, Vol. 41, No. 4, March 2014, pp. 1104-1117, 2014.##[36] G. Tsatsaronis, I. Varlamis, M. Vazirgiannis, "Text Relatedness Based on a Word Thesaurus", Journal of Artificial Intelligence Research, Vol. 37 pp.1-39, 2010.##[37] A. Zamanifar, B. Minaei-Bidgoli, and Sharifi, Mohsen. "A new hybrid farsi text summariza-tion technique based on term co-occurrence and conceptual property of the text. Software Engineering, Artificial Intelligence", Network-ing, and Parallel/Distributed Comput-ing, SNPD'08. Ninth ACIS International Conference on. IEEE, 2008.##[38] W. Witten, I.H. Medley, Thesaurus based automatic keyphrase indexing, ACM/IEEE-CS JCDL '06 (Joint Conference on Digital Libraries), 2006.##[39] Y. Zhang, N. Z. Heywood and E. Milios, "World Wide Web Site Summarization Web Intelligence and Agent Systems", Technical Report, 2006.##[1] راد، ف.، پروین، ح.، دهباشی، آ.، مینایی، ب.، ارائه روشی جدید برای شاخص‌گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشه‌بندی متون، نشریه پردازش علائم و داده‌ها، دوره 13، شماره 1، صفحه 87-100، 1395.##[2] دهباشی هاشم، آتوسا، بهبود خوشه بندی متون فارسی بر اساس کلمات کلیدی با استفاده از اطلاعات زبان شناختی و اصطلاح‌نامه. پایان‌نامه کارشناسی ارشد، 1389.##[3] یغمایی، ف.، تعبدی س، بهبود دسته‌بندی متون فارسی در روش همسایگی وزن‌دار، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، 1391.##[4] علاقه بند، م.ر.، سعیدی محمدی، م.ر.، دزفولیان، م.ح.، خوشه‌بندی متون مبتنی بر مرکز دسته با استفاده از روش SVD و بهره‌گیری از نقاط همسایگی، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، 1391.##[1] F. Rad, H. Parvin, A. Dehbashi, B. Minaei, "A New Method for Automatic Indexing and Extract-ing Keywords for Information Retrieval and Clustering of Texts", Journal of Signal Processing and Data, Volume 13, No. 1, page 87-100, 2017.##[2] Dehbashi Hashem, Atoosa, "Improved clustering of Persian texts based on keywords using linguis-tic information and thesaurus". Master thesis, 2010.##[3] F. Yaghmaei, S. Tabodi, "Improving the Classification of Persian Texts in Weighted Neighboring Method", The First International Conference on Line Processing and Persian Language, 2012.##[4] M.R., Alagheband, M.R Saeedi Mohammadi, M.H Dezfulian, "clustering of center-based texts using the SVD method and utilizing neighborhoods", the first international conference on processing Persian language and language, 2012.##[5] A.R, Arasteh, M.H, Elahimanesh, A. Sharif, B. Minaei-Bidgoli, "Semantically Clustering of Persian Words", Proceeding of 1st International Conference on Persian Language Processing (ICPLP), Semnan, Iran, Sep. 5-6, 2012.##[6] Berry, W. Michael, and Castellanos, Malu, eds, Survey of text mining. New York: Springer, 2004.##[7] Borko, Harold, and Bernick, Myrna, "Automatic document classification", Journal of the ACM (JACM) 10, no. 2: 151-162, 1963.##[8] Cavnar, B. William, and Trenkle, M. John, "N-gram-based text categorization", Ann Arbor MI 48113, no. 2: 161-175, 1994.##[9] F. Colace, M. D. Santo, L. Greco, P. Napoletano, "Text classification using a few labeled examples", Journal of Computers in Human Behavior, Vol. 30, January 2014, pp. 689-697, 2014.##[10] Cleverdon, Cyril, "Optimizing convenient online access to bibliographic databases', Information services and Use 4, no. 1: 37-47, 1984.##[11] D. Choi, B. Ko, H. Kim, P. Kim, Text analysis for detecting terrorism-related articles on the web, Journal of Network and Computer Applications, Vol. 38, pp. 16-21, 2014.##[12] A. Díaz, M. Buenaga, L. A. Ure-a, and M. García, "Integrating Linguistic Resources in an Uniform Way for Text Classification Tasks", In First International Conference on Language Resources & Evaluation, Granada (Spain), 1998.##[13] M. Deegan, "Keyword Extraction with Thesauri and Content Analysis", URL: http://www.rlg.or-g/en/page.php?Page_ID=17068, 2004.##[14] Escudero, Gerard, Màrquez, Lluís, and Rigau, German, "Boosting applied to word sense disambiguation", Springer Berlin Heidelberg, 2000.##[14] K. Frantzi, S. Ananiadou and H. Mima, Automatic Recognition of Multi-word Terms: the C-value/NC-value Method, Digital Libraries, 3(2), pp. 115-130, 2000.##[15] S. Forsyth, Richard, "New directions in text categorization", In Causal models and intelligent data management, pp. 151-185. Springer Berlin Heidelberg.##[16] N. Freitas, and A. Kaestner, "Automatic text summarization using a machine learning approach", 16th Brazilian Symposium on Artificial Intelligence (SBIA), Brazil. Vol. 398, 2005.##[17] Granitzer, Michael, Hierarchical text classifica-tion using methods from machine learning. Master's Thesis, Graz University of Technology, 2003.##[18] D. Hyun, "Automatic Keyword Extraction Using Category Correlation of Data", Heidelberg, pp. 224-230, 2006.##[19] Harter, Stephen P. "A probabilistic approach to automatic keyword indexing", Part II. An algorithm for probabilistic indexing. Journal of the American Society for Information Science 26, no. 5: 280-289, 1975.##[20] Hassel, Martin, and Mazdak, Nima, FarsiSum: a Persian text summarizer. Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages. Association for Computational Linguistics, 2004.##[21] Huang, Yan. "Support vector machines for text categorization based on latent semantic indexing", Electrical and Computer Engineering Department, The Johns Hopkins University, Tech. Rep, 2003.##[22] Kessler, Brett, Numberg, Geoffrey, and Schütze, Hinrich, Automatic detection of text genre. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 32-38. Association for Computational Linguistics, 1997.##https://doi.org/10.3115/979617.979622##[23] Knight, Kevin, Mining online text. Communica-tions of the ACM 42, no. 11: 58-61, 1999.##[24] Larkey, S, Leah, "Automatic essay grading using text categorization techniques", In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 90-95. ACM, 1998.##[25] Liu, Luying, Kang, Jianchu, Yu, Jing and Wang. Zhongliang, "A comparative study on unsupervised feature selection methods for text clustering. In Natural Language Processing and Knowledge Engineering", 2005. IEEE NLP-KE'05. Proceedings of 2005 IEEE International Conference on, pp. 597-601. IEEE, 2005.##[26] H. P, Luhn, "11 Keyword-in-Context Index for Technical Literature (KWIC Index)", Readings in automatic language processing 1: 159, 1996.##[27] Manning, D. Christopher, "Foundations of statistical natural language processing", Edited by Hinrich Schütze. MIT press, 1999.##[28] Maron, Melvin Earl., "Automatic indexing: an experimental inquiry", Journal of the ACM (JACM) 8, no. 3: 404-417, 1961.##[29] Myers, Kary, Kearns, Michael, Singh, Satinder, and Walker, A. Marilyn, "A boosting approach to topic spotting on subdialogues", Family Life 27, no. 3: 1, 2000.##[30] Moschitti, Alessandro, "Answer filtering via text categorization in question answering systems", In Tools with Artificial Intelligence, Proceed-ings. 15th IEEE International Conference on, pp. 241-248. IEEE, 2003.##[31] H. Parvin, B. Minaei-Bidgoli, and A. Dahbashi, "Improving persian text classification using persian thesaurus", In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391-398. Springer Berlin Heidelberg, 2011.##[32] Sable, L. Carl, and Hatzivassiloglou, Vasileios. "Text-based approaches for non-topical image categorization", International Journal on Digital Libraries 3, no. 3: 261-275, 2000.##[33] Salton, Gerard, and Yang, Chung-Shu, "On the specification of term values in automatic indexing", Journal of documentation 29, no. 4: 351-372, 1973.##[34] Schapire, E. Robert, and Singer, Yoram, "BoosTexter: A boosting-based system for text categorization", Machine learning 39, no. 2-3: 135-168, 2000.##[35] G. Tangil, J. E. Tapiador, P. Peris-Lopez, J. Blasco, Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families, Journal of Expert Systems with Applications, Vol. 41, No. 4, March 2014, pp. 1104-1117, 2014.##[36] G. Tsatsaronis, I. Varlamis, M. Vazirgiannis, "Text Relatedness Based on a Word Thesaurus", Journal of Artificial Intelligence Research, Vol. 37 pp.1-39, 2010.##[37] A. Zamanifar, B. Minaei-Bidgoli, and Sharifi, Mohsen. "A new hybrid farsi text summariza-tion technique based on term co-occurrence and conceptual property of the text. Software Engineering, Artificial Intelligence", Network-ing, and Parallel/Distributed Comput-ing, SNPD'08. Ninth ACIS International Conference on. IEEE, 2008.##[38] W. Witten, I.H. Medley, Thesaurus based automatic keyphrase indexing, ACM/IEEE-CS JCDL '06 (Joint Conference on Digital Libraries), 2006.##[39] Y. Zhang, N. Z. Heywood and E. Milios, "World Wide Web Site Summarization Web Intelligence and Agent Systems", Technical Report, 2006.## ##

