مسیریابی مؤثر سازگار با انرژی در شبکه‌های سیار موردی براساس منطق فازی شهودی Intuitionistic fuzzy logic for adaptive energy efficient routing in mobile ad-hoc networks 1 1 شبکه‌های سیار موردی بدون هیچ زیرساختار ثابت و مدیریت مرکزی در هر محیطی که نیاز باشد، تشکیل می‌شوند. این شبکه‌ها به‌طور موقت شکل می‌گیرند و گره‌ها می‌توانند آزادانه حرکت کنند؛ به‌طوریکه هر گره دارای انرژی محدودی است که به‌وسیله باتری تأمین می‌شود. به‌دلیل محدودیت انرژی، مسیریابی با انرژی کارآمد یکی از مسائل بسیار مهم و چالش برانگیز در این شبکه‌ها است؛ بنابراین بیش‌تر پژوهش‌گران به‌دنبال ارائه روشی برای مسیریابی انرژی آگاه هستند. در این مقاله برای ارائه یک پروتکل مسیریابی انرژی آگاه از یک سامانه منطق فازی شهودی برای تنظیم پارامتر تمایل گره در پروتکل مسیریابی AODV کمک گرفته شده است. تصمیم برای شرکت‌کردن هر گره متحرک در مسیریابی به‌وسیله سامانه منطق فازی شهودی با مقدار انرژی باقی‌مانده و مقدار انرژی مصرفی گرفته می‌شود. به‌منظور ارزیابی، پروتکل پیشنهادی (IFEE-AODV) را با استفاده از نرم‌افزار متلب شبیه‌سازی کرده و با روش‌های دیگر (AODV، DFES-AODV و SFES-AODV) مقایسه کرده‌ایم که نتایج به‌دست‌آمده نشان می‌دهد، این پروتکل از نظر نرخ تحویل بسته و زمان عمر شبکه در مقایسه با سه روش دیگر از عملکرد خوبی برخوردار است. 2 In recent years, mobile ad-hoc networks have been used widely due to advances in wireless technology. These networks are formed in any environment that is needed without a fixed infrastructure or centralized management. Mobile ad-hoc networks have some characteristics and advantages such as wireless medium access, multi-hop routing, low cost development, dynamic topology and etc. In these networks the nodes formed temporarily and can move freely and each node has a limited energy that is supplied by the battery. Energy-efficient routing is one of the most important and challenging issues in these networks because of the limited energy. Therefore, most researchers seek to provide a method for energy aware routing. Soft computing methods help mobile ad-hoc networks, so that these networks would be worked more efficiently. One of these methods is using intuitionistic fuzzy logic that improves the evaluation parameters such as throughput. In this paper, an intuitionistic fuzzy logic system has been used for adjusting node willingness parameter in AODV protocol. Decision about participating in the routing of each mobile node is done by the intuitionistic fuzzy logic system with remaining energy and consumption energy of each node. In order to evaluate the proposed protocol entitled IFEE-AODV (Intuitionistic Fuzzy logic for Energy Efficient routing based AODV), we simulated IFEE-AODV by using MATLAB software and compared these results with AODV(Ad hoc On-demand Distance Vector), DFES-AODV (Dynamic Fuzzy Energy State based AODV) and SFES-AODV (Static Fuzzy Energy State based AODV) protocols. The results show that this protocol in metrics of packet delivery ratio and network lifetime has better performance than other protocols. 12 3 2019/03/3 1397/12/12 2019/11/10 1398/8/19 ارشام برومند سعید Arsham Borumand Saeid دانشگاه شهید باهنر کرمان ایران arsham@uk.ac.ir مریم حسام پور Maryam Hesampour دانشگاه شهید باهنر کرمان ایران hesampourmaryam@gmail.com مرجان کوچکی رفسنجانی Marjan Kuchaki Rafsanjani دانشگاه شهید باهنر کرمان ایران kuchaki@uk.ac.ir Mobile ad-hoc networks (MANETs) Routing Energy efficient Energy aware Intuitionistic fuzzy logic system شبکه‌های سیار موردی مسیریابی انرژی کارآمد انرژی آگاه سامانه منطق فازی شهودی [1] H. M. Sun, C. H. Chen and Y. F. Ku, "A novel acknowledgment-based approach against collude attacks in MANET", Journal of Expert System with Application, Vol. 39, pp. 7968-7975, 2012.##[2] M. Ilyas, The Hand book of Ad Hoc Wireless Networks, CRC press, 2002.##[3] S. Chettibi and S. Chikhi, "Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks", Applied Soft Computing, Vol. 38, pp. 321-328, 2016.##[4] W. El-Hajj, D. Kountanis, A. Al-Fuqaha and M. Guizani, "A fuzzy based hierarchical energy efficient routing protocol for large scale mobile ad hoc networks", Proceedings of the IEEE International Conference on Communications, Istanbul, Turkey, pp. 3585-3590, June 11-15, 2006.##[5] W. Naruephiphat and W. Usaha, "Balancing tradeoffs for energy efficient routing in MANETs based on reinforcement learning", Proceedings of the 67th IEEE Vehicular Technology Conference, Singapore, pp. 2361-2365, May 11-14, 2008.##[6] N. Chen, Q. Zhang and S. Jin, A fuzzy path selection power-based for MANET, Fuzzy Information and Engineering, B Cao, T. Li, C. Zhang (Eds.), Springer, Berlin Heidelberg, pp. 1283-1291, 2009.##[7] P. Hiremath and S. Joshi, "Energy efficient routing protocol with adaptive fuzzy threshold energy for MANETs", International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 3, pp. 402-407, 2012.##[8] S. Chettibi and S. Chikhi, "An adaptive energy aware routing protocol for MANETs using zero-order sugeno fuzzy system", International Journal of Computer Sience, Vol. 10, No. 1, pp. 136-141, 2013.##[9] K. T. Atanassov, Intuitionistic fuzzy logic: Theory and Applications, Studies in Fuzziness and soft computing Heildberg, Physica-verlag, 1999.##[10] K. T. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets and Systems", Vol. 20, pp. 87-96, 1986.##[11] K. T. Atanassov, "More on intuitionistic fuzzy sets", Fuzzy sets and Systems, Vol. 33, pp. 37- 45, 1989.##[12] M. Kuchaki Rafsanjani, A. Borumand Saeid, F. Mirzapour, "Hybrid multi-criteria group decision making for supplier selection problem with interval-valued intuitionistic fuzzy data", Signal and Data Processing (JSDP), Vol. 17, No. 3, pp. 3-16, 2020.##[13] E. Eslami, "Fuzzy set theory and its extensions", Fuzzy Systems and Applications, Vol. 1, No. 1, pp. 1-22, 2018.##[14] EH. Mamdani, "Application of fuzzy algorithms for control of simple dynamic plant", Proceedings of the Institution of Electrical Engineers, Vol. 121, No. 12, pp. 1585-1588, December 1974.##[15] M. Akram, S. Shahzad, A. Butt and A. Kaliq, "Intuitionistic fuzzy logic control for heater fans", Mathematics in Computer Science, Vol. 7, No. 2, pp. 137-254, 2013.##[1] H. M. Sun, C. H. Chen and Y. F. Ku, "A novel acknowledgment-based approach against collude attacks in MANET", Journal of Expert System with Application, Vol. 39, pp. 7968-7975, 2012.##[2] M. Ilyas, The Hand book of Ad Hoc Wireless Networks, CRC press, 2002.##[3] S. Chettibi and S. Chikhi, "Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks", Applied Soft Computing, Vol. 38, pp. 321-328, 2016.##[4] W. El-Hajj, D. Kountanis, A. Al-Fuqaha and M. Guizani, "A fuzzy based hierarchical energy efficient routing protocol for large scale mobile ad hoc networks", Proceedings of the IEEE International Conference on Communications, Istanbul, Turkey, pp. 3585-3590, June 11-15, 2006.##[5] W. Naruephiphat and W. Usaha, "Balancing tradeoffs for energy efficient routing in MANETs based on reinforcement learning", Proceedings of the 67th IEEE Vehicular Technology Conference, Singapore, pp. 2361-2365, May 11-14, 2008.##[6] N. Chen, Q. Zhang and S. Jin, A fuzzy path selection power-based for MANET, Fuzzy Information and Engineering, B Cao, T. Li, C. Zhang (Eds.), Springer, Berlin Heidelberg, pp. 1283-1291, 2009.##[7] P. Hiremath and S. Joshi, "Energy efficient routing protocol with adaptive fuzzy threshold energy for MANETs", International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 3, pp. 402-407, 2012.##[8] S. Chettibi and S. Chikhi, "An adaptive energy aware routing protocol for MANETs using zero-order sugeno fuzzy system", International Journal of Computer Sience, Vol. 10, No. 1, pp. 136-141, 2013.##[9] K. T. Atanassov, Intuitionistic fuzzy logic: Theory and Applications, Studies in Fuzziness and soft computing Heildberg, Physica-verlag, 1999.##[10] K. T. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets and Systems", Vol. 20, pp. 87-96, 1986.##[11] K. T. Atanassov, "More on intuitionistic fuzzy sets", Fuzzy sets and Systems, Vol. 33, pp. 37- 45, 1989.##[12] م. کوچکی رفسنجانی، ا. برومند سعید و فرزانه میرزاپور، تصمیم¬گیری گروهی چند¬معیاره ترکیبی برای مسأله انتخاب تأمین¬کننده¬ با داده¬های فازی شهودی بازه¬ای مقدار، دوره 17، شماره 3، صفحات 16-3، 1399.##[12] M. Kuchaki Rafsanjani, A. Borumand Saeid, F. Mirzapour, "Hybrid multi-criteria group decision making for supplier selection problem with interval-valued intuitionistic fuzzy data", Signal and Data Processing (JSDP), Vol. 17, No. 3, pp. 3-16, 2020.##[13] ا. اسلامی، نظریه مجموعه¬های فازی و تعمیم¬های آن، سیستم¬های فازی و کاربردها، سال اول، شماره اول، صفحات 22-1، 1397.##[13] E. Eslami, "Fuzzy set theory and its extensions", Fuzzy Systems and Applications, Vol. 1, No. 1, pp. 1-22, 2018.##[14] EH. Mamdani, "Application of fuzzy algorithms for control of simple dynamic plant", Proceedings of the Institution of Electrical Engineers, Vol. 121, No. 12, pp. 1585-1588, December 1974.##[15] M. Akram, S. Shahzad, A. Butt and A. Kaliq, "Intuitionistic fuzzy logic control for heater fans", Mathematics in Computer Science, Vol. 7, No. 2, pp. 137-254, 2013.## ##