تبدیل خودکار درخت‌بانک وابستگی فارسی به درخت‌بانک سازه‌ای Converting Dependency Treebank to Constituency Treebank for Persian 1 1 درخت‌بانک‌ها به‌طورمعمول به دو شکل مبتنی بر ساختار وابستگی و مبتنی بر ساختار سازه‌ای ایجاد می‌شوند. هر دوی این ساختارها در حوزه زبان‌شناسی و پردازش زبان طبیعی کاربرد دارند. هم‌‌اکنون چندین درخت‌بانک وابستگی برای زبان فارسی وجود دارد، اما درخت‌بانک‌ سازه‌‌ای با حجم بزرگ برای این زبان وجود ندارد. در این مقاله قصد داریم روشی را برای تبدیل یک درخت‌بانک وابستگی به معادل سازه‌ای آن، بر اساس یک الگوریتم موجود ارائه دهیم. الگوریتم مبنا با استفاده از مجموعه‌ای از قواعد تبدیل، زیردرخت‌های سازه‌ای متناظر با یال‌های وابستگی را یافته و با ترکیب آنها ساختار سازه‌ای نهایی را تولید می‌کند. ما این الگوریتم را بر روی ساختارهای وابستگی زبان فارسی اعمال و ضمن ارائه نتایج، اصلاحاتی را در جهت بهبود کارایی آن ارائه می‌کنیم. نشان داده می‌شود که پیمایش یال‌های وابستگی در یک جهت خاص بر روی کیفیت الگوریتم تأثیرگذار است. همین‌طور ما اصلاحاتی را در الگورتیم مربوط به تطبیق قواعد و الگوریتم اتصال زیردرخت‌ها ارائه می‌کنیم. این اصلاحات کارایی الگوریتم را به شکل قابل ملاحظه‌ای افزایش می‌دهند. نتایج عملی بهبودی را به اندازه 48/16% نسبت ‌به الگوریتم مبنا نشان می‌دهد.   2 There are two major types of treebanks: dependency-based and constituency-based. Both of them have applications in natural language processing and computational linguistics. Several dependency treebanks have been developed for Persian. However, there is no available big size constituency treebank for this language. In this paper, we aim to propose an algorithm for automatic conversion of a dependency treebank to a constituency treebank for Persian. Our method is based on an existing method. However, we make modification to enhance its accuracy. The base algorithm constructs a constituency structure according to a set of conversion rules. Each rule maps a dependency relation to a constituency subtree. The constituency structure is built by combining these subtrees. We investigate the effects of the order in which dependency relations are processed on the output constituency structure. We show that the best order depends on the charactersitics of the target language. We also make modification in the algorithm for matching the conversion rules. To match a dependency relation to a conversion rule, we start with detailed infromation and if no match was found, we decrease the details and also change the method for matching. We also make modification in the algorithm used for combining the constituency subtrees. We use statistical data derived from a treebank to find a proper position for attaching a constituency subtree to the projection chain of the head. The expremental results show that these modifications provide an improvement of 16.48% in the accuracy of the conversion algorithm.     79 96 2015/10/122015/09/212016/06/52016/03/12015/10/302016/02/21 1394/12/2 2017/10/252017/12/22017/07/82017/05/52017/10/252017/10/25 1396/8/3 احمد پورامینی Ahmad Pouramini داشنگاه صنعتی سیرجان ایران pouramini@gmail.com مسعود قیومی Masood Ghayoomi پژوهشگاه علوم انسانی و مطالعات فرهنگی ایران masood.ghayoomi@gmail.com امینه ناصری Amine Naseri داشنگاه صنعتی سیرجان ایران naseri.amine@sirjantech.ac.ir Natural language processing Treebanks Dependency structure Phrase structure پردازش زبان طبیعی پیکره زبانی درخت‌بانک وابستگی درخت‌بانک سازه‌ای [1] Soltanzadeh F, Bahrani M, Eslami M. "A Rule-Based Approach in Converting a Dependency Parse Tree into Phrase Structure Parse Tree for Persian", JSDP. vol. 12 (4), pp. 95-115, 2016.##[2] Dehghan M H, Faili H. "Generating the Persian Constituency Treebank in an Automatic Convert-ing Method", JSDP, vol. 13 (2), pp.121-137, 2016.##[3] Black, Ezra, et al. "A procedure for quantitatively comparing the syntactic coverage of English grammars." Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991.##[4] Bhatt, Rajesh, and Fei Xia. "Challenges in converting between treebanks: a case study from the hutb." META-RESEARCH Workshop on Advanced Treebanking. 2012.##[5] Collins, Michael, et al. "A statistical parser for Czech." Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. Association for Computational Linguistics, 1999.##[6] Covington, Michael A. "An empirically motivated reinterpretation of Dependency Grammar." arXiv preprint cmp-lg/9404004, 1994.##[7] Ghayoomi, Masood. "Bootstrapping the Develop-ment of an HPSG-based Treebank for Persian." Linguistic Issues in Language Techno-logy vol. 7, no. 1, pp. 1-13. 2012.##[8] Ghayoomi, M., & Kuhn, J. "Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank". In LREC, pp. 802-809, 2014.##[9] Kaplan, Ronald M. "The formal architecture of lexical-functional grammar." Formal issues in lexical-functional grammar, vol. 47, pp. 7-27, 1995.##[10] Klein, A., "From dependency to constituency: Automatic generation of Penn Treebank trees from LFG f-structures", M.S. Thesis , Univer-sity of Heidelberg, Germany, 2009.##[11] Marcus, M. P., Marcinkiewicz, M. A., & Santorini, B. "Building a large annotated corpus of English: The Penn Treebank", Computational linguistics, vol. 19, no. 2, pp 313-330, 1993.##[12] Pollard, Carl, and Ivan A. Sag. Head-driven phrase structure grammar. University of Chi-cago Press, 1994.##[13] Rasooli, M. S., Kouhestani, M., & Moloodi, A. "Development of a Persian syntactic depend-ency treebank". In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 306-314, 2013.##[14] Schabes, Y., Abeille, A., & Joshi, A. K. "Parsing strategies with 'lexicalized' grammars: application to tree adjoining grammars", In Proceedings of the 12th conference on Computational linguistics, Association for Computational Linguistics, vol. 2, pp. 578-583, 1988.##[15] SekineS. & Collins.M. J, The evalb software, 1997. Available: http://cs.nyu.edu/cs/projects/proteus/evalb. [Accessed: 01- Oct- 2017].##[16] Seraji, M., Megyesi, B., & Nivre, J. "Bootstrapping a Persian dependency treebank". Linguistic Issues in Language Technology, vol. 7, no. 18, pp 1-10, 2012.##[17] Steedman, M. The syntactic process, vol. 24. Cambridge: MIT press, 2000.##[18] Wang, Z., & Zong, C. "Phrase structure parsing with dependency structure", In Proceedings of the 23rd International Conference on Computa-tional Linguistics: Posters, Association for Computational Linguistics, pp. 1292-1300, August. 2010.##[19] Xia, F., & Palmer, M. "Converting dependency structures to phrase structures", In Proceedings of the first international conference on Human language technology research, Association for Computational Linguistics, pp. 1-5, March. 2001.##[20] Xia, F., Rambow, O., Bhatt, R., Palmer, M., & Misra Sharma, D. "Towards a multi-representa-tional treebank", LOT Occasional Series, vol. 12., pp. 159-170, 2008.##[1] سلطان زاده ف.، بحرانی م. و اسلامی م. "دادگان درخت نحوی شریف: دادگان درخت نحوی ساخت‌سازهای زبان فارسی" مجموعه مقالات سومین همایش زبانشناسی رایانشی ایران، دانشگاه صنعتی شریف، 28-29 آبان، 1393.##[2] دهقان، م.،فیلی، ه. "تولید درختبانک سازه‌ای زبان فارسی به روش تبدیل خودکار". پردازش علائم و داده‌ها. جلد ۱۳، شماره ۲، صفحه ۱۲۱-۱۳۷، 1395.##[1] Soltanzadeh F, Bahrani M, Eslami M. "A Rule-Based Approach in Converting a Dependency Parse Tree into Phrase Structure Parse Tree for Persian", JSDP. vol. 12 (4), pp. 95-115, 2016.##[2] Dehghan M H, Faili H. "Generating the Persian Constituency Treebank in an Automatic Convert-ing Method", JSDP, vol. 13 (2), pp.121-137, 2016.##[3] Black, Ezra, et al. "A procedure for quantitatively comparing the syntactic coverage of English grammars." Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991.##[4] Bhatt, Rajesh, and Fei Xia. "Challenges in converting between treebanks: a case study from the hutb." META-RESEARCH Workshop on Advanced Treebanking. 2012.##[5] Collins, Michael, et al. "A statistical parser for Czech." Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. Association for Computational Linguistics, 1999.##[6] Covington, Michael A. "An empirically motivated reinterpretation of Dependency Grammar." arXiv preprint cmp-lg/9404004, 1994.##[7] Ghayoomi, Masood. "Bootstrapping the Develop-ment of an HPSG-based Treebank for Persian." Linguistic Issues in Language Techno-logy vol. 7, no. 1, pp. 1-13. 2012.##[8] Ghayoomi, M., & Kuhn, J. "Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank". In LREC, pp. 802-809, 2014.##[9] Kaplan, Ronald M. "The formal architecture of lexical-functional grammar." Formal issues in lexical-functional grammar, vol. 47, pp. 7-27, 1995.##[10] Klein, A., "From dependency to constituency: Automatic generation of Penn Treebank trees from LFG f-structures", M.S. Thesis , Univer-sity of Heidelberg, Germany, 2009.##[11] Marcus, M. P., Marcinkiewicz, M. A., & Santorini, B. "Building a large annotated corpus of English: The Penn Treebank", Computational linguistics, vol. 19, no. 2, pp 313-330, 1993.##[12] Pollard, Carl, and Ivan A. Sag. Head-driven phrase structure grammar. University of Chi-cago Press, 1994.