یک سامانه پیشنهاددهنده اجتماعی مبتنی بر تجزیه ماتریس با در‌نظر‌گرفتن پویایی علایق کاربران A social recommender system based on matrix factorization considering dynamics of user preferences 1 1 در سال‌های اخیر، استفاده از سامانه‌‌های پیشنهاددهنده در شبکه‌های اجتماعی رشد قابل توجهی داشته است. در این سامانه‌‌ها، رفتار و علایق کاربران در طول زمان تغییر می‌کند و تطبیق سامانه‌های پیشنهاددهنده با این پویایی علایق و نیازهای کاربران به‌منظور ارائه پیشنهادات دقیق‌تر به کاربران ضروری است. علی‌رغم اهمیت این موضوع، اغلب سامانه‌‌های پیشنهاددهنده، رفتار پویای کاربر را در نظر نمی‌گیرند. در این مقاله، یک سامانه پیشنهاددهنده اجتماعی با در‌نظر‌گرفتن پویایی علایق کاربران ارائه می‌شود که از روش تجزیه ماتریس استفاده می‌کند. در مدل پیشنهادی با در‌نظر‌گرفتن این‌که هر کاربر الگوی تغییر علایق خاص خود را دارد، فرض می‌شود که علایق فعلی کاربر به علایق او در دوره زمانی قبلی بستگی دارد، و یک ماتریس انتقال علایق برای هر کاربر به‌منظور مدل‌کردن پویایی علایق کاربر بین دو دوره متوالی آموزش داده می‌شود و با ترکیب امتیازاتِ کاربران و اعتماد بین آن‌ها بر اساس روش تجزیه ماتریس، امتیازاتِ کاربران به اقلام پیش‌بینی می‌شود. ارزیابی‌ها بر روی مجموعه داده Epinions نشان می‌دهند که مدل پیشنهادی نسبت به روش‌های مقایسه‌شده، منجر به بهبود بیشتر دقت در پیش‌بینی امتیازات می‌‌شود. همچنین تحلیل پیچیدگی زمانی مدل پیشنهادی بیان‌گر مقیاس‌پذیر‌بودن این مدل است. 2 With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems, the users’ behavior is dynamic and their preferences change over time for different reasons. The adaptability of recommender systems to capture the evolving user preferences, which are changing constantly, is essential. Recent studies point out that the modeling and capturing the dynamics of user preferences lead to significant improvements in recommendation accuracy. In spite of the importance of this issue, only a few approaches recently proposed that take into account the dynamic behavior of the users in making recommendations. Most of these approaches are based on the matrix factorization scheme. However, most of them assume that the preference dynamics are homogeneous for all users, whereas the changes in user preferences may be individual and the time change pattern for each user differs. In addition, because the amount of numerical ratings dramatically reduced in a specific time period, the sparsity problem in these approaches is more intense. Exploiting social information such as the trust relations between users besides the users’ rating data can help to alleviate the sparsity problem. Although social information is also very sparse, especially in a time period, it is complementary to rating information. Some works use tensor factorization to capture user preference dynamics. Despite the success of these works, the processing and solving the tensor decomposition is hard and usually leads to very high computing costs in practice, especially when the tensor is large and sparse. In this paper, considering that user preferences change individually over time, and based on the intuition that social influence can affect the users’ preferences in a recommender system, a social recommender system is proposed. In this system, the users’ rating information and social trust information are jointly factorized based on a matrix factorization scheme. Based on this scheme, each users and items is characterized by a sets of features indicating latent factors of the users and items in the system. In addition, it is assumed that user preferences change smoothly, and the user preferences in the current time period depend on his/her preferences in the previous time period. Therefore, the user dynamics are modeled into this framework by learning a transition matrix of user preferences between two consecutive time periods for each individual user. The complexity analysis implies that this system can be scaled to large datasets with millions of users and items. Moreover, the experimental results on a dataset from a popular product review website, Epinions, show that the proposed system performs better than competitive methods in terms of MAE and RMSE. 28 13 2019/03/32018/11/13 1397/8/22 2019/11/102020/08/18 1399/5/28 حمیدرضا طهماسبی Hamidreza Tahmasbi دانشگاه آزاد اسلامی واحد کاشمر ایران htahma@gmail.com مهرداد جلالی Mehrdad Jalali دانشگاه آزاد اسلامی واحد مشهد ایران jalali@mshdiau.ac.ir حسن شاکری Hassan Shakeri دانشگاه آزاد اسلامی واحد مشهد ایران shakeri@mshdiau.ac.ir Social recommender system rating prediction preference dynamics matrix factorization trust سامانه پیشنهاددهنده اجتماعی پیش‌بینی امتیازات پویایی علایق تجزیه ماتریس اعتماد [1] M. Hosseini, M. Nasrollahi, and A. Baghaei. "A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering". JSDP, vol 15, no. 2, pp.119-132, 2018.##[2] A. Y. Aravkin, K. R. Varshney, and L. Yang, "Dynamic matrix factorization with social influence," in 2016 IEEE International Workshop on Machine Learning for Signal Processing, 2016, pp. 1-6.##[3] H. Bao, Q. Li, S. S. Liao, S. Song, and H. Gao, "A new temporal and social PMF-based method to predict users' interests in micro-blogging," Decision Support Systems, vol. 55, no. 3, pp. 698-709, 2013.##[4] I. Barjasteh, R. Forsati, D. Ross, A. H. Esfahanian, and H. Radha, "Cold-start recommendation with provable Guarantees: A decoupled approach," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1462-1474, 2016.##[5] J. Cheng, Y. Liu, H. Zhang, X. Wu, and F. Chen, "A new recommendation algorithm based on user's dynamic information in complex social network," Mathematical Problems in Engineering, vol. 2015, pp. 1-6, 2015.##[6] W. Cheng, G. Yin, Y. Dong, H. Dong, and W. Zhang, "Collaborative filtering recommenda-tion on users' interest sequences," Plos One, vol. 11, no. 5, p. e0155739, 2016.##[7] W.-S. Chin, B.-W. Yuan, M.-Y. Yang, Y. Zhuang, Y.-C. Juan, and C.-J. Lin, "LIBMF: a library for parallel matrix factorization in shared-memory systems," The Journal of Machine Learning Research, vol. 17, no. 1, pp. 2971-2975, 2016.##[8] E. Frolov and I. Oseledets, "Tensor methods and recommender systems," Wiley Inter-disciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 3, pp. 1-41, 2017.##[9] J. Gaillard, "Recommender systems : Dynamic adaptation and argumentation.", Ph.D. disser-tation, Dept. Computer Science, Avignon Univ., Avignon, France, 2014.##[10] G. Guo, J. Zhang, and N. Yorke-Smith, "A novel recommendation model regularized with user trust and item ratings," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1607-1620, 2016.##[11] B. Ju, Y. Qian, M. Ye, R. Ni, and C. Zhu, "Using dynamic multi-task non-negative matrix factorization to detect the evolution of user preferences in collaborative filtering," PLoS ONE, vol. 10, no. 8, pp. 1-20, 2015.##[12] Y. Koren, "Collaborative filtering with temporal dynamics," Communications of the ACM, vol. 53, no. 4, pp. 89-97, 2010.##[13] S. Li and Y. Fu, "Robust representations for response prediction," in Robust Repre-sentation for Data Analytics, Springer, pp. 147-174, 2017.##[14] D. R. Liu, K. Y. Chen, Y. C. Chou, and J. H. Lee, "Online recommendations based on dynamic adjustment of recommendation lists," Knowledge-Based Systems, vol. 161, pp. 375-389, 2018.##[15] Y. Y. Lo, W. Liao, C. S. Chang, and Y. C. Lee, "Temporal matrix factorization for tracking concept drift in individual user preferences," IEEE Transactions on Computational Social Systems, vol. 5, no. 1, pp. 156-168, 2018.##[16] W. Lu, S. Ioannidis, S. Bhagat, and L. V. S. Lakshmanan, "Optimal recommendations under attraction, aversion, and social influence," Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, 2014, pp. 811-820.##[17] C. Luo, X. Cai, and N. Chowdhury, "Self-training temporal dynamic collaborative filtering," In Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2014, pp. 461-472.##[18] A. Mnih and R. Salakhutdinov, "Probabilistic matrix factorization," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2008, pp. 1257-1264.##[19] J. Pan, Z. Ma, Y. Pang, and Y. Yuan, "Robust probabilistic tensor analysis for time-variant collaborative filtering," Neurocomputing, vol. 119, pp. 139-143, 2013.##[20] F. S. F. Pereira, J. Gama, S. de Amo, and G. M. B. Oliveira, "On analyzing user preference dynamics with temporal social networks," Machine Learning, vol. 107, no. 11, pp. 1745-1773, 2018.##[21] D. Rafailidis, "Modeling trust and distrust information in recommender systems via joint matrix factorization with signed graphs," in Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016, pp. 1060-1065.##[22] D. Rafailidis, P. Kefalas, and Y. Manolopoulos, "Preference dynamics with multimodal user-item interactions in social media recommendation," Expert Systems with Applications, vol. 74, pp. 11-18, 2017.##[23] D. Rafailidis and A. Nanopoulos, "Modeling users preference dynamics and side information in recommender systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 6, pp. 782-792, 2016.##[24] C. Rana and S. K. Jain, "An evolutionary clustering algorithm based on temporal features for dynamic recommender systems," Swarm and Evolutionary Computation, vol. 14, pp. 21-30, 2014.##[25] N. Sahoo, D. A. Tepper, and T. Mukhopadhyay, "A hidden markov model for collaborative filtering," MIS Quarterly, vol. 36, no. 4, pp. 1329-1356, 2012.##[26] Y. Shi, M. Larson, and A. Hanjalic, "Collaborative filtering beyond the user-item matrix : A survey of the state of the art and future challenges," ACM Computing Surveys (CSUR), vol. 47, no. 1, p. 3, 2014.##[27] A. P. Singh and G. J. Gordon, "Relational learning via collective matrix factorization," Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, 2008, pp. 650-658.##[28] J. Z. Sun, D. Parthasarathy, and K. R. Varshney, "Collaborative kalman filtering for dynamic matrix factorization," IEEE Transactions on Signal Processing, vol. 62, no. 14, pp. 3499-3509, 2014.##[29] H. Tahmasbi, M. Jalali, and H. Shakeri, "Modeling Temporal Dynamics of User Preferences in Movie Recommendation," in 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), 2018, pp. 194-199.##[30] J. Tang, "Epinions Dataset." [Online]. Available: http://www.cse.msu.edu/~tang-jili/trust.html. [Accessed: 05-Jan-2018].##[31] J. Tang, H. Gao, A. Das Sarma, Y. Bi, and H. Liu, "Trust evolution: Modeling and its applications," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 6, pp. 1724-1738, 2015.##[32] C. Tong, J. Qi, Y. Lian, J. Niu, and J. J. P. C. Rodrigues, "TimeTrustSVD: A collaborative filtering model integrating time, trust and rating information," Future Generation Computer Systems, vol. 93, pp. 933-941, 2019.##[33] J. Wang, S. Zhang, X. Liu, Y. Jiang, and M. Zhang, "A novel collective matrix factorization model for recommendation with fine-grained social trust prediction," Con-currency Computation, vol. 29, no. 19, pp. 1-14, 2017.##[34] H. Wu, K. Yue, Y. Pei, B. Li, Y. Zhao, and F. Dong, "Collaborative topic regression with social trust ensemble for recommendation in social media systems," Knowledge-Based Systems, vol. 97, pp. 111-122, 2016.