##[13] Rasooli, M. S., Kouhestani, M., & Moloodi, A. "Development of a Persian syntactic depend-ency treebank". In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 306-314, 2013.##[14] Schabes, Y., Abeille, A., & Joshi, A. K. "Parsing strategies with 'lexicalized' grammars: application to tree adjoining grammars", In Proceedings of the 12th conference on Computational linguistics, Association for Computational Linguistics, vol. 2, pp. 578-583, 1988.##[15] SekineS. & Collins.M. J, The evalb software, 1997. Available: http://cs.nyu.edu/cs/projects/proteus/evalb. [Accessed: 01- Oct- 2017].##[16] Seraji, M., Megyesi, B., & Nivre, J. "Bootstrapping a Persian dependency treebank". Linguistic Issues in Language Technology, vol. 7, no. 18, pp 1-10, 2012.##[17] Steedman, M. The syntactic process, vol. 24. Cambridge: MIT press, 2000.##[18] Wang, Z., & Zong, C. "Phrase structure parsing with dependency structure", In Proceedings of the 23rd International Conference on Computa-tional Linguistics: Posters, Association for Computational Linguistics, pp. 1292-1300, August. 2010.##[19] Xia, F., & Palmer, M. "Converting dependency structures to phrase structures", In Proceedings of the first international conference on Human language technology research, Association for Computational Linguistics, pp. 1-5, March. 2001.##[20] Xia, F., Rambow, O., Bhatt, R., Palmer, M., & Misra Sharma, D. "Towards a multi-representa-tional treebank", LOT Occasional Series, vol. 12., pp. 159-170, 2008.## ##

استفاده از برآورد حالت‌های پویای دست مبتنی بر مدل، برای تقلید عملکرد بازوی انسان توسط ربات با داده‌های کینکت Using of Model Based Hand Poses Estimation for Imitation of User's Arm Movements by Robot Arm 1 1 برای اجرای فرآیند ضبط حرکت، لازم است داده­های مناسب، در طول زمان با دنبال‌کردن نقاط کلیدی از هدف مورد نظر استخراج شوند. با این داده­ها و طی یک سری عملیات پس­پردازشی کارهای زیادی از جمله ساخت مجدد آن حرکت در فضای سه­بعدی می‌توان انجام داد. در این مقاله یک الگوی برآورد حالت­های پویای دست مبتنی بر مدل با استفاده از روش ضبط حرکت بدون نشانه­گذاری ارائه می­شود. در این پژوهش حرکات بازوی عامل انسانی در قالب دنباله­ای از تصاویر رنگی به همراه داده­های عمق و اسکلت به‌دست‌آمده از کینکت (ابزاری برای ضبط حرکت بدون نشانه­گذاری) با سرعت سی فریم در ثانیه به‌عنوان داده­های ورودی­ استفاده شده­اند. الگوی پیشنهادی، ویژگی­های زمانی و مکانی از دنباله تصاویر ورودی استخراج می­کند و روی تعیین موقعیت نوک انگشتان شست و اشاره و به‌دست‌آوردن زوایای مفاصل ربات، به‌منظور تقلید حرکت بازوی عامل انسانی در سه ­بعد در یک محیط کنترل‌نشده تمرکز دارد. در این پژوهش از  بازوی ربات واقعی RoboTEK II ST240 استفاده شده و حرکات بازوی عامل انسانی به حرکات تعریف‌شده برای این بازوی ربات محدود شده است. بردارویژگی جهت برآورد حالت به‌ازای هر فریم، به مختصات x، y و عمق برخی مفاصل و مختصات نوک انگشتان شست و اشاره نیازدارد. از داده­های عمق و اسکلت برای تعیین زوایای مفاصل ربات استفاده می­شود؛ ولی تعیین نوک انگشتان به‌طور مستقیم با داده­های موجود امکان­پذیر نیست؛ از این­رو سه رویکرد برای شناسایی نوک انگشتان شست و اشاره با استفاده از داده­های موجود ارائه می­شود. در این رویکردها از مفاهیمی همچون آستانه­گیری، لبه­یابی، ساخت پوسته محدب، مدل‌کردن رنگ پوست و تفریق پس­زمینه استفاده می­شود. در پایان برای تقلید حرکت، با استفاده از بردارهای ویژگی به‌ازای هر فریم، حالت متناظر بر روی بازوی ربات اعمال می­شود. برای ارزیابی تقلید حرکت، مسیرهای طی‌شده توسط قسمت نهایی دست عامل انسانی و قسمت مجری نهایی بازوی ربات با هم مقایسه شده‌اند. نمودارهایی که میزان تغییرات زوایای مفاصل را برای این دو مورد نشان می­دهند، گویای مؤثر بودن الگوی پیشنهادی در تقلید عملکرد بازوی انسانی است.   2 Pose estimation is a process to identify how a human body and/or individual limbs are configured in a given scene. Hand pose estimation is an important research topic which has a variety of applications in human-computer interaction (HCI) scenarios, such as gesture recognition, animation synthesis and robot control. However, capturing the hand motion is quite a challenging task due to its high flexibility. Many sensor-based and vision-based methods have been proposed to fulfill the task. In sensor-based systems, specialized hardware is used for hand motion capture. Generally, vision-based hand pose estimation methods can be divided into two categories: appearance-based methods and model-based methods. In appearance-based approaches, various features are extracted from the input images to estimate the hand pose. Usually a lot of training samples are used to train a mapping function from the features to the hand poses in advance. Given the learned mapping function, the hand pose can be estimated efficiently. In model-based approaches the hand pose is estimated by aligning a projected 3D hand model to the extracted hand features in the inputs. Therefore, the desired information to be provided includes state at any time. These methods require a lot of calculations which are not possible in practice to implement them immediately. Hand pose estimation using (color/depth) images consist of three steps: Hand detection and its separation Feature extraction Setting the parameters of the model using extracted feature and updating the model To extract necessary features for pose estimation, depending on used model and usage of hand gesture analysis, features such as fingertips position, number of fingers, palm position and joint angles are extracted. In this paper a model-based markerless dynamic hand poses estimation scheme is presented.  Motion Capture is the process of recording a live motion event and translating it into usable mathematical terms by tracking a number of key points in space over time and combining them to obtain a single 3D representation of the performance. The sequence of depth images, color images and skeleton data obtained from Kinect (a new tool for markerless motion capture) at 30 frames per second are as inputs of this scheme. The proposed scheme exploits both temporal and spatial features of the input sequences, and focuses on index and thumb fingertips localization and joint angles of the robot arm to mimic the user's arm movements in 3D space in an uncontrolled environment. The RoboTECH II ST240 is used as a real robot arm model. Depth and skeleton data are used to determine the angles of the robot joints. Three approaches to identify the tip of the thumb and index fingers are presented using existing data, each with its own limitations. In these approaches, concepts such as thresholding, edge detection, making convex hull, skin modeling and background subtraction are used. Finally, by comparing tracked trajectories of the user's wrist and robot end effector, the graphs show an error about 0.43 degree in average which is an appropriate performance in this research. The key contribution of this work is hand pose estimation per every input frame and updating arm robot according to estimated pose. Thumb and index fingertips detection as part of feature vector resulted using presented approaches. User movements transmit to the corresponding Move instruction for robot. Necessary features for Move instruction are rotation values around joints in different directions and opening value of index and thumb fingers at each other.   97 116 2015/10/122015/09/212016/06/52016/03/12015/10/302016/02/212016/05/22 1395/3/2 2017/10/252017/12/22017/07/82017/05/52017/10/252017/10/252017/03/5 1395/12/15 مریم زارع مهرجردی Maryam Zare mehrjardi دانشگاه یزد ایران zaremaryam@stu.yazd.ac.ir مهدی رضائیان Mehdi Rezaeian دانشگاه یزد ایران mrezaeian@yazd.ac.ir pose estimation depth data markerless motion capture Kinect 3d model برآورد حالت داده عمق ضبط حرکت بدون نشانه‌گذاری کینکت مدل سه‌بعدی [1] T. B. Moeslund and E. Granum, "A survey of computer vision-based human motion capture", Computer vision and image understanding, vol. 81, no. 3, pp. 231–268, 2001.##[2] H. Liang, J. Yuan, D. Thalmann, and Z. Zhang, "Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization", vol. 29, no. 6–8, pp. 837–848, 2013.##[3] K. Fujimura and X. Liu, "Sign recognition using depth image streams", 7th International Conference on Automatic Face and Gesture Recognition, pp. 381–386, 2006.##[4] J. MacCormick and A. Blake, "A probabilistic exclusion principle for tracking multiple objects", International Journal of Computer Vision, vol. 39, no. 1, pp. 57–71, 2000.##[5] M. Moghaddam, M. Nahvi, and R. H. Pak, "Static Persian Sign Language Recognition Using Kernel-Based Feature Extraction", 7th Iranian Machine Vision and Image Processing (MVIP), pp. 1–5, 2011.##[6] P. Breuer, C. Eckes, and S. Müller, "Hand gesture recognition with a novel IR time-of-flight range camera--a pilot study", in Comput-er Vision/Computer Graphics Collaboration Techniques, Springer, pp. 247–260, 2007.##[7] C. Plagemann, V. Ganapathi, D. Koller, and S. Thrun, "Real-time identification and localiza-tion of body parts from depth images", IEEE International Conference on Robotics and Automation (ICRA), pp. 3108–3113, 2010.##[8] A. Baak, M. Müller, G. Bharaj, H.-P. Seidel, and C. Theobalt, "A data-driven approach for real-time full body pose reconstruction from a depth camera", Consumer Depth Cameras for Computer Vision. Springer London, pp. 71–98, 2013.##[9] L. A. Schwarz, A. Mkhitaryan, D. Mateus, and N. Navab, "Estimating human 3d pose from time-of-flight images based on geodesic distances and optical flow", IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 700–706, 2011.##[10] A. Kuznetsova and B. Rosenhahn, "Hand pose estimation from a single rgb-d image", Advances in Visual Computing. Springer Berlin Heidelberg, pp. 592–602, 2013.##[11] J. L. Raheja, A. Chaudhary, and K. Singal, "Tracking of fingertips and centers of palm using kinect", 3th international conference on Computational intelligence, modelling and simulation, pp. 248–252, 2011.##[12] Y. Li, "Hand gesture recognition using Kinect", IEEE 3rd International Conference on Software Engineering and Service Science, pp. 196–199, 2012.##[13] Z. Li and R. Jarvis, "Real time hand gesture recognition using a range camera", Australa-sian Conference on Robotics and Automation, pp. 21–27, 2009.##[14] Q. K. Le, C. H. Pham, and T. H. Le, "Road Traffic Control Gesture Recognition using Depth Images", journal of IEEK Transactions on Smart Processing and Computing, vol. 1, no. 1, pp. 1–7, 2012.##[15] L. Cheng, Q. Sun, H. Su, Y. Cong, and S. Zhao, "Design and implementation of human-robot interactive demonstration system based on Kinect", 24th Chinese Control and Decision Conference, pp. 971–975,2012.##[16] K. Qian, J. Niu, and H. Yang, "Developing a Gesture Based Remote Human-Robot Interac-tion System Using Kinect", International Journal of Smart Home, vol. 7, no. 4, 2013.##[17] D. Xu, X. Wu, Y.-L. Chen, and Y. Xu, "Online Dynamic Gesture Recognition for Human Robot Interaction", Journal of Intelligent & Robotic Systems, pp. 1–14, 2014.##[18] A. Billard and M.J. Matarić, "Learning human arm movements by imitation:: Evaluation of a biologically inspired connectionist architect-ture", Robotics and Autonomous Syst-ems, vol. 37, no. 2, pp.145-160, 2001.##[19] S. Filiatrault and A.M. Cretu. "Human arm motion imitation by a humanoid robot", IEEE International Symposium on Robotic and Sensors Environments, pp.31-36,2014.##[20] J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, "Real-time human pose recognition in parts from single depth images", Communica-tions of the ACM, vol. 56, no. 1, pp. 116–124, 2013.##[21] P. Kakumanu, S. Makrogiannis, and N. Bourbakis, "A survey of skin-color modeling and detection methods", Pattern Recognition, vol. 40, no. 3, pp. 1106–1122, 2007.##[22] J. Yang, W. Lu, and A. Waibel, "Skin-color modeling and adaptation, " Springer, 1997.##[23] V. Vezhnevets, V. Sazonov, and A. Andreeva, "A survey on pixel-based skin color detection techniques", Proc. Graphicon, vol. 3, pp. 85–92, 2003.##[24] R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696–706, 2002.##[25] D. Chai and A. Bouzerdoum, "A Bayesian approach to skin color classification in YCbCr color space", TENCON Proceedings, vol. 2, pp. 421–424, 2000.##[26] D. Chai and K. N. Ngan, "Locating facial region of a head-and-shoulders color image", Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 124–129, 1998.##[27] K.-W. Wong, K.-M. Lam, and W.-C. Siu, "A robust scheme for live detection of human faces in color images", Signal Processing: Image Communication, vol. 18, no. 2, pp. 103–114, 2003.##[28] J. J. De Dios and N. Garcia, "Face detection based on a new color space YCgCr", Interna-tional Conference on Image Processing, vol. 3, pp. 906–909, 2003.##[29] H.-S. Yeo, B.-G. Lee, and H. Lim, "Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware", Multimedia Tools and Applica-tions, vol. 74, no. 8, pp. 2687–2715, 2013.##[1] T. B. Moeslund and E. Granum, "A survey of computer vision-based human motion capture", Computer vision and image understanding, vol. 81, no. 3, pp. 231–268, 2001.##[2] H. Liang, J. Yuan, D. Thalmann, and Z. Zhang, "Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization", vol. 29, no. 6–8, pp. 837–848, 2013.##[3] K. Fujimura and X. Liu, "Sign recognition using depth image streams", 7th International Conference on Automatic Face and Gesture Recognition, pp. 381–386, 2006.##[4] J. MacCormick and A. Blake, "A probabilistic exclusion principle for tracking multiple objects", International Journal of Computer Vision, vol. 39, no. 1, pp. 57–71, 2000.##[5] M. Moghaddam, M. Nahvi, and R. H. Pak, "Static Persian Sign Language Recognition Using Kernel-Based Feature Extraction", 7th Iranian Machine Vision and Image Processing (MVIP), pp. 1–5, 2011.##[6] P. Breuer, C. Eckes, and S. Müller, "Hand gesture recognition with a novel IR time-of-flight range camera--a pilot study", in Comput-er Vision/Computer Graphics Collaboration Techniques, Springer, pp. 247–260, 2007.##[7] C. Plagemann, V. Ganapathi, D. Koller, and S. Thrun, "Real-time identification and localiza-tion of body parts from depth images", IEEE International Conference on Robotics and Automation (ICRA), pp. 3108–3113, 2010.##[8] A. Baak, M. Müller, G. Bharaj, H.-P. Seidel, and C. Theobalt, "A data-driven approach for real-time full body pose reconstruction from a depth camera", Consumer Depth Cameras for Computer Vision. Springer London, pp. 71–98, 2013.##[9] L. A. Schwarz, A. Mkhitaryan, D. Mateus, and N. Navab, "Estimating human 3d pose from time-of-flight images based on geodesic distances and optical flow", IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 700–706, 2011.##[10] A. Kuznetsova and B. Rosenhahn, "Hand pose estimation from a single rgb-d image", Advances in Visual Computing. Springer Berlin Heidelberg, pp. 592–602, 2013.##[11] J. L. Raheja, A. Chaudhary, and K. Singal, "Tracking of fingertips and centers of palm using kinect", 3th international conference on Computational intelligence, modelling and simulation, pp. 248–252, 2011.##[12] Y. Li, "Hand gesture recognition using Kinect", IEEE 3rd International Conference on Software Engineering and Service Science, pp. 196–199, 2012.##[13] Z. Li and R. Jarvis, "Real time hand gesture recognition using a range camera", Australa-sian Conference on Robotics and Automation, pp. 21–27, 2009.##[14] Q. K. Le, C. H. Pham, and T. H. Le, "Road Traffic Control Gesture Recognition using Depth Images", journal of IEEK Transactions on Smart Processing and Computing, vol. 1, no. 1, pp. 1–7, 2012.##[15] L. Cheng, Q. Sun, H. Su, Y. Cong, and S. Zhao, "Design and implementation of human-robot interactive demonstration system based on Kinect", 24th Chinese Control and Decision Conference, pp. 971–975,2012.##[16] K. Qian, J. Niu, and H. Yang, "Developing a Gesture Based Remote Human-Robot Interac-tion System Using Kinect", International Journal of Smart Home, vol. 7, no. 4, 2013.##[17] D. Xu, X. Wu, Y.-L. Chen, and Y. Xu, "Online Dynamic Gesture Recognition for Human Robot Interaction", Journal of Intelligent & Robotic Systems, pp. 1–14, 2014.##[18] A. Billard and M.J. Matarić, "Learning human arm movements by imitation:: Evaluation of a biologically inspired connectionist architect-ture", Robotics and Autonomous Syst-ems, vol. 37, no. 2, pp.145-160, 2001.##[19] S. Filiatrault and A.M. Cretu. 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بهبود صحت ابهام‌زدایی نام نویسنده با استفاده از خوشه‌بندی تجمّعی Improving the accuracy of the author name disambiguation by using clustering ensemble 1 1 امروزه کتابخانه‌های دیجیتال از مهم­ترین و سریع­ترین منابع پژوهشی در جهان محسوب می­شوند. از نقطه‌نظر مدیریت تجمیع دانش، توانایی جستجوی صحیح، دقیق و سریع مطالب علمی مد نظر کاربر، اهمیت زیادی دارد. پیچیدگی و وجود تشابه در بانک­های اطلاعاتی موجب می­گردد این منابع در هنگام بهره­برداری با چالش­ها و ابهامات زیادی مواجه شوند و همین چالش­ها دست‌مایه پژوهش‌های گسترده‌ای را در این حوزه شکل داده است. یکی از مهم­ترین این چالش­ها، وجود ابهام در نام نویسنده است. در این خصوص روش‌های بسیاری با بهره‌گیری از روش‌های خوشه­بندی نسبت به حل نام­های مبهم مبادرت ورزیده­اند. این روش‌ها تا حدودی توانسته‌اند مشکل را برطرف کنند، اما همچنان مسئله تکه‌تکه‌بودن خوشه‌ها و خطا در نتایج تولیدی، از معایب روش‌های موجود است. از سویی تجربه نشان داده که یک روش به‌تنهایی نتایجی با صحت بالا نمی‌تواند تولید کند. بدین منظور در این مقاله مدلی جهت حل مشکل ذکر‌شده ارائه شده است‌. راهکار پیشنهادی در دو گام، عملیات ابهام‌زدایی را انجام می­دهد. در گام نخست خوشه­های اولیه با استفاده از "الگوریتم خوشه‌بندی سلسله‌مراتبی تجمعی با پارامترها و توابع اندازه‌گیری مشابهت مختلف"، تولید می‌شوند. در گام دوم با بهره­گیری از "الگوریتم خوشه‌بندی تجمعی"، خوشه­های تولید‌شده به‌گونه­ای ترکیب می­شوند تا خوشه‌هایی غنی با درصد