##[35] T. Wu, Y. Feng, J. Sang, B. Qiang, and Y. Wang, "A novel recommendation algorithm incorporating temporal dynamics, reviews and item correlation," IEICE TRANS-ACTIONS on Information and Systems, vol. 101, no. 8, pp. 2027-2034, 2018.##[36] L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell, "Temporal collaborative filtering with bayesian probabilistic tensor factorization," in Proceedings of the 2010 SIAM International Conference on Data Mining, 2010, pp. 211-222.##[37] B. Yang, Y. Lei, J. Liu, and W. Li, "Social collaborative filtering by trust," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 8, pp. 1633-1647, Aug. 2017.##[38] X. Yang, Y. Guo, Y. Liu, and H. Steck, "A survey of collaborative filtering based social recommender systems," Computer Communi-cations, vol. 41, pp. 1-10, 2014.##[39] H. Yin, B. Cui, L. Chen, Z. Hu, and Z. Huang, "A temporal context-aware model for user behavior modeling in social media systems," in Proceedings of the 2014 ACM SIGMOD international conference on Management of data, 2014, no. 1, pp. 1543-1554.##[40] Y. Yu, Y. Gao, H. Wang, and R. Wang, "Joint user knowledge and matrix factorization for recommender systems," World Wide Web, vol. 21, no. 4, pp. 1141-1163, 2018.##[41] F. Yu, A. Zeng, S. Gillard, and M. Medo, "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, vol. 452, pp. 192-208, 2016.##[42] Z. Zhang and H. Liu, "Social recommendation model combining trust propagation and sequential behaviors," Applied Intelligence, vol. 43, no. 3, pp. 695-706, 2015.##[43] F. Zhang, Q. Liu, and A. Zeng, "Timeliness in recommender systems," Expert Systems with Applications, vol. 85, pp. 270-278, 2017.##[44] C. Zhang, K. Wang, H. Yu, J. Sun, and E.-P. Lim, "Latent factor transition for dynamic collaborative filtering," in Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, pp. 452-460.##[45] W. Zhang, Y. Du, T. Yoshida, and Y. Yang, "DeepRec: A Deep Neural Network Approach to Recommendation with Item Embedding and Weighted Loss Function," Information Sciences, vol. 470, pp. 121-140, 2019.##[46] B. Zou, C. Li, L. Tan, and H. Chen, "GPUTENSOR: efficient tensor factorization for context-aware recommendations," Infor-mation Sciences, vol. 299, pp. 159-177, 2015.##[1] حسینی منیره، نصرالهی مقصود، بقائی علی، "یک سامانه توصیه‎گر ترکیبی با استفاده از اعتماد و خوشه‎بندی دوجهته به‎منظور افزایش کارایی پالایش‎گروهی"، پردازش علائم و داده‌ها، 15 (2) : 119-132، 1397.##[1] M. Hosseini, M. Nasrollahi, and A. Baghaei. "A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering". JSDP, vol 15, no. 2, pp.119-132, 2018.##[2] A. Y. Aravkin, K. R. Varshney, and L. Yang, "Dynamic matrix factorization with social influence," in 2016 IEEE International Workshop on Machine Learning for Signal Processing, 2016, pp. 1-6.##[3] H. Bao, Q. Li, S. S. Liao, S. Song, and H. Gao, "A new temporal and social PMF-based method to predict users' interests in micro-blogging," Decision Support Systems, vol. 55, no. 3, pp. 698-709, 2013.##[4] I. Barjasteh, R. Forsati, D. Ross, A. H. Esfahanian, and H. Radha, "Cold-start recommendation with provable Guarantees: A decoupled approach," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1462-1474, 2016.##[5] J. Cheng, Y. Liu, H. Zhang, X. Wu, and F. Chen, "A new recommendation algorithm based on user's dynamic information in complex social network," Mathematical Problems in Engineering, vol. 2015, pp. 1-6, 2015.##[6] W. Cheng, G. Yin, Y. Dong, H. Dong, and W. Zhang, "Collaborative filtering recommenda-tion on users' interest sequences," Plos One, vol. 11, no. 5, p. e0155739, 2016.##[7] W.-S. Chin, B.-W. Yuan, M.-Y. Yang, Y. Zhuang, Y.-C. Juan, and C.-J. Lin, "LIBMF: a library for parallel matrix factorization in shared-memory systems," The Journal of Machine Learning Research, vol. 17, no. 1, pp. 2971-2975, 2016.##[8] E. Frolov and I. Oseledets, "Tensor methods and recommender systems," Wiley Inter-disciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 3, pp. 1-41, 2017.##[9] J. Gaillard, "Recommender systems : Dynamic adaptation and argumentation.", Ph.D. disser-tation, Dept. Computer Science, Avignon Univ., Avignon, France, 2014.##[10] G. Guo, J. Zhang, and N. Yorke-Smith, "A novel recommendation model regularized with user trust and item ratings," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1607-1620, 2016.##[11] B. Ju, Y. Qian, M. Ye, R. Ni, and C. Zhu, "Using dynamic multi-task non-negative matrix factorization to detect the evolution of user preferences in collaborative filtering," PLoS ONE, vol. 10, no. 8, pp. 1-20, 2015.##[12] Y. Koren, "Collaborative filtering with temporal dynamics," Communications of the ACM, vol. 53, no. 4, pp. 89-97, 2010.##[13] S. Li and Y. Fu, "Robust representations for response prediction," in Robust Repre-sentation for Data Analytics, Springer, pp. 147-174, 2017.##[14] D. R. Liu, K. Y. Chen, Y. C. Chou, and J. H. Lee, "Online recommendations based on dynamic adjustment of recommendation lists," Knowledge-Based Systems, vol. 161, pp. 375-389, 2018.##[15] Y. Y. Lo, W. Liao, C. S. Chang, and Y. C. Lee, "Temporal matrix factorization for tracking concept drift in individual user preferences," IEEE Transactions on Computational Social Systems, vol. 5, no. 1, pp. 156-168, 2018.##[16] W. Lu, S. Ioannidis, S. Bhagat, and L. V. S. Lakshmanan, "Optimal recommendations under attraction, aversion, and social influence," Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, 2014, pp. 811-820.##[17] C. Luo, X. Cai, and N. Chowdhury, "Self-training temporal dynamic collaborative filtering," In Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2014, pp. 461-472.##[18] A. Mnih and R. Salakhutdinov, "Probabilistic matrix factorization," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2008, pp. 1257-1264.##[19] J. Pan, Z. Ma, Y. Pang, and Y. Yuan, "Robust probabilistic tensor analysis for time-variant collaborative filtering," Neurocomputing, vol. 119, pp. 139-143, 2013.##[20] F. S. F. Pereira, J. Gama, S. de Amo, and G. M. B. Oliveira, "On analyzing user preference dynamics with temporal social networks," Machine Learning, vol. 107, no. 11, pp. 1745-1773, 2018.##[21] D. Rafailidis, "Modeling trust and distrust information in recommender systems via joint matrix factorization with signed graphs," in Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016, pp. 1060-1065.##[22] D. Rafailidis, P. Kefalas, and Y. Manolopoulos, "Preference dynamics with multimodal user-item interactions in social media recommendation," Expert Systems with Applications, vol. 74, pp. 11-18, 2017.##[23] D. Rafailidis and A. Nanopoulos, "Modeling users preference dynamics and side information in recommender systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 6, pp. 782-792, 2016.##[24] C. Rana and S. K. Jain, "An evolutionary clustering algorithm based on temporal features for dynamic recommender systems," Swarm and Evolutionary Computation, vol. 14, pp. 21-30, 2014.##[25] N. Sahoo, D. A. Tepper, and T. Mukhopadhyay, "A hidden markov model for collaborative filtering," MIS Quarterly, vol. 36, no. 4, pp. 1329-1356, 2012.##[26] Y. Shi, M. Larson, and A. Hanjalic, "Collaborative filtering beyond the user-item matrix : A survey of the state of the art and future challenges," ACM Computing Surveys (CSUR), vol. 47, no. 1, p. 3, 2014.##[27] A. P. Singh and G. J. Gordon, "Relational learning via collective matrix factorization," Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, 2008, pp. 650-658.##[28] J. Z. Sun, D. Parthasarathy, and K. R. Varshney, "Collaborative kalman filtering for dynamic matrix factorization," IEEE Transactions on Signal Processing, vol. 62, no. 14, pp. 3499-3509, 2014.##[29] H. Tahmasbi, M. Jalali, and H. Shakeri, "Modeling Temporal Dynamics of User Preferences in Movie Recommendation," in 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), 2018, pp. 194-199.##[30] J. Tang, "Epinions Dataset." [Online]. Available: http://www.cse.msu.edu/~tang-jili/trust.html. [Accessed: 05-Jan-2018].##[31] J. Tang, H. Gao, A. Das Sarma, Y. Bi, and H. Liu, "Trust evolution: Modeling and its applications," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 6, pp. 1724-1738, 2015.##[32] C. Tong, J. Qi, Y. Lian, J. Niu, and J. J. P. C. Rodrigues, "TimeTrustSVD: A collaborative filtering model integrating time, trust and rating information," Future Generation Computer Systems, vol. 93, pp. 933-941, 2019.##[33] J. Wang, S. Zhang, X. Liu, Y. Jiang, and M. Zhang, "A novel collective matrix factorization model for recommendation with fine-grained social trust prediction," Con-currency Computation, vol. 29, no. 19, pp. 1-14, 2017.##[34] H. Wu, K. Yue, Y. Pei, B. Li, Y. Zhao, and F. Dong, "Collaborative topic regression with social trust ensemble for recommendation in social media systems," Knowledge-Based Systems, vol. 97, pp. 111-122, 2016.##[35] T. Wu, Y. Feng, J. Sang, B. Qiang, and Y. Wang, "A novel recommendation algorithm incorporating temporal dynamics, reviews and item correlation," IEICE TRANS-ACTIONS on Information and Systems, vol. 101, no. 8, pp. 2027-2034, 2018.##[36] L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell, "Temporal collaborative filtering with bayesian probabilistic tensor factorization," in Proceedings of the 2010 SIAM International Conference on Data Mining, 2010, pp. 211-222.##[37] B. Yang, Y. Lei, J. Liu, and W. Li, "Social collaborative filtering by trust," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 8, pp. 1633-1647, Aug. 2017.##[38] X. Yang, Y. Guo, Y. Liu, and H. Steck, "A survey of collaborative filtering based social recommender systems," Computer Communi-cations, vol. 41, pp. 1-10, 2014.##[39] H. Yin, B. Cui, L. Chen, Z. Hu, and Z. Huang, "A temporal context-aware model for user behavior modeling in social media systems," in Proceedings of the 2014 ACM SIGMOD international conference on Management of data, 2014, no. 1, pp. 1543-1554.##[40] Y. Yu, Y. Gao, H. Wang, and R. Wang, "Joint user knowledge and matrix factorization for recommender systems," World Wide Web, vol. 21, no. 4, pp. 1141-1163, 2018.##[41] F. Yu, A. Zeng, S. Gillard, and M. Medo, "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, vol. 452, pp. 192-208, 2016.##[42] Z. Zhang and H. Liu, "Social recommendation model combining trust propagation and sequential behaviors," Applied Intelligence, vol. 43, no. 3, pp. 695-706, 2015.##[43] F. Zhang, Q. Liu, and A. Zeng, "Timeliness in recommender systems," Expert Systems with Applications, vol. 85, pp. 270-278, 2017.##[44] C. Zhang, K. Wang, H. Yu, J. Sun, and E.-P. Lim, "Latent factor transition for dynamic collaborative filtering," in Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, pp. 452-460.##[45] W. Zhang, Y. Du, T. Yoshida, and Y. Yang, "DeepRec: A Deep Neural Network Approach to Recommendation with Item Embedding and Weighted Loss Function," Information Sciences, vol. 470, pp. 121-140, 2019.##[46] B. Zou, C. Li, L. Tan, and H. Chen, "GPUTENSOR: efficient tensor factorization for context-aware recommendations," Infor-mation Sciences, vol. 299, pp. 159-177, 2015.## ##