کمتری از تکه‌تکه‌بودن و صحت بالاتر تولید شوند. در ارزیابی‌ الگوریتم پیشنهادی از "مجموعه دادگان DBLP، تحت معیار K" استفاده شده است. نتایج،  بهبود قابل توجهی را در ترکیب خوشه‌های مذکور نشان می­دهند.   2 Today, digital libraries are important academic resources including millions of citations and bibliographic essential information such as titles, author's names and location of publications. From the view of knowledge accumulation management, the ability to search fast, accurate, desired contents, has a great importance. The complexity and similarity in these resources cause many challenges and ambiguities. One of the most of these challenges is the author name disambiguation which makes an extensive scope of research. Although many effective methods have been developed by using clustering techniques in disambiguation of the author's name, the accuracy of these methods is not acceptable and still there are some problems such as fragmentation and error in the produced results of these methods, since there is no uniform standard of citations, various combinations, and numerous, written, verbal patterns. In fact, experiences have shown that the use of a single method to disambiguate names does not provide results with a high accuracy despite concerns expressed above. In this paper, a new method is proposed to disambiguate author names in different formats and combinations with more accuracy. The proposed solution carries out the disambiguation in two steps; In the first step, agglomerative hierarchical clustering algorithm produces clusters using similar functions and different thresholds. In the second step, clusters produced by clustering ensemble technique in the previous stage are combined to provide more accurate clusters with less fragmentation. The proposed method is experimentally evaluated by conducted DBLP datasets with K criterion. The evaluation results show that the proposed method enhances the accuracy of disambiguation of author names in different formats.   117 128 2015/10/122015/09/212016/06/52016/03/12015/10/302016/02/212016/05/222016/05/23 1395/3/3 2017/10/252017/12/22017/07/82017/05/52017/10/252017/10/252017/03/52017/12/31 1396/10/10 سید محمد مرتضوی Sayed Mohammad Mortazavi دانشگاه آزاد اسلامی واحد نجف آباد ایران mortazavi.s.m@outlook.com محمد حسین ندیمی شهرکی Mohammad Hossein Nadimi Shahraki دانشگاه آزاد اسلامی واحد نجف آباد ایران nadimi@iaun.ac.ir مصطفی موسی خانی Mostafa Mosakhani دانشگاه آزاد اسلامی واحد نجف آباد ایران m_student1367@yahoo.com Digital libraries Author Name Disambiguation Ambiguous name Clustering Ensemble کتابخانه‌های دیجیتال ابهام‌زدایی نام نویسنده نام مبهم خوشه‌بندی تجمعی [1] R. G. Cota, A. A. Ferreira, C. Nascimento, M. A. Gonçalves, and A. H. Laender, "An unsupervised heuristic‐based hierarchical method for name disambiguation in bibliographic citations," Journal of the American Society for Information Science and Technology, vol. 61, pp. 1853-1870, 2010.##[2] B.-W. On and D. Lee, "Scalable Name Disambiguation using Multi-level Graph Part-ition," in SDM, 2007.##[3] X. Fan, J. Wang, X. Pu, L. Zhou, and B. Lv, "On graph-based name disambiguation," Journal of Data and Information Quality (JDIQ), vol. 2, p. 10, 2011.##[4] Z. Chen, D. V. Kalashnikov, and S. Mehrotra, "Adaptive graphical approach to entity resolu-tion," in Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, 2007, pp. 204-213.##[5] L. D. u Thu, "Named Entity Disambiguation in Digital Libraries," 2010.##[6] B.-W. On, E. Elmacioglu, D. Lee, J. Kang, and J. Pei, "Improving grouped-entity resolution using quasi-cliques," in Data Mining, 2006. ICDM'06. Sixth International Conference on, 2006, pp. 1008-1015.##[7] I.-S. Kang, S.-H. Na, S. Lee, H. Jung, P. Kim, W.-K. Sung, et al., "On co-authorship for author disambiguation," Information Processing & Management, vol. 45, pp. 84-97, 2009.##[8] H. Han, L. Giles, H. Zha, C. Li, and K. Tsioutsiouliklis, "Two supervised learning approaches for name disambiguation in author citations," in Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on, 2004, pp. 296-305.##[9] H. Han, H. Zha, and C. L. Giles, "Name disambiguation in author citations using a k-way spectral clustering method," in Digital Libraries, 2005. JCDL'05. Proceedings of the 5th ACM/IEEE-CS Joint Conference on, 2005, pp. 334-343.##[10] J. Huang, S. Ertekin, and C. L. Giles, "Efficient name disambiguation for large-scale databases," in Knowledge Discovery in Databases: PKDD 2006, ed: Springer, 2006, pp. 536-544.##[11] B. Zhang and M. A. Hasan, "Name Entity Disambiguation in Anonymized Graphs using Link Analysis: A Network Embedding based Solution," arXiv preprint arXiv:1702.02287, 2017.##[12] S. Ressler, "Social network analysis as an approach to combat terrorism: Past, present, and future research," Homeland Security Affairs, vol. 2, pp. 1-10, 2006.##[13] F. H. Levin and C. A. Heuser, "Using Genetic Programming to Evaluate the Impact of Social Network Analysis in Author Name Disambigu-ation," in AMW, 2010.##[14] D. Shin, T. Kim, H. Jung, and J. Choi, "Automatic method for author name disambigu-ation using social networks," in Advanced Information Networking and Applica-tions (AINA), 2010 24th IEEE Intern-ational Confe-rence on, 2010, pp. 1263-1270.##[15] Y. Ju, B. Adams, K. Janowicz, Y. Hu, B. Yan, and G. McKenzie, "Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occur-rence with Topic Modeling," in Knowledge Engineering and Knowledge Management: 20th International Conference, EKAW 2016, Bolo-gna, Italy, November 19-23, 2016, Procee-dings 20, 2016, pp. 353-367.##[16] I. B. L. Getoor, "A Latent Dirichlet Model for Unsupervised Entity Resolution," in Procee-dings of the Sixth SIAM International Confe-rence on Data Mining, 2006, p. 47.##[17] I. Bhattacharya and L. Getoor, "Collective entity resolution in relational data," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, p. 5, 2007.##[18] Y. Song, J. Huang, I. G. Councill, J. Li, and C. L. Giles, "Generative models for name disambiguation," in Proceedings of the 16th international conference on World Wide Web, 2007, pp. 1163-1164.##[19] D. A. Pereira, B. Ribeiro-Neto, N. Ziviani, A. H. Laender, M. A. Gonçalves, and A. A. Ferreira, "Using web information for author name disambiguation," in Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, 2009, pp. 49-58.##[20] K.-H. Yang, H.-T. Peng, J.-Y. Jiang, H.-M. Lee, and J.-M. Ho, "Author name disambiguation for citations using topic and web correlation," in Research and Advanced Technology for Digital Libraries, ed: Springer, 2008, pp. 185-196.##[21] V. I. Torvik and N. R. Smalheiser, "Author name disambiguation in MEDLINE," ACM Transa-ctions on Knowledge Discovery from Data (TKDD), vol. 3, p. 11, 2009.##[22] A. A. Ferreira, A. Veloso, M. A. Gonçalves, and A. H. Laender, "Effective self-training author name disambiguation in scholarly digital libra-ries," in Proceedings of the 10th annual joint conference on Digital libraries, 2010, pp. 39-48.##[23] W. W. Cohen, H. Kautz, and D. McAllester, "Hardening soft information sources," in Proceedings of the sixth ACM SIGKDD interna-tional conference on Knowledge discovery and data mining, 2000, pp. 255-259.##[24] F. H. Levin and C. A. Heuser, "Evaluating the use of social networks in author name disambiguation in digital libraries," Journal of Information and Data Management, vol. 1, p. 183, 2010.##[25] M. H. Nadimi and M. Mosakhani, "A more Accurate Clustering Method by using Co-author Social Networks for Author Name Disambigu-ation," Journal of Computing and Security, vol. 1, 2015.##[1] R. G. Cota, A. A. Ferreira, C. Nascimento, M. A. Gonçalves, and A. H. Laender, "An unsupervised heuristic‐based hierarchical method for name disambiguation in bibliographic citations," Journal of the American Society for Information Science and Technology, vol. 61, pp. 1853-1870, 2010.##[2] B.-W. On and D. Lee, "Scalable Name Disambiguation using Multi-level Graph Part-ition," in SDM, 2007.##[3] X. Fan, J. Wang, X. Pu, L. Zhou, and B. Lv, "On graph-based name disambiguation," Journal of Data and Information Quality (JDIQ), vol. 2, p. 10, 2011.##[4] Z. Chen, D. V. Kalashnikov, and S. Mehrotra, "Adaptive graphical approach to entity resolu-tion," in Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, 2007, pp. 204-213.##[5] L. D. u Thu, "Named Entity Disambiguation in Digital Libraries," 2010.##[6] B.-W. On, E. Elmacioglu, D. Lee, J. Kang, and J. Pei, "Improving grouped-entity resolution using quasi-cliques," in Data Mining, 2006. ICDM'06. Sixth International Conference on, 2006, pp. 1008-1015.##[7] I.-S. Kang, S.-H. Na, S. Lee, H. Jung, P. Kim, W.-K. Sung, et al., "On co-authorship for author disambiguation," Information Processing & Management, vol. 45, pp. 84-97, 2009.##[8] H. Han, L. Giles, H. Zha, C. Li, and K. Tsioutsiouliklis, "Two supervised learning approaches for name disambiguation in author citations," in Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on, 2004, pp. 296-305.##[9] H. Han, H. Zha, and C. L. Giles, "Name disambiguation in author citations using a k-way spectral clustering method," in Digital Libraries, 2005. JCDL'05. Proceedings of the 5th ACM/IEEE-CS Joint Conference on, 2005, pp. 334-343.##[10] J. Huang, S. Ertekin, and C. L. Giles, "Efficient name disambiguation for large-scale databases," in Knowledge Discovery in Databases: PKDD 2006, ed: Springer, 2006, pp. 536-544.##[11] B. Zhang and M. A. Hasan, "Name Entity Disambiguation in Anonymized Graphs using Link Analysis: A Network Embedding based Solution," arXiv preprint arXiv:1702.02287, 2017.##[12] S. Ressler, "Social network analysis as an approach to combat terrorism: Past, present, and future research," Homeland Security Affairs, vol. 2, pp. 1-10, 2006.##[13] F. H. Levin and C. A. Heuser, "Using Genetic Programming to Evaluate the Impact of Social Network Analysis in Author Name Disambigu-ation," in AMW, 2010.##[14] D. Shin, T. Kim, H. Jung, and J. Choi, "Automatic method for author name disambigu-ation using social networks," in Advanced Information Networking and Applica-tions (AINA), 2010 24th IEEE Intern-ational Confe-rence on, 2010, pp. 1263-1270.##[15] Y. Ju, B. Adams, K. Janowicz, Y. Hu, B. Yan, and G. McKenzie, "Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occur-rence with Topic Modeling," in Knowledge Engineering and Knowledge Management: 20th International Conference, EKAW 2016, Bolo-gna, Italy, November 19-23, 2016, Procee-dings 20, 2016, pp. 353-367.##[16] I. B. L. Getoor, "A Latent Dirichlet Model for Unsupervised Entity Resolution," in Procee-dings of the Sixth SIAM International Confe-rence on Data Mining, 2006, p. 47.##[17] I. Bhattacharya and L. Getoor, "Collective entity resolution in relational data," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, p. 5, 2007.##[18] Y. Song, J. Huang, I. G. Councill, J. Li, and C. L. Giles, "Generative models for name disambiguation," in Proceedings of the 16th international conference on World Wide Web, 2007, pp. 1163-1164.##[19] D. A. Pereira, B. Ribeiro-Neto, N. Ziviani, A. H. Laender, M. A. Gonçalves, and A. A. Ferreira, "Using web information for author name disambiguation," in Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, 2009, pp. 49-58.##[20] K.-H. Yang, H.-T. Peng, J.-Y. Jiang, H.-M. Lee, and J.-M. Ho, "Author name disambiguation for citations using topic and web correlation," in Research and Advanced Technology for Digital Libraries, ed: Springer, 2008, pp. 185-196.##[21] V. I. Torvik and N. R. Smalheiser, "Author name disambiguation in MEDLINE," ACM Transa-ctions on Knowledge Discovery from Data (TKDD), vol. 3, p. 11, 2009.##[22] A. A. Ferreira, A. Veloso, M. A. Gonçalves, and A. H. Laender, "Effective self-training author name disambiguation in scholarly digital libra-ries," in Proceedings of the 10th annual joint conference on Digital libraries, 2010, pp. 39-48.##[23] W. W. Cohen, H. Kautz, and D. McAllester, "Hardening soft information sources," in Proceedings of the sixth ACM SIGKDD interna-tional conference on Knowledge discovery and data mining, 2000, pp. 255-259.##[24] F. H. Levin and C. A. Heuser, "Evaluating the use of social networks in author name disambiguation in digital libraries," Journal of Information and Data Management, vol. 1, p. 183, 2010.##[25] M. H. Nadimi and M. Mosakhani, "A more Accurate Clustering Method by using Co-author Social Networks for Author Name Disambigu-ation," Journal of Computing and Security, vol. 1, 2015.## ##

تشخیص پیوسته میزان استرس در طول رانندگی با استفاده از روش خوشه‌بندی Fuzzy c-means A Fuzzy C-means Clustering Approach for Continuous Stress Detection during Driving 1 1 استرس یکی از مهم‌ترین علل بروز اختلالات جسمی و روانی است که منشأ بسیاری از بیماری‌ها به‌شمار می‌رود. در دو دهه اخیر تشخیص سطح استرس در حین رانندگی به‌منظور پیش‌گیری از سوانح مورد اقبال جدی پژوهش‌گران بوده است. نکته‌ای که در پژوهش‌های پیشین در خصوص اندازه‌گیری استرس در طول رانندگی مورد توجه قرار نگرفته، این است که سطح استرس در حین رانندگی با توجه به وقایع غیر قابل پیش‌بینی تغییر می‌کند. در این مقاله نشان داده شده است که سطح استرس را در یک دوره زمانی طولانی، به طور مثال مدت زمان رانندگی در بزرگراه، ثابت نمی‌توان در نظر گرفت؛ سپس براساس این فرض، ایده جدیدی برای تشخیص سطح استرس بر مبنای روش خوشه‌بندی fuzzy c-means و برچسب‌زدن به خوشه‌ها توسط خبره پیشنهاد شده است. در الگوریتم پیشنهادی، با تلفیق میزان تعلق خوشه و ضریب وزنی متناظر با برچسب هر خوشه، معیاری کمی از استرس برای فواصل زمانی بسیار کوتاه حاصل می‌شود. در‌واقع دادگان استرس در رانندگی برچسب هایی نادقیقی دارند که روش پیشنهادی با استفاده از دانش نهادینه‌شده در دادگان و به شیوه‌ای قاعده‌مند، استرس را به‌صورت پیوسته تخمین می‌زند. در این مقاله، علاوه‌بر ارزیابی کیفی نتایج بر اساس شواهد حین آزمایش، از معیار کمی همبستگی بین معیار استرس حاصل از روش پیشنهادی با رتبه‌بندی عینی شرکت‌کنندگان استفاده شده است. ارزیابی‌های کیفی و کمی نشانگر کارآیی روش پیشنهادی در افزایش دقت و صحت تشخیص میزان استرس است.   2 Stress is one of the main causes of physical and mental disorders leading to various types of diseases. In recent two decades, stress level detection during driving to avoid accidents has attracted much of researchers’ attentions. However, the existing studies usually neglect this fact that stress level during driving varies due to irregular events. Contrary to the previous works, this paper demonstrates that to assume a fixed level of stress for a long period- e.g. while driving in highway- is unreasonable. According to the above assumption, a novel approach for continuous stress detection is proposed based on fuzzy c-means clustering and cluster labeling by the expert. Fuzzy c-means clustering is used to specify levels of stress instead of the former different classification and labeling methods. Concurrently, utilizing background knowledge of data and clustering results, the label of each cluster is obtained. Then, proper weights are assigned to labeled clusters.  By combining the membership values of clusters and weights associated with each cluster’s label, a score of stress is obtained in short time intervals. Stress in driving dataset provide stressful conditions during real driving. The experiments were performed on a specific route of open roads and where drivers traverse were limited to daily commutes. For each drive, Electrocardiogram (ECG), Electromyogram (EMG), foot and hand Galvanic skin response (GSR), respiration and marker signals were acquired from the sensors worn by the driver. Clearly, the more number of physiological signals are used, the more computational cost must be paid, so in this work, heart rate, EMG, foot GSR and hand GSR from mentioned dataset are selected. After that, six features consisting of the mean value of the heart rate, the mean value of EMG, the mean value of the hand GSR and the mean value of foot GSR in addition to mean absolute differences for hand and foot GSR are extracted for each 10 second window (100 second window with 90% overlap) of signals. Next step is to cluster via fuzzy c-means algorithm. In this study, the data is located in 5 clusters and according to the membership degree of each window, input signals and background data from dataset, an adequate label is assigned by the expert to each cluster. The labels of these five clusters are "very low", "low", "medium", “high" and "very high" stress, which are respectively the least stressed to the most stressful. Therefore, the base weight vector is obtained as . The weights assigned to the clusters will be a permutation of the mentioned base weight vector. After assigning the weight of clusters, in each window, the membership degree obtained by the Fuzzy c-means method is multiplied by the weight assigned to that cluster and the resulting numbers are accumulated for the 5 clusters. The calculated value scales to the range of 0 to 100, in order to quantifying the stress. For better representation, a collection of 100 different colors in the range of dark blue to dark red of the visible spectra will be defined by the use of “colormap” command in MATLAB. By taking the calculated value to the range of 0 to 100, one of the mentioned colors will be chosen. So the color will be associated to the stress value of the corresponding window. In this paper, in addition to the qualitative assessment of the results, the correlation between the determined stress and subjective rating scores is considered as a quantitative criterion. The results illustrate the effectiveness of the proposed method to improve both the precision and accuracy of stress detection. In fact, the stress in driving dataset have imprecise labels which the proposed systematic approach estimates the stress continuously utilizing the background knowledge of data. The results clearly represent valid, efficient criteria for stress during driving in each moment without using long time window, show the continues stress from the beginning of the experiment until the end of it, and exaggerate individual differs and unexpected hazards during the experiment.   