ارائه مدلی برای تشخیص شایعات فارسی مبتنی بر تحلیل ویژگی‌های محتوایی در متن شبکه‌های اجتماعی A Model for Detecting of Persian Rumors based on the Analysis of Contextual Features in the Content of Social Networks 1 1 شایعه یک تلاش جمعی است که در آن از قدرت واژگان برای تفسیر یک موقعیت مبهم‏ ولی جذاب استفاده میشود؛ بنابراین، شناسایی زبان شایعه میتواند در تشخیص شایعات کمککننده باشد. پژوهش‌های پیشین برای حل مسأله تشخیص شایعه بیشتر بر روی اطلاعات متنی موجود در ریتوییت و توییت پاسخ کاربران و کمتر بر روی متن اصلی شایعه متمرکز شدهاند. اغلب این پژوهش‌ها بر روی زبان انگلیسی بوده و کارهای محدودی در زبان فارسی انجام شده است؛ از اینرو، این مقاله تنها با تمرکز برروی متن اصلی شایعات فارسی و معرفی ویژگیهایی با ارزش اطلاعات محتوایی بالا، مدلی مبتنی بر ویژگیهای محتوایی فیزیکی و غیرفیزیکی برای تشخیص شایعات فارسی منتشر‌شده برروی توییتر و تلگرام ارائه می‌کند. مدل پیشنهادی شایعات فارسی مجموعه‌داده توییتر را با معیار-F 848/0، شایعات مجموعه‌داده زلزله کرمانشاه را با معیار-F 952/0 و شایعات تلگرامی را با معیار-F 867/0 شناسایی کرده است؛ که نشان‌دهنده توانمندی مدل پیشنهادی برای شناسایی شایعات تنها با تمرکز بر ویژگیهای محتوایی متن شایعه منبع است. 2 The rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. Therefore, identifying the rumor language can be helpful in identifying it. The previous research has focused more on the contextual information to reply tweets and less on the content features of the original rumor to address the rumor detection problem. Most of the studies have been in the English language, but more limited work has been done in the Persian language to detect rumors. This study analyzed the content of the original rumor and introduced informative content features to early identify Persian rumors (i.e., when it is published on news media but has not yet spread on social media) on Twitter and Telegram. Therefore, the proposed model is based on physical and non-physical content features in three categories including, lexical, syntactic, and pragmatic. These features are a combination of the common content features along with the proposed new content-based features. Since no social context information is available at the time of posting rumors, the proposed model is independent of propagation-based features and relies on the content-based information of the original rumor. Although in the proposed model, much information (including user information, the user's reaction to the rumor, and propagation structures) are ignored, but helpful content information can be obtained for classification by content analysis of the original rumor. Several experiments have been performed on the various combinations of feature sets (i.e., common and proposed content features) to explore the capability of features in distinguishing rumors and non-rumors separately and jointly. To this end, three machine learning algorithms including, Random Forest (RF), AdaBoost, and Support Vector Machine (SVM) have been used as strong classifications to evaluate the accuracy of the proposed model. To achieve the best performance of classification algorithms on the training dataset, it is necessary to use feature selection techniques. In this study, the Sequential Forward Floating Search (SFFS) approach has been used to select valuable features. Also, the statistical results of the t-test on the P-value (<=0.05) demonstrate that most of the new features proposed in this study reveal statistically significant differences between rumor and non-rumor documents. The experimental results are shown the performance of new proposed features to improve the accuracy of the rumor detection. The F-measure of the proposed model to detect Persian rumors on the Twitter dataset was 0.848, on the Kermanshah earthquake dataset was 0.952 and on the Telegram dataset was 0.867, which indicated the ability of the proposed method to identify rumors only by focusing on the content features of the original rumor text. The results of evaluating the proposed model on Twitter rumors show that, despite the short length of Twitter tweets and the extraction of limited content information from tweets, the proposed model can detect Twitter rumors with acceptable accuracy. Hence, the ability of content features to distinguish rumors from non-rumors is proven. 50 29 2019/03/32018/11/132019/06/13 1398/3/23 2019/11/102020/08/182020/09/23 1399/7/2 زلیخا جهانبخش نقده Zoleikha Jahanbakhsh-Nagadeh دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران ایران zoleikha.jahanbakhsh@srbiau.ac.ir محمد رضا فیضی درخشی Mohammad-Reza Feizi-Derakhshi دانشگاه تبریز ایران mfeizi@tabrizu.ac.ir آرش شریفی Arash Sharifi دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران ایران a.sharifi@srbiau.ac.ir Persian rumors detection Content analysis Physical and non-physical content features Text processing تشخیص شایعات فارسی تحلیل محتوی ویژگی‌های محتوایی فیزیکی و غیرفیزیکی پردازش متن [1] A. Zubiaga et al., "Detection and resolution of rumours in social media: A survey," ACM Comput. Surv., vol. 51, no. 2, p. 32, 2018.##[2] C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web - WWW '11, 2011,pp. 675.##[3] V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei, "Rumor has it: Identifying misinformation in microblogs," in Proceedings of the conference on empirical methods in natural language processing, 2011, pp. 1589-1599.##[4] R. Dayani, N. Chhabra, T. Kadian, and R. Kaushal, "Rumor: Detecting Misinformation in Twitter," in 3rd Security and Privacy Symposium, 2015.##[5] S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent Features of Rumor Propagation in Online Social Media," in 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1103-1108.##[6] S. Hamidian and M. T. Diab, "Rumor Detection and Classification for Twitter Data," in the Fifth International Conference on Social Media Technologies, Communication, and Informatics, 2015.##[7] G. Giasemidis et al., "Determining the veracity of rumours on Twitter," in International Conference on Social Informatics, 2016, pp. 185-205.##[8] K. Wu, S. Yang, and K. Q. Zhu, "False rumors detection on Sina Weibo by propagation structures," in 2015 IEEE 31st International Conference on Data Engineering, 2015, vol. 2015-May, pp. 651-662.##[9] S. Hamidian and M. Diab, "Rumor Identification and Belief Investigation on Twitter," in Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016, pp. 3-8.##[10] Z. Zhao, P. Resnick, and Q. Mei, "Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts," in Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 1395-1405.##[11] E. Rahim zade, S. Soltanipour, "Analysis of the content of denied news and rumors in the print and online media of Iran on the eve of the 10th term of the Islamic Consultative Assembly," in the second national conference on media, communications and education Citizenship, Tehran, 2017, (In Persian).##[12] S. Zamani, M. Asadpour, and D. Moazzami, "Rumor detection for Persian Tweets," in 2017 Iranian Conference on Electrical Engineering (ICEE), 2017, pp. 1532-1536.##[13] M. Seifikar, S. Farzi, and S. D. Mahmoodabad, "Kermanshah Earthquake Event Tracking Through Persian Tweets," in 2018 9th International Symposium on Telecommunications (IST), 2018, pp. 424-428.##[14] A. Y. K. Chua and S. Banerjee, "Linguistic predictors of rumor veracity on the Internet," in Lecture Notes in Engineering and Computer Science, 2016, vol. 1, pp. 387-391.##[15] Q. Li, Q. Zhang, and L. Si, "eventai at semeval-2019 task 7: Rumor detection on social media by exploiting content, user credibility and propagation information," in Proceedings of the 13th International Workshop on Semantic Evaluation, 2019, pp. 855-859.##[16] F. Xing and C. Guo, "Mining Semantic Information in Rumor Detection via a Deep Visual Perception Based Recurrent Neural Networks," in 2019 IEEE International Congress on Big Data (BigDataCongress), 2019, pp. 17-23.##[17] S. Vosoughi and D. Roy, "A human-machine collaborative system for identifying rumors on twitter," in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, pp. 47-50.##[18] A. Y. K. Chua and S. Banerjee, "Linguistic predictors of rumor veracity on the Internet," pp. 387-391, 2016.##[19] A. Zubiaga, M. Liakata, and R. Procter, "Exploiting context for rumour detection in social media," in International Conference on Social Informatics, 2017, pp. 109-123.##[20] S. Mahmoodabad, … S. F.-2018 9th I., and U. 2018, "Persian Rumor Detection on Twitter," ieeexplore.ieee.org.##[21] H. K. Thakur, A. Gupta, A. Bhardwaj, and D. Verma, "Rumor Detection on Twitter Using a Supervised Machine Learning Framework," Int. J. Inf. Retr. Res., vol. 8, no. 3, pp. 1-13, Jul. 2018.##[22] S. Vosoughi, D. Roy, and S. Aral, "The spread of true and false news online," Science (80-. )., vol. 359, no. 6380, pp. 1146-1151, 2018.