129 142 2015/10/122015/09/212016/06/52016/03/12015/10/302016/02/212016/05/222016/05/232015/06/18 1394/3/28 2017/10/252017/12/22017/07/82017/05/52017/10/252017/10/252017/03/52017/12/312017/10/25 1396/8/3 سارا پورمحمدی Sara Pourmohammadi دانشگاه سمنان ایران pourmohammadi@semnan.ac.ir علی مالکی Ali Maleki دانشگاه سمنان ایران amaleki@semnan.ac.ir Continuous stress detection Stress during driving Fuzzy c-means clustering Fuzzy c-meansتشخیص پیوسته میزان استرس استرس در رانندگی خوشه‌بندی [1] M. A. Younesi Heravi, M. A. Khalilzadeh, R. Sarafan, and M. Azarnoosh, "Lie detector system based on PhotoPlethysmoGraph (PPG) and Galvanic Skin Response (GSR) signals by means of neural network," Signal and Data processing, vol. 9, no. 2, pp. 49-60, 2013.##[2] C. S. Segerstrom and E. G. Miller, "Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry," Psychological Bulletin, vol. 130, no. 4, pp.601-630, 2004.##[3] J. Healey and R. W. Picard, "Detecting stress during real-world driving tasks using physiological sensors," IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 2, pp.156–166, 2005.##[4] C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I. Fotiadis, "Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach," IEEE Transaction on systems, man, and cybernetics-part a: systems and humans, vol. 38, no. 3, pp. 502-512, 2008.##[5] J. Zhai and A. Barreto, "Stress detection in computer users through non-invasive monitoring of physiological signals," Biomedical Science Instrumentation, vol. 42, pp.495–500, 2006.##[6] S. de Sierra, C. S. Avila, G. Bailador, and J.G. Casanova, "A stress detection system based on physiological signals and fuzzy logic," IEEE Transactions on Industrial Electronics, vol. 58, no. 10, pp. 4857-4865, 2011.##[7] N. Sharma and T. Gedeon, "Objective measures, sensors and computational techniques for stress recognition and classification: A survey," computer methods and programs in biomedicine, vol. 108, pp. 1287–1301, 2012.##[8] J.J.G. de Vries, S. C. Pauws, and M. Biehl, "Insightful stress detection from physiology modalities using Learning Vector Quantization," Neurocomputing, vol. 151, pp. 873–882, 2015.##[9] B. Park, E. Jang, M. Chung, and S. Kim, "Design of Prototype-Based Emotion Recognizer Using Physiological Signals," ETRI Journal, vol. 35, no. 5, pp. 869-879, 2013.##[10] S.A. Hosseini and M.A. Khalilzadeh, "Emotional stressrecognition system using EEG and psychophysiological signals: using new labelling process of EEG signals in emotional stress state," International Conference of Biomedical Engineering and Computer Science (ICBECS), pp.1–6, 2010.##[11] T. Lin, M. Omata, W. Hu, and A. Imamiya, "Do physiological data relate to traditional usability indexes?" Proceeding of the 17th Australia Conference on Computer–Human Interaction: Citizens Online: Considerations for Today and the Future, pp.1–10, 2005.##[12] M. Kumar, M. Weippert, R. Vilbrandt, S. Kreuzfeld, and R. Stoll, "Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment," IEEE Transactions on fuzzy systems, vol. 15, no. 5, pp. 791-808, 2007.##[13] J. Wang, Ch. Lin, and Y. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp. 136–143, 2013.##[14] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Troster, and U. Ehlert, " Discriminating Stress From Cognitive Load Using a Wearable EDA Device," IEEE Transactions on information technology in biomedicine, vol. 14, no. 2, pp. 410-417, 2010.##[15] Z. Dharmawan, "Analysis of Computer Games Player Stress Level Using EEG Data," M.S. thesis, Dep. Elect. Eng., Delft Univ., Netherlands, 2007.##[16] M. Kumar, S. Neubert, S. Behrendt, A. Rieger, M. Weippert, and N. Stoll, "Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals," IEEE Transactions on fuzzy systems, vol. 20, no. 4, pp. 746-759, 2012.##[17] M. Jiang and Z. Wang, "A method for stress detection based on FCM algorithm," 2nd International Congress on Image and Signal Processing, CISP. , pp. 1 – 5, 2009.##[18] J. Healey, "Wearable and automotive systems for affect recognition from physiology," PhD thesis Dep. Elect. Eng. and comp. science, MIT Univ., 2000.##[19] PHYSIONET, Stress Recognition in Automobile Drivers (drivedb), http://physionet.org/##[20] K. Plarre, A. Raij, S.M. Hossain, A. Ahsan Ali, M. Nakajimaz, M. al'Absiz, E. Ertin, T. Kamarck, S. Kumar, M. Scott, D. Siewioreky, A. Smailagicy, E. Wittmers, and z. Jr, "Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment," 10th International Conference on Information Processing in Sensor Networks (IPSN), pp.12-14, 2011.##[21] Y. Deng, Z. Wu, Ch. Chu, and T. Yang, "Evaluating Feature Selection for Stress Identification," 13th International Conference on Information Reuse and Integration (IRI), pp.584-591, 2012.##[22] L. H. Miller and B. M. Shmavonian, "Replicability of two GSR indices as a function of stress and cognitive activity," Journal of Personality and Social Psychology, pp.753–756, 1965.##[23] B.S. McEwen and R.M. Sapolsky, "Stress and cognitive function," Journal of Current Opinion in Neurobiology, vol. 5, pp. 205–216, 1995.##[24] U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, J. S. Suri, "Heart rate variability: a review," Medical and biological engineering and computing, vol. 44, no. 12, pp. 1031-51, 2006.##[25] D. Giakoumis, D. Tzovarasa, and G. Hassapis, "Subject-dependent biosignal features for increased accuracy in psychological stress detection," International Journal of Human-Computer Studies, vol. 71, pp. 425–439, 2013.##[26] M. Li, S. Yi-chun, L. Yin, Y. Hong, and X. Wei, "Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm," Journal of Shanghai Jiaotong Univ. (Sci.), vol. 20, no.1, pp. 51-55, 2015.##[27] M. Singh and A. Queyam, "Stress Detection in Automobile Drivers using Physiological Parameters: A Review," International Journal of Electronics Engineering, vol. 5, no. 2, pp. 1-5, 2013.##[28] A. Akbas, "Evaluation of the Physiological Data Indicating the Dynamic Stress Level of Drivers," Scientific Research and Essays, vol.6, no.2, pp.430-439, 2006.##[1] یونسی هروی محمد امین، خلیل زاده محمد علی، صرافان رسول و آذرنوش مهدی، "تشخیص دروغ بر مبنای سیگنال‎های فوتوپلتیسموگراف و مقاومت الکتریکی پوست با استفاده از شبکه‎ی عصبی"، پردازش علائم و داده‌ها، شماره ۹ (پیاپی ۲) ، صفحات ۴۹-۶۰، 1391.##[1] M. A. Younesi Heravi, M. A. Khalilzadeh, R. Sarafan, and M. Azarnoosh, "Lie detector system based on PhotoPlethysmoGraph (PPG) and Galvanic Skin Response (GSR) signals by means of neural network," Signal and Data processing, vol. 9, no. 2, pp. 49-60, 2013.##[2] C. S. Segerstrom and E. G. Miller, "Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry," Psychological Bulletin, vol. 130, no. 4, pp.601-630, 2004.##[3] J. Healey and R. W. Picard, "Detecting stress during real-world driving tasks using physiological sensors," IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 2, pp.156–166, 2005.##[4] C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I. Fotiadis, "Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach," IEEE Transaction on systems, man, and cybernetics-part a: systems and humans, vol. 38, no. 3, pp. 502-512, 2008.##[5] J. Zhai and A. Barreto, "Stress detection in computer users through non-invasive monitoring of physiological signals," Biomedical Science Instrumentation, vol. 42, pp.495–500, 2006.##[6] S. de Sierra, C. S. Avila, G. Bailador, and J.G. Casanova, "A stress detection system based on physiological signals and fuzzy logic," IEEE Transactions on Industrial Electronics, vol. 58, no. 10, pp. 4857-4865, 2011.##[7] N. Sharma and T. Gedeon, "Objective measures, sensors and computational techniques for stress recognition and classification: A survey," computer methods and programs in biomedicine, vol. 108, pp. 1287–1301, 2012.##[8] J.J.G. de Vries, S. C. Pauws, and M. Biehl, "Insightful stress detection from physiology modalities using Learning Vector Quantization," Neurocomputing, vol. 151, pp. 873–882, 2015.##[9] B. Park, E. Jang, M. Chung, and S. Kim, "Design of Prototype-Based Emotion Recognizer Using Physiological Signals," ETRI Journal, vol. 35, no. 5, pp. 869-879, 2013.##[10] S.A. Hosseini and M.A. Khalilzadeh, "Emotional stressrecognition system using EEG and psychophysiological signals: using new labelling process of EEG signals in emotional stress state," International Conference of Biomedical Engineering and Computer Science (ICBECS), pp.1–6, 2010.