##[23] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Inf. Sci. (Ny)., vol. 497, pp. 38-55, 2019.##[24] G. W. Allport and L. Postman, The psychology of rumor. Henry Holt, 1947.##[25] H. Mohammadi and S. H. Khasteh, "A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset," Oct. 2018.##[26] M. M. Homayounpour and A. S. Panah, "Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods," Int. J. Inf. Commun. Technol. Res., vol. 2, no. 1, pp. 65-71, 2010.##[27] Z. Jahanbakhsh-Nagadeh, M.-R. Feizi-Derakhshi, and A. Sharifi, "A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors," J. Soft Comput. Inf. Technol. (JSCIT) Vol, vol. 9, no. 1, 2020.##[28] S. M. Mohammad and P. D. Turney, "Crowdsourcing a word-emotion association lexicon," in Computational Intelligence, 2013, vol. 29, no. 3, pp. 436-465.##[29] H. Moradi, F. Ahmadi, and M.-R. Feizi-Derakhshi, "A Hybrid Approach for Persian Named Entity Recognition," Iran. J. Sci. Technol. Trans. A Sci., vol. 41, no. 1, pp. 215-222, 2017.##[30] V. Korde and C. N. Mahender, "Text classification and classifiers: A survey," Int. J. Artif. Intell. Appl., vol. 3, no. 2, p. 85, 2012.##[31] A.-R. Feizi-Derakhshi et al., "Sepehr_RumTel01," 2019.##[32] H. Jafary, M.-T. Taghavifard, P. Hanafizadeh, and A. Kazazi, "Native Quality Assessment Model of news sites (NEWSQUAL)," J. Soft Comput. Inf. Technol., vol. 7, no. 1, pp. 56-71, 2018, (In Persian).##[33] M. Wainberg, B. Alipanahi, and B. J. Frey, "Are random forests truly the best classifiers?," J. Mach. Learn. Res., vol. 17, no. 1, pp. 3837-3841, 2016.##[34] J. Wainer, "Comparison of 14 different families of classification algorithms on 115 binary datasets," arXiv Prepr. arXiv1606.00930, 2016.##[35] A. J. Wyner, M. Olson, J. Bleich, and D. Mease, "Explaining the success of adaboost and random forests as interpolating classifiers," J. Mach. Learn. Res., vol. 18, no. 1, pp. 1558-1590, 2017.##[36] L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001.##[37] Y. Freund and R. E. Schapire, "A desicion-theoretic generalization of on-line learning and an application to boosting," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 904, 1995, pp. 23-37.##[38] C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995.##[39] N. El Aboudi and L. Benhlima, "Review on wrapper feature selection approaches," in Proceedings - 2016 International Conference on Engineering and MIS, ICEMIS 2016, 2016.##[1] A. Zubiaga et al., "Detection and resolution of rumours in social media: A survey," ACM Comput. Surv., vol. 51, no. 2, p. 32, 2018.##[2] C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web - WWW '11, 2011,pp. 675.##[3] V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei, "Rumor has it: Identifying misinformation in microblogs," in Proceedings of the conference on empirical methods in natural language processing, 2011, pp. 1589-1599.##[4] R. Dayani, N. Chhabra, T. Kadian, and R. Kaushal, "Rumor: Detecting Misinformation in Twitter," in 3rd Security and Privacy Symposium, 2015.##[5] S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent Features of Rumor Propagation in Online Social Media," in 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1103-1108.##[6] S. Hamidian and M. T. Diab, "Rumor Detection and Classification for Twitter Data," in the Fifth International Conference on Social Media Technologies, Communication, and Informatics, 2015.##[7] G. Giasemidis et al., "Determining the veracity of rumours on Twitter," in International Conference on Social Informatics, 2016, pp. 185-205.##[8] K. Wu, S. Yang, and K. Q. Zhu, "False rumors detection on Sina Weibo by propagation structures," in 2015 IEEE 31st International Conference on Data Engineering, 2015, vol. 2015-May, pp. 651-662.##[9] S. Hamidian and M. Diab, "Rumor Identification and Belief Investigation on Twitter," in Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016, pp. 3-8.##[10] Z. Zhao, P. Resnick, and Q. Mei, "Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts," in Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 1395-1405.##[11]سمانه سلطانی¬پور، احسان رحیم¬زاده، "تحلیل محتوای اخبار و شایعات تکذیب شده در رسانه¬های مکتوب و آنلاین ایران در آستانه برگزاری انتخابات دهمین دوره مجلس شورای اسلامی"، دومین همایش ملی رسانه، ارتباطات و آموزش¬های شهروندی، تهران، 11 اسفند 1395.##[11] E. Rahim zade, S. Soltanipour, "Analysis of the content of denied news and rumors in the print and online media of Iran on the eve of the 10th term of the Islamic Consultative Assembly," in the second national conference on media, communications and education Citizenship, Tehran, 2017, (In Persian).##[12] S. Zamani, M. Asadpour, and D. Moazzami, "Rumor detection for Persian Tweets," in 2017 Iranian Conference on Electrical Engineering (ICEE), 2017, pp. 1532-1536.##[13] M. Seifikar, S. Farzi, and S. D. Mahmoodabad, "Kermanshah Earthquake Event Tracking Through Persian Tweets," in 2018 9th International Symposium on Telecommunications (IST), 2018, pp. 424-428.##[14] A. Y. K. Chua and S. Banerjee, "Linguistic predictors of rumor veracity on the Internet," in Lecture Notes in Engineering and Computer Science, 2016, vol. 1, pp. 387-391.##[15] Q. Li, Q. Zhang, and L. Si, "eventai at semeval-2019 task 7: Rumor detection on social media by exploiting content, user credibility and propagation information," in Proceedings of the 13th International Workshop on Semantic Evaluation, 2019, pp. 855-859.##[16] F. Xing and C. Guo, "Mining Semantic Information in Rumor Detection via a Deep Visual Perception Based Recurrent Neural Networks," in 2019 IEEE International Congress on Big Data (BigDataCongress), 2019, pp. 17-23.##[17] S. Vosoughi and D. Roy, "A human-machine collaborative system for identifying rumors on twitter," in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, pp. 47-50.##[18] A. Y. K. Chua and S. Banerjee, "Linguistic predictors of rumor veracity on the Internet," pp. 387-391, 2016.##[19] A. Zubiaga, M. Liakata, and R. Procter, "Exploiting context for rumour detection in social media," in International Conference on Social Informatics, 2017, pp. 109-123.##[20] S. Mahmoodabad, … S. F.-2018 9th I., and U. 2018, "Persian Rumor Detection on Twitter," ieeexplore.ieee.org.##[21] H. K. Thakur, A. Gupta, A. Bhardwaj, and D. Verma, "Rumor Detection on Twitter Using a Supervised Machine Learning Framework," Int. J. Inf. Retr. Res., vol. 8, no. 3, pp. 1-13, Jul. 2018.##[22] S. Vosoughi, D. Roy, and S. Aral, "The spread of true and false news online," Science (80-. )., vol. 359, no. 6380, pp. 1146-1151, 2018.##[23] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Inf. Sci. (Ny)., vol. 497, pp. 38-55, 2019.##[24] G. W. Allport and L. Postman, The psychology of rumor. Henry Holt, 1947.##[25] H. Mohammadi and S. H. Khasteh, "A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset," Oct. 2018.##[26] M. M. Homayounpour and A. S. Panah, "Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods," Int. J. Inf. Commun. Technol. Res., vol. 2, no. 1, pp. 65-71, 2010.##[27] Z. Jahanbakhsh-Nagadeh, M.-R. Feizi-Derakhshi, and A. Sharifi, "A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors," J. Soft Comput. Inf. Technol. (JSCIT) Vol, vol. 9, no. 1, 2020.##[28] S. M. Mohammad and P. D. Turney, "Crowdsourcing a word-emotion association lexicon," in Computational Intelligence, 2013, vol. 29, no. 3, pp. 436-465.##[29] H. Moradi, F. Ahmadi, and M.-R. Feizi-Derakhshi, "A Hybrid Approach for Persian Named Entity Recognition," Iran. J. Sci. Technol. Trans. A Sci., vol. 41, no. 1, pp. 215-222, 2017.##[30] V. Korde and C. N. Mahender, "Text classification and classifiers: A survey," Int. J. Artif. Intell. Appl., vol. 3, no. 2, p. 85, 2012.##[31] A.-R. Feizi-Derakhshi et al., "Sepehr_RumTel01," 2019.##[32]محمدتقی تقوی فرد، پیام حنفی زاده، ابوالفضل کزازی، حمید جعفری، "مدل بومی ارزیابی کیفیت سایت‌های خبری(NEWSQUAL) "، مجله علمی-پژوهشی رایانش نرم و فن¬آوری اطلاعات، دوره 7، شماره 1، بهار و تابستان 1397، صفحه 56-71.##[32] H. Jafary, M.-T. Taghavifard, P. Hanafizadeh, and A. Kazazi, "Native Quality Assessment Model of news sites (NEWSQUAL)," J. Soft Comput. Inf. Technol., vol. 7, no. 1, pp. 56-71, 2018, (In Persian).##[33] M. Wainberg, B. Alipanahi, and B. J. Frey, "Are random forests truly the best classifiers?," J. Mach. Learn. Res., vol. 17, no. 1, pp. 3837-3841, 2016.##[34] J. Wainer, "Comparison of 14 different families of classification algorithms on 115 binary datasets," arXiv Prepr. arXiv1606.00930, 2016.##[35] A. J. Wyner, M. Olson, J. Bleich, and D. Mease, "Explaining the success of adaboost and random forests as interpolating classifiers," J. Mach. Learn. Res., vol. 18, no. 1, pp. 1558-1590, 2017.##[36] L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001.##[37] Y. Freund and R. E. Schapire, "A desicion-theoretic generalization of on-line learning and an application to boosting," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 904, 1995, pp. 23-37.##[38] C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995.##[39] N. El Aboudi and L. Benhlima, "Review on wrapper feature selection approaches," in Proceedings - 2016 International Conference on Engineering and MIS, ICEMIS 2016, 2016.## ##

بهبود دقت واژگان کلیدی استخراج‌شده از متن فارسی با استفاده از الگوریتم Word2Vec Improving Precision of Keywords Extracted From Persian Text Using Word2Vec Algorithm 1 1 واژگان کلیدی لغات مهمی از سند هستند که بیان‌گر توصیفی از متن هستند و نقش بسیار مهمی در فهم دقیق و سریع از محتوا دارند. شناسایی واژگان کلیدی از متن با روش‌های معمول کاری زمان‌بر و پرهزینه است. در این مقاله ابتدا با استفاده از شبکه عصبی پیشرو و از طریق الگوریتم Word2Vec ماتریس همبستگی واژگان را به‌ازای یک سند محاسبه و سپس با استفاده از ماتریس همبستگی و یک فهرست اولیه محدود از واژگان کلیدی، نزدیک‌ترین واژگان را از نظر شباهت در قالب فهرست نزدیک‌ترین همسایگی‌ها استخراج می‌کنیم. فهرست به‌دست‌آمده را به‌صورت نزولی مرتب و از ابتدای فهرست، درصدهای مختلفی از واژگان را انتخاب و به‌ازای هر درصد، ده مرتبه فرایند آموزش شبکه عصبی و ساخت ماتریس همبستگی و استخراج فهرست نزدیک‌ترین همسایگی‌ها را تکرار و در‌نهایت میانگین دقت، فراخوانی و معیارF را محاسبه می‌کنیم. این کار را تا جایی ادامه می‌دهیم که به بهترین نتایج در ارزیابی دست یابیم؛ نتایج نشان می‌دهند که به‌ازای انتخاب حداکثر چهل درصدِ واژگان از ابتدای فهرستِ نزدیک‌ترین همسایگی‌ها، نتایج مورد قبولی به‌دست می‌آید. الگوریتم بر روی پیکره‌ای با هشتصد خبر که به‌صورت دستی واژگان کلیدی آن‌ها را استخراج کرده‌ایم، آزمایش‌شده است و نتایج آزمایش‌ها نشان می‌دهد که دقت روش پیشنهادی 78 درصد خواهد بود. 2 Keywords can present the main concepts of the text without human intervention according to the model. Keywords are important vocabulary words that describe the text and play a very important role in accurate and fast understanding of the content. The purpose of extracting keywords is to identify the subject of the text and the main content of the text in the shortest time. Keyword extraction plays an important role in the fields of text summarization, document labeling, information retrieval, and subject extraction from text. For example, summarizing the contents of large texts into smaller texts is difficult, but having keywords in the text can make you aware of the topics in the text. Identifying keywords from the text with common methods is time-consuming and costly. Keyword extraction methods can be classified into two types with observer and without observer. In general, the process of extracting keywords can be explained in such a way that first the text is converted into smaller units called the word, then the redundant words are removed and the remaining words are weighted, then the keywords are selected from these words. Our proposed method in this paper for identifying keywords is a method with observer. In this paper, we first calculate the word correlation matrix per document using a feed forward neural network and Word2Vec algorithm. Then, using the correlation matrix and a limited initial list of keywords, we extract the closest words in terms of similarity in the form of the list of nearest neighbors. Next we sort the last list in descending format, and select different percentages of words from the beginning of the list, and repeat the process of learning the neural network 10 times for each percentage and creating a correlation matrix and extracting the list of closest neighbors. Finally, we calculate the average accuracy, recall, and F-measure. We continue to do this until we get the best results in the evaluation, the results show that for the largest selection of 40% of the words from the beginning of the list of closest neighbors, the acceptable results are obtained. The algorithm has been tested on corpus with 800 news items that have been manually extracted by keywords, and laboratory results show that the accuracy of the suggested method will be 78%. 60 51 2019/03/32018/11/132019/06/132018/04/22 1397/2/2 2019/11/102020/08/182020/09/232021/02/27 1399/12/9 محمدرضا حسنی آهنگر Mohammad Reza Hasni Ahangar دانشگاه جامع امام حسین (ع) ایران mrhasani@ihu.ac.ir علی امیری جزه Ali Amiri jezeh دانشگاه جامع امام حسین (ع) ایران aamirij@ihu.ac.ir keywords word2vec algorithm neural network giving weight features واژگان کلیدی الگوریتم word2Vec شبکه عصبی وزن دهی ویژگی [1] F. Liu, X. Huang, W. Huang, and S. X. Duan, "Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments," Symmetry, vol. 12, no. 11, p. 1923, 2020.##[2] H. Yan, Q. He, and W. Xie, "Crnn-Ctc Based Mandarin Keywords Spotting," in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020: IEEE, pp. 7489-7493.##[3] Y. Zhang, M. Tuo, Q. Yin, L. Qi, X. Wang, and T. Liu, "Keywords extraction with deep neural network model," Neurocomputing, vol. 383, pp. 113-121, 2020.##[4] M. Mohammadi and M. Analouyi, "Keyword extraction in Persian documents," presented at the 13th Conference of Iran Computer Association, Kish, Iran, 2007.##[5] H. Veisi, N. Aflaki, and P. Parsafard, "Variance-based features for keyword extraction in Persian and English text documents," Scientia Iranica, vol. 27, no. 3, pp. 1301-1315, 2020.##[6] C. Zhang, "Automatic keyword extraction from documents using conditional random fields," Journal of Computational Information Systems, vol. 4, no. 3, pp. 1169-1180, 2008.##[7] X. Wan and J. Xiao, "Single Document Keyphrase Extraction Using Neighborhood Knowledge," in AAAI, 2008, vol. 8, pp. 855-860.##[8] D. B. Bracewell, F. Ren, and S. Kuriowa, "Multilingual single document keyword extraction for information retrieval," in 2005 International Conference on Natural Language Processing and Knowledge Engineering, 2005: IEEE, pp. 517-522.##[9] P. D. Turney, "Learning to extract keyphrases from text," arXiv preprint cs/021, 2002, 2013.##[10] Y. Matsuo and M. Ishizuka, "Keyword extraction from a single document using word co-occurrence statistical information," International Journal on Artificial Intelligence Tools, vol. 13, no. 01, pp. 157-169, 2004.##[11] S. Rose, D. Engel, N. Cramer, and W. Cowley, "Automatic keyword extraction from individual documents," Text mining: applications and theory, vol. 1, pp. 1-20, 2010.##[12] J. Wang, H. Peng, and J.-s. Hu, "Automatic keyphrases extraction from document using neural network," in Advances in Machine Learning and Cybernetics: Springer, 2006, pp. 633-641.##[13] A. Ahmadi and T. Hosseinkhah, "Extract keywords from a text using neural networks," presented at the 10th International Conference on Industrial Engineering, Tehran, Iran Industrial Engineering Association, Amirkabir University of Technology, 2013.##[14] S. De Deyne, S. Verheyen, and G. Storms, "Structure and organization of the mental lexicon: A network approach derived from syntactic dependency relations and word associations," in Towards a theoretical framework for analyzing complex linguistic networks: Springer, 2016, pp. 47-79.##[15] E. L. Lin and G. L. Murphy, "Thematic relations in adults' concepts," Journal of experimental psychology: General, vol. 130, no. 1, p. 3, 2001.##[16] F. Liu, D. Pennell, F. Liu, and Y. Liu, "Unsupervised approaches for automatic keyword extraction using meeting transcripts," in Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics, 2009, pp. 620-628.##[17] X. Ao, X. Yu, D. Liu, and H. Tian, "News keywords extraction algorithm based on TextRank and classified TF-IDF," in 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020: IEEE, pp. 1364-1369.##[18] F. Liu, X. Huang, and W. Huang, "Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments," in International Conference on Cooperative Design, Visualization and Engineering, 2020: Springer, pp. 178-183.##[19] R. Campos, V. Mangaravite, A. Pasquali, A. Jorge, C. Nunes, and A. Jatowt, "YAKE! Keyword extraction from single documents using multiple local features," Information Sciences, vol. 509, pp. 257-289, 2020.##[20] J. R. Thomas, S. K. Bharti, and K. S. Babu, "Automatic keyword extraction for text summarization in e-newspapers," in Procee-dings of the international conference on informatics and analytics, 2016, pp. 1-8.##[21] D. M. Allen, "The relationship between variable selection and data agumentation and a method for prediction," technometrics, vol. 16, no. 1, pp. 125-127, 1974.##[22] M. Stone, "Cross‐validatory choice and assessment of statistical predictions," Journal of the Royal Statistical Society: Series B (Methodological), vol. 36, no. 2, pp. 111-133,1974.##[23] M. Stone, "An asymptotic equivalence of choice of model by cross‐validation and Akaike's criterion," Journal of the Royal Statistical Society: Series B (Methodological), vol. 39, no. 1, pp. 44-47, 1977.##[24] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word represen-tations in vector space," arXiv preprint arXiv:1301.3781, 2013.##[25] C. Manning and R. Socher, "Natural language processing with deep learning," Lecture Notes Stanford University School of Engineering, 2017.##[26] J. Hu, S. Li, Y. Yao, L. Yu, G. Yang, and J. Hu, "Patent keyword extraction algorithm based on distributed representation for patent classification," Entropy, vol. 20, no. 2, pp. 104, 2018.##[27] H. Omid and S. Saeedeh, Sadidpour, "Automatic extraction of Persian short text keywords using word2vec," Electronic and cyber defense, vol. 8, 2, pp. 105-114, 2020.##[1] F. Liu, X. Huang, W. Huang, and S. X. Duan, "Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments," Symmetry, vol. 12, no. 11, p. 1923, 2020.##[2] H. Yan, Q. He, and W. Xie, "Crnn-Ctc Based Mandarin Keywords Spotting," in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020: IEEE, pp. 7489-7493.##[3] Y. Zhang, M. Tuo, Q. Yin, L. Qi, X. Wang, and T. Liu, "Keywords extraction with deep neural network model," Neurocomputing, vol. 383, pp. 113-121, 2020.##[4] M. Mohammadi and M. Analouyi, "Keyword extraction in Persian documents," presented at the 13th Conference of Iran Computer Association, Kish, Iran, 2007.##[5] H. Veisi, N. Aflaki, and P. Parsafard, "Variance-based features for keyword extraction in Persian and English text documents," Scientia Iranica, vol. 27, no. 3, pp. 1301-1315, 2020.##[6] C. Zhang, "Automatic keyword extraction from documents using conditional random fields," Journal of Computational Information Systems, vol. 4, no. 3, pp. 1169-1180, 2008.##[7] X. Wan and J. Xiao, "Single Document Keyphrase Extraction Using Neighborhood Knowledge," in AAAI, 2008, vol. 8, pp. 855-860.##[8] D. B. Bracewell, F. Ren, and S. Kuriowa, "Multilingual single document keyword extraction for information retrieval," in 2005 International Conference on Natural Language Processing and Knowledge Engineering, 2005: IEEE, pp. 517-522.##[9] P. D. Turney, "Learning to extract keyphrases from text," arXiv preprint cs/021, 2002, 2013.##[10] Y. Matsuo and M. Ishizuka, "Keyword extraction from a single document using word co-occurrence statistical information," International Journal on Artificial Intelligence Tools, vol. 13, no. 01, pp. 157-169, 2004.##[11] S. Rose, D. Engel, N. Cramer, and W. Cowley, "Automatic keyword extraction from individual documents," Text mining: applications and theory, vol. 1, pp. 1-20, 2010.