##[11] T. Lin, M. Omata, W. Hu, and A. Imamiya, "Do physiological data relate to traditional usability indexes?" Proceeding of the 17th Australia Conference on Computer–Human Interaction: Citizens Online: Considerations for Today and the Future, pp.1–10, 2005.##[12] M. Kumar, M. Weippert, R. Vilbrandt, S. Kreuzfeld, and R. Stoll, "Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment," IEEE Transactions on fuzzy systems, vol. 15, no. 5, pp. 791-808, 2007.##[13] J. Wang, Ch. Lin, and Y. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp. 136–143, 2013.##[14] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Troster, and U. Ehlert, " Discriminating Stress From Cognitive Load Using a Wearable EDA Device," IEEE Transactions on information technology in biomedicine, vol. 14, no. 2, pp. 410-417, 2010.##[15] Z. Dharmawan, "Analysis of Computer Games Player Stress Level Using EEG Data," M.S. thesis, Dep. Elect. Eng., Delft Univ., Netherlands, 2007.##[16] M. Kumar, S. Neubert, S. Behrendt, A. Rieger, M. Weippert, and N. Stoll, "Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals," IEEE Transactions on fuzzy systems, vol. 20, no. 4, pp. 746-759, 2012.##[17] M. Jiang and Z. Wang, "A method for stress detection based on FCM algorithm," 2nd International Congress on Image and Signal Processing, CISP. , pp. 1 – 5, 2009.##[18] J. Healey, "Wearable and automotive systems for affect recognition from physiology," PhD thesis Dep. Elect. Eng. and comp. science, MIT Univ., 2000.##[19] PHYSIONET, Stress Recognition in Automobile Drivers (drivedb), http://physionet.org/##[20] K. Plarre, A. Raij, S.M. Hossain, A. Ahsan Ali, M. Nakajimaz, M. al'Absiz, E. Ertin, T. Kamarck, S. Kumar, M. Scott, D. Siewioreky, A. Smailagicy, E. Wittmers, and z. Jr, "Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment," 10th International Conference on Information Processing in Sensor Networks (IPSN), pp.12-14, 2011.##[21] Y. Deng, Z. Wu, Ch. Chu, and T. Yang, "Evaluating Feature Selection for Stress Identification," 13th International Conference on Information Reuse and Integration (IRI), pp.584-591, 2012.##[22] L. H. Miller and B. M. Shmavonian, "Replicability of two GSR indices as a function of stress and cognitive activity," Journal of Personality and Social Psychology, pp.753–756, 1965.##[23] B.S. McEwen and R.M. Sapolsky, "Stress and cognitive function," Journal of Current Opinion in Neurobiology, vol. 5, pp. 205–216, 1995.##[24] U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, J. S. Suri, "Heart rate variability: a review," Medical and biological engineering and computing, vol. 44, no. 12, pp. 1031-51, 2006.##[25] D. Giakoumis, D. Tzovarasa, and G. Hassapis, "Subject-dependent biosignal features for increased accuracy in psychological stress detection," International Journal of Human-Computer Studies, vol. 71, pp. 425–439, 2013.##[26] M. Li, S. Yi-chun, L. Yin, Y. Hong, and X. Wei, "Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm," Journal of Shanghai Jiaotong Univ. (Sci.), vol. 20, no.1, pp. 51-55, 2015.##[27] M. Singh and A. Queyam, "Stress Detection in Automobile Drivers using Physiological Parameters: A Review," International Journal of Electronics Engineering, vol. 5, no. 2, pp. 1-5, 2013.##[28] A. Akbas, "Evaluation of the Physiological Data Indicating the Dynamic Stress Level of Drivers," Scientific Research and Essays, vol.6, no.2, pp.430-439, 2006.## ##

مقایسه روش‌های مختلف یادگیری ماشین در خلاصه‌سازی استخراجی گفتار به گفتار فارسی بدون استفاده از رونوشت A comparison of machine learning techniques for Persian Extractive Speech to Speech Summarization without Transcript 1 1 در این مقاله، خلاصه‌سازی استخراجی گفتار با استفاده از روش‌های مختلف یادگیری ماشین مورد مطالعه قرار گرفته است. خلاصه‌سازی یک فایل گفتاری به معنای استخراج بخش‌های مهم و شاخص گفتار به‌منظور  دسترسی، جستجو، استخراج و مرورگری آسان‌تر و کم‌هزینه‌تر اطلاعات فایل‌های گفتاری است. در این مقاله، یک روش جدید خلاصه‌سازی گفتار بدون استفاده از سامانه بازشناسی خودکار گفتار ارائه شده است. الگوهای تکراری بین دو جمله گفتاری با استفاده از الگوریتم S-DTW، به‌طورمستقیم از روی سیگنال گفتار شناسایی می‌شوند. بعد از تعیین شباهت بین دو جمله و استخراج تعدادی ویژگی از هر جمله تأثیر روش‌های مختلف یادگیری ماشین، بانظارت، بی‌نظارت و نیمه‌نظارتی مورد بررسی قرار گرفته است. آزمایش‌ها برروی یک پیکره خوانده‌شده اخبار فارسی انجام شده است. نتایج نشان می‌دهد با استفاده از  ویژگی‌های مناسب، بدون استفاده از رونوشت به کارایی بالاتری نسبت به روش‌های پایه (3٪ افزایش در مقایسه با انتخاب نخستین جملات و 5٪ افزایش در مفایسه با انتخاب طولانی‌ترین جملات با استفاده از معیار ROUGE-3) می‌توان دست پیدا کرد.   2 In this paper, extractive speech summarization using different machine learning algorithms was investigated. The task of Speech summarization deals with extracting important and salient segments from speech in order to access, search, extract and browse speech files easier and in a less costly manner. In this paper, a new method for speech summarization without using automatic speech recognition system (ASR) is proposed. ASR systems usually have high error rates especially in adverse acoustic environment and for low resource languages. Our goal was to answer this question: is it possible to summarize a Persian speech without ASR using less or no training data? We have proposed a method which discovers salient parts directly from speech signal by using a semi-supervised algorithm. The proposed algorithm consists of three main stages, features extraction, identifying key patterns and selecting important sentences. First we have segmented speech voices manually into sentences to eliminate sentence segmentation errors. Therefore, we could have better comparison between different summarization methods. Then we have extracted some features from each sentence such as sentence duration, if the sentence is first or last sentence in the speech and so on. Also, repetitive patterns between each two sentence of speech are discovered directly from speech signal by using S-DTW algorithm. S-DTW algorithm can discover repetitive patterns between two speech signals by using MFCC features. By using these repetitive patterns between each pair of sentences we can make a similarity matrix. Therefore, we could measure the similarity distance between each pair of sentences and eliminate redundant sentences from summary without the need to use an ASR system After finding the similarity between each two speech segments and extracting some features from each segment, various machine learning algorithms including unsupervised (MMR, TextRank), supervised (SVM, Naïve Bayes) and semi-supervised algorithms (self-training, Co-training) are used in order to extract salient parts. Experiences are done in read Persian news. The results show that using semi-supervised co-training method and appropriate features, the performance of speech summarization system on read Persian news corpus can improve about 3% compared to selecting the first sentences and by 5% compared to longest sentences when ROUGE-3 is used as the evaluation measure.     143 157 2015/10/122015/09/212016/06/52016/03/12015/10/302016/02/212016/05/222016/05/232015/06/182016/02/18 1394/11/29 2017/10/252017/12/22017/07/82017/05/52017/10/252017/10/252017/03/52017/12/312017/10/252017/06/10 1396/3/20 هدی سادات جعفری Hoda Sadat Jafari دانشگاه صنعتی امیرکبیر ایران hodas.jafari@gmail.com محمدمهدی همایون پور mohammadmehdi homayounpour دانشگاه صنعتی امیرکبیر ایران homayoun@aut.ac.ir Extractive speech summarization speech signal key patterns S-DTW algorithm machine learning خلاصه‌سازی استخراجی گفتار سیگنال گفتار الگوها کلیدی الگوریتم S-DTW یادگیری ماشین [1] A. McCallum, "An Ecologically Valid Evaluation of Speech Summarizationin the University Lecture Domain," MSc thesis, University of Toronto, 2012.##[2] S. R. Maskey, "Automatic Broadcast News Speech Summarization," PhD thesis, School of Arts and Sciences, Columbia University, 2008.##[3] R. Flamary, X. Anguera, and N. Oliver, "Spoken WordCloud: Clustering recurrent patterns in speech," in CBMI 2011, pp. 133-138.##[4] Y. Liu, S. Xie, and F. Liu, "Using n-best recognition output for extractive summarization and keyword extraction in meeting speech," in ICASSP 2010, pp. 5310-5313.##[5] S. Xie and Y. Liu, "Using N-Best Lists and Confusion Networks for Meeting Summariza-tion," IEEE Transactions on Audio, Speech & Language Processing, vol. 19, no. 5, pp. 1160-1169, 2011.##[6] J. Zhang, R. H. Y. Chan, P. Fung, and L. Cao, "A comparative study on speech summarization of broadcast news and lecture speech," in INTERSPEECH 2007, pp. 2781-2784.##[7] S. Xie, D. Hakkani-Tür, B. Favre, and Y. 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