##[12] J. Wang, H. Peng, and J.-s. Hu, "Automatic keyphrases extraction from document using neural network," in Advances in Machine Learning and Cybernetics: Springer, 2006, pp. 633-641.##[13] A. Ahmadi and T. Hosseinkhah, "Extract keywords from a text using neural networks," presented at the 10th International Conference on Industrial Engineering, Tehran, Iran Industrial Engineering Association, Amirkabir University of Technology, 2013.##[14] S. De Deyne, S. Verheyen, and G. Storms, "Structure and organization of the mental lexicon: A network approach derived from syntactic dependency relations and word associations," in Towards a theoretical framework for analyzing complex linguistic networks: Springer, 2016, pp. 47-79.##[15] E. L. Lin and G. L. Murphy, "Thematic relations in adults' concepts," Journal of experimental psychology: General, vol. 130, no. 1, p. 3, 2001.##[16] F. Liu, D. Pennell, F. Liu, and Y. Liu, "Unsupervised approaches for automatic keyword extraction using meeting transcripts," in Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics, 2009, pp. 620-628.##[17] X. Ao, X. Yu, D. Liu, and H. Tian, "News keywords extraction algorithm based on TextRank and classified TF-IDF," in 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020: IEEE, pp. 1364-1369.##[18] F. Liu, X. Huang, and W. Huang, "Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments," in International Conference on Cooperative Design, Visualization and Engineering, 2020: Springer, pp. 178-183.##[19] R. Campos, V. Mangaravite, A. Pasquali, A. Jorge, C. Nunes, and A. Jatowt, "YAKE! Keyword extraction from single documents using multiple local features," Information Sciences, vol. 509, pp. 257-289, 2020.##[20] J. R. Thomas, S. K. Bharti, and K. S. Babu, "Automatic keyword extraction for text summarization in e-newspapers," in Procee-dings of the international conference on informatics and analytics, 2016, pp. 1-8.##[21] D. M. Allen, "The relationship between variable selection and data agumentation and a method for prediction," technometrics, vol. 16, no. 1, pp. 125-127, 1974.##[22] M. Stone, "Cross‐validatory choice and assessment of statistical predictions," Journal of the Royal Statistical Society: Series B (Methodological), vol. 36, no. 2, pp. 111-133,1974.##[23] M. Stone, "An asymptotic equivalence of choice of model by cross‐validation and Akaike's criterion," Journal of the Royal Statistical Society: Series B (Methodological), vol. 39, no. 1, pp. 44-47, 1977.##[24] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word represen-tations in vector space," arXiv preprint arXiv:1301.3781, 2013.##[25] C. Manning and R. Socher, "Natural language processing with deep learning," Lecture Notes Stanford University School of Engineering, 2017.##[26] J. Hu, S. Li, Y. Yao, L. Yu, G. Yang, and J. Hu, "Patent keyword extraction algorithm based on distributed representation for patent classification," Entropy, vol. 20, no. 2, pp. 104, 2018.##[27] H. Omid and S. Saeedeh, Sadidpour, "Automatic extraction of Persian short text keywords using word2vec," Electronic and cyber defense, vol. 8, 2, pp. 105-114, 2020.## ##

شناسایی جوامع هم‌پوشان با استفاده از هوش جمعی چند‌عامله Identifying overlapping communities using multi-agent collective intelligence 1 1 چشم‌انداز شبکه اجتماعی روش واضحی را برای تجزیه و تحلیل ساختار کل نهادهای اجتماعی فراهم می‌کند. تشخیص جوامع در شبکه‌ها یکی از چالش‌های اساسی در علم شبکه و نیز یکی از بزرگ‌ترین دغدغه‌ها پس از شناسایی جوامع، شناسایی جامعه اصلی عوامل فعال در شبکه است که متعلق به چندین جامعه هستند. یافتن جوامعی که با یکدیگر هم‌پوشانی دارند، در شبکه‌های اجتماعی یک مبحث مهم و جالب در داده‌کاوی و سامانه‌‌های پیشنهاد‌دهنده است. الگوریتم ارائه‌شده در این مقاله مبتنی بر بهینه‌سازی ازدحام ذرات چند‌عامله است؛ به‌‌گونه‌ای که خود سازماندهی در مجموعه فعالیت‌ عامل‌ها دیده می‌شود. هوش جمعی باعث افزایش دقت جستجوی سراسری می‌شود و با استفاده از نوع خاصی کدگذاری، تعداد جوامع را تشخیص می‌دهد؛ به‌گونه‌ای که شاخص پیمانگی به‌عنوان تابع برازش، در بهینه‌سازی ازدحام ذرات مورد استفاده قرار خواهد گرفت. آزمایش‌های متعدد نشان‌ می‌دهد الگوریتم معرفی‌شده با نام بهینه‌سازی ازدحام ذرات چند‌عامله، قادر به تشخیص گره‌های موجود در جوامع هم‌پوشان با دقت بسیار بالا است. در گذشته پژوهش‌هایی در‌‌خصوص تشخیص جوامع با استفاده از بهنیه‌سازی ازدحام ذرات انجام شده است، اما آن‌ها تنها‌ قادر به تشخیص جوامع غیر هم‌پوشان هستند. 2 The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial for many network systems, the algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In this method, the modularity function is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm which is called Multi Agent Particle Swarm is superior compared with other algorithms. This algorithm is capable of detecting nodes in overlapping communities with high accuracy. The point in using the previously presented PSO algorithms for community detection is that they recognize non-overlapping communities, and this goes back to the representation of genes by these methods, but the use of multi-agent collective intelligence by our algorithm has led to the identification of nodes in overlapping communities. The results show that the nodes that are shared between a set of agents, these nodes are active nodes that create an overlap in the communities. Our experimental results show that when a member node is more than one community, this node is a good candidate to be selected as the active node, which has led to the creation of overlapping networks. 74 61 2019/03/32018/11/132019/06/132018/04/222019/02/6 1397/11/17 2019/11/102020/08/182020/09/232021/02/272020/01/22 1398/11/2 محمد عکافان Mohammad Akafan دانشگاه آزاد اسلامی واحد تهران/شمال ایران akafan@chmail.ir بهروز مینایی Behrouz Minaei دانشگاه علم و صنعت ایران ایران b_minaei@iust.ac.ir علیرضا باقری Alireza Bagheri دانشگاه صنعتی امیرکبیر ایران ar_bagheri@aut.ac.ir Particle swarm optimization Multi-agent Modularity Social network Overlapping community detection بهینه‌سازی ازدحام ذرات چند عامله پیمانگی شبکه‌ اجتماعی شناسایی جوامع هم‌پوشان [1] M. G. a. M. E. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, pp. 7821-7826, 2002.##[2] Bajec and L. Š. M, "Group detection in complex networks: An algorithm and comparison of the state of the art," Physica A: Statistical Mechanics and its Applications, pp. 144-156, 2014.##[3] S. Fortunato, "Community detection in graphs," Physics Report, pp. 75-174, 2010.##[4] Newman, "Finding and evaluating community structure in networks," Physical, 2004.##[5] B. J. J. L. e. a. Shang R, "Community detection based on modularity and an improved genetic algorithm," Physica A:Statistical Mechanics and its Applications, pp. 1215-1231, 2013.##[6] Y. B. L. J. e. a. Jin D, "Ant colony optimization based on random walk for community detection in complex networks," Journal of Software, pp. 451-464, 2012.##[7] A. A. P. M. Shadi Rahimi, A multi-objective particle swarm optimization algorithm for community detection in complex networks, ELSEVIER, 2017.##[8] C. Wu, T. Li, F. Teng and X. Chen, "An improved PSO algorithm for community detection," International Conference on Intelligent Systems and Knowledge Engineering, 2015.##[9] T. C. Y. S. Y. N. X. Z. Fan Cheng, "A Local Information based Multi-objective Evolutionary Algorithm for Community Detection in Complex Networks," Applied Soft Computing Journal, pp. 42, 2018.##[10] I. I. a. T. G.Palla, "Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society," Nature, pp. 814-818, 2015.##[11] X. L. I. Z. Faliang Huang, "Overlapping Community Detectionfor MultimediaSocial Networks," IEEE, pp. 12, 2017.##[12] J. Yang and J. Leskovec, "Community-Affiliation Graph Model for Overlapping Network Community Detection," IEEE 12th International Conference on Data Mining, pp. 14-19, 2012.##[13] J. K. R. C. Eberhart, "New optimizer using particle swarm theory," In Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39-43, 1995.##[14] Schutt.J.F, B. I. Koh, J. A. Reinbolt, B. J. Fregly, R. T. Haftka and A. D. Geotge, "Evaluation of a Particle Swarm algorithm for biornechanical," Jornal of Bionechanical engineering , pp. 465-474, 2005.##[15] C. Zhang, X. Hei, D. Yang and L. Wang, "A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks," International Journal of Pattern Recognition , vol. 30, no. 2, pp. 170-185, 2016.##[16] G.-G. W. Deb, A. H. Gandomi and A. H. AlaviSuash, "A hybrid method based on krill herd and quantum-behaved particle swarm optimization," Neural Computing and Applications, 2016.##[17] Y.-C. L. ,. S. R. Raheel Ahmad, "A Multi-Agent Based Approach for Particle SwarmOptimization," International Conference on Integration of Knowledge Intensive Multi-Agent, pp. 267-271, 2007.##[18] M. Vasile and L. Ricciardi, "Multi Agent Collaborative Search," Springer International Publishing Switzerland, 2017.##[19] R. I. S Ismail, "Modularity approach for community detection in complex networks," ACM IMCOM '17 Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, 2017.##[20] M. e. a. Shahmoradi, "Multilayer overlapping community detection using multi-objective optimization," Future Generation Computer Systems, 2019.##[21] "A Mixed Representation-Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection," IEEE TRANSACTIONS ON CYBERNETICS, vol. 47, no. 9, pp. 2703 - 2716, 13 June 2017.##[22] Gong, M., et al.," Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition", IEEE Transactions on evolutionary computation, 2013. No, 18(1), pp. 82-97.##[1] M. G. a. M. E. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, pp. 7821-7826, 2002.##[2] Bajec and L. Š. M, "Group detection in complex networks: An algorithm and comparison of the state of the art," Physica A: Statistical Mechanics and its Applications, pp. 144-156, 2014.##[3] S. Fortunato, "Community detection in graphs," Physics Report, pp. 75-174, 2010.##[4] Newman, "Finding and evaluating community structure in networks," Physical, 2004.##[5] B. J. J. L. e. a. Shang R, "Community detection based on modularity and an improved genetic algorithm," Physica A:Statistical Mechanics and its Applications, pp. 1215-1231, 2013.##[6] Y. B. L. J. e. a. Jin D, "Ant colony optimization based on random walk for community detection in complex networks," Journal of Software, pp. 451-464, 2012.##[7] A. A. P. M. Shadi Rahimi, A multi-objective particle swarm optimization algorithm for community detection in complex networks, ELSEVIER, 2017.##[8] C. Wu, T. Li, F. Teng and X. Chen, "An improved PSO algorithm for community detection," International Conference on Intelligent Systems and Knowledge Engineering, 2015.##[9] T. C. Y. S. Y. N. X. Z. Fan Cheng, "A Local Information based Multi-objective Evolutionary Algorithm for Community Detection in Complex Networks," Applied Soft Computing Journal, pp. 42, 2018.##[10] I. I. a. T. G.Palla, "Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society," Nature, pp. 814-818, 2015.##[11] X. L. I. Z. Faliang Huang, "Overlapping Community Detectionfor MultimediaSocial Networks," IEEE, pp. 12, 2017.##[12] J. Yang and J. Leskovec, "Community-Affiliation Graph Model for Overlapping Network Community Detection," IEEE 12th International Conference on Data Mining, pp. 14-19, 2012.##[13] J. K. R. C. Eberhart, "New optimizer using particle swarm theory," In Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39-43, 1995.##[14] Schutt.J.F, B. I. Koh, J. A. Reinbolt, B. J. Fregly, R. T. Haftka and A. D. Geotge, "Evaluation of a Particle Swarm algorithm for biornechanical," Jornal of Bionechanical engineering , pp. 465-474, 2005.##[15] C. Zhang, X. Hei, D. Yang and L. Wang, "A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks," International Journal of Pattern Recognition , vol. 30, no. 2, pp. 170-185, 2016.##[16] G.-G. W. Deb, A. H. Gandomi and A. H. AlaviSuash, "A hybrid method based on krill herd and quantum-behaved particle swarm optimization," Neural Computing and Applications, 2016.##[17] Y.-C. L. ,. S. R. Raheel Ahmad, "A Multi-Agent Based Approach for Particle SwarmOptimization," International Conference on Integration of Knowledge Intensive Multi-Agent, pp. 267-271, 2007.##[18] M. Vasile and L. Ricciardi, "Multi Agent Collaborative Search," Springer International Publishing Switzerland, 2017.##[19] R. I. S Ismail, "Modularity approach for community detection in complex networks," ACM IMCOM '17 Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, 2017.##[20] M. e. a. Shahmoradi, "Multilayer overlapping community detection using multi-objective optimization," Future Generation Computer Systems, 2019.##[21] "A Mixed Representation-Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection," IEEE TRANSACTIONS ON CYBERNETICS, vol. 47, no. 9, pp. 2703 - 2716, 13 June 2017.##[22] Gong, M., et al.," Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition", IEEE Transactions on evolutionary computation, 2013. No, 18(1), pp. 82-97.## ##

کنترل‌گر عصبی‎-اسمیت پیش‌بین برای بهبود عملکرد سامانه‌های کنترل از طریق شبکه Neural-Smith Predictor Method for Improvement of Networked Control Systems 1 1 کنترل‌گر اسمیت پیش‌بین یک راه‌حل مناسب در برابر چالش تأخیر تصادفی بسته‌های داده در سامانه‏‌های کنترل از طریق شبکه است. مدل‏‌سازی دقیق و برخط دستگاه به ویژه در حالتی که دستگاه غیرخطی است و یا دارای پارامترهای مجهول و تغییرپذیر است، می‌‏تواند عملکرد کنترلی سامانۀ کنترل از طریق شبکه را به مقدار قابل‌توجهی بهبود بخشد. در این پژوهش، کنترل‏‌گری با نام”کنترل‌گر عصبی‌اسمیت پیش‌‏بین“ ارائه شده که در آن با استفاده از شبکه‌‏های عصبی پرسپترون به مدل‏‌سازی برخط دستگاه پرداخته و از یک شبکۀ عصبی دیگر به‌عنوان مرکز پردازش سیگنال کنترل‌گر استفاده شده است. با استفاده از کنترل‌گر پیشنهادی، تغییرات پارامترهای دستگاه در اثر کارکرد در طول زمان، به‌صورت برخط مدل‌سازی می‌‏شود و سیگنال کنترلی مناسب تولید می‌‏شود. نتایج شبیه‌‏سازی نشان می‌دهد در سامانۀ کنترل از طریق شبکه، در حالتی که تأخیر تصادفی شبکه و تغییرات تابع تبدیل دستگاه افزایش می‌یابد، استفاده از کنترلگر عصبی- اسمیت پیش‌‏بین نسبت به کنترل‌گر اسمیت پیش‌بین ساده، عملکرد بهتری دارد. به‌عنوان مثال وقتی که تأخیر تصادفی شبکه در بازۀ ]21-19[ میلیثانیه باشد، تفاوت مقدار ITAE سامانۀ پیشنهادی با اسمیت پیش‌‏بین ساده برابر با 0004/0 است، اما به‌ازای تأخیر شبکه در بازۀ ]930-910[ میلی‌‏ثانیه این تفاوت درحدود 027/0 است. 2 Networked control systems (NCSs) are distributed control systems in which the nodes, including controllers, sensors, actuators, and plants are connected by a digital communication network such as the Internet. One of the most critical challenges in networked control systems is the stochastic time delay of arriving data packets in the communication network among the nodes. Using the Smith predictor as the controller is a common solution to overcome network time delay. Online and accurate modeling of the plant improves the performance of the networked control system, especially when the plant is nonlinear and has unknown parameters and time-variant behavior. In this paper, a novel controller, Neural-Smith predictor, is proposed, which firstly models plant using a perceptron neural network and secondly, another neural network is used as the core of signal processing of the controller. The parameters variation of the plant during time is considered online by the controller, and then the desired control signal is generated. The Integral of Time multiplied by the Absolut value of Error (ITAE) is a proper performance index for position control, so this index has been used to compare the results. Results of simulations show that NCS using the Neural-Smith predictor has better performance in comparison to the common Smith predictor and the novel compensation method using a modified communication disturbance observer (MCDOB) when the values of network time delay and variation of plant’s transfer function are excessive. For example, while the range of stochastic time delay is between 19 and 21 ms, the difference between the ITAE of controllers is 0.0004. This value increases to 0.027, while the range of stochastic time delay is between 910 and 930 ms. 86 75 2019/03/32018/11/132019/06/132018/04/222019/02/62018/05/13 1397/2/23 2019/11/102020/08/182020/09/232021/02/272020/01/222021/03/1 1399/12/11 بنیامین حق نیاز جهرمی Benyamin Haghniaz Jahromi دانشگاه یزد ایران b.haghniaz@stu.yazd.ac.ir سید محمد تقی المدرسی Seyed Mohammad Taghi AlModarresi دانشگاه یزد ایران smta@yazd.ac.ir پویا حاجبی Pooya Hajebi دانشگاه یزد ایران Hajebi@stu.yazd.ac.ir Networked Control Systems Neural Networks Neural-Smith predictor Online System Modeling Smith predictor Stochastic Time Delay تأخیر تصادفی شبکه سامانۀ کنترل از طریق شبکه شبکه‌های عصبی کنترل‌گر اسمیت پیش‌بین ساده کنترل‌گر عصبی-اسمیت پیش‌‌بین مدل‌سازی برخط. [1] F. Du, W. Du, "Networked control systems with RBF neural network control and new Smith predictor," 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, 2009, pp. 2744-2748.##[2] W . Du, F. Du, "New Smith Predictor and FRBF Neural Network Control for Networked Control Systems," Eighth IEEE/ACIS International Conference on Computer and Information Science, Shanghai, 2009, pp. 210-215.##[3] M. Mahmoud, M. Hamdan, "Fundamental Issues in Networked Control Systems," IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, 2018, pp. 902-922.##[4] C. L. Lai, P. L Hsu, "Design the Remote Control System with the Time-delay Estimator and the Adaptive Smith Predictor," IEEE Transactions on Industrial Informatics, vol. 6, no. 1, 2010, pp. 73-80.##[5] A. C. Meruelo, D. M. Simpson, S. M. Veres, P. L. Newland, "Improved System Identification Using Artificial Neural Networks and Analysis of Individual Differences in Responses of an Identified Neuron," Neural Networks, vol. 75, 2016, pp. 56-65,##[6] Z. Tian, S. Li, Y. Wang, X. Wang, Q. Zhang, "The Time-delay Compensation Method for Networked Control System Based on Improved Fast Implicit GPC," International Journal of Control and Automation, vol. 9, no. 1, 2016, pp. 231-240.##[7] F. Y. Wang, D. Liu, Networked Control Systems: Theory and Applications, Springer Publishing, 2008.##[8] Z. Xian-Ming, H. Qing-Long, Y. Xinghuo, "Survey on Recent Advances in Networked Control Systems," IEEE Transactions on Industrial Informatics, vol. 12, 2016, pp. 1740-1752.##[9] H.-C. Yi, H.-W. Kim, J.-Y Choi, "Design of Networked Control System with Discrete-time State Predictor over WSN," Journal of Advances in Computer Networks, vol. 2, no. 2, 2014, pp. 106-109.##[10] K.-E. You, L.-H. Xie, "Survey of Recent Progress in Networked Control Systems," Acta Automatica Sinica, vol. 39, no. 2, 2013, pp. 101-117.##[11] L. Zhang, H. Gao, O. Kaynak, "Network-induced Constraints in Networked Control Systems-A Survey," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, 2013, pp. 403-416.##[12] T. Yamanaka, K. Yamada, R. Hotchi and R. Kubo, "Simultaneous Time-Delay and Data-Loss Compensation for Networked Control Systems With Energy-Efficient Network Interfaces," in IEEE Access, vol. 8, 2020, pp. 110082-110092.##[13] P. Hajebi, S.M.T. AlModarresi, "Improvement of Networked Control Systems Performance Using Rotation in Fuzzy Logic Controller Rules." Journal of Signal and Data Processing (JSDP), vol. 11, no. 2, 2015, pp. 31- 42.##[1] F. Du, W. Du, "Networked control systems with RBF neural network control and new Smith predictor," 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, 2009, pp. 2744-2748.##[2] W . Du, F. Du, "New Smith Predictor and FRBF Neural Network Control for Networked Control Systems," Eighth IEEE/ACIS International Conference on Computer and Information Science, Shanghai, 2009, pp. 210-215.##[3] M. Mahmoud, M. Hamdan, "Fundamental Issues in Networked Control Systems," IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, 2018, pp. 902-922.##[4] C. L. Lai, P. L Hsu, "Design the Remote Control System with the Time-delay Estimator and the Adaptive Smith Predictor," IEEE Transactions on Industrial Informatics, vol. 6, no. 1, 2010, pp. 73-80.##[5] A. C. Meruelo, D. M. Simpson, S. M. Veres, P. L. Newland, "Improved System Identification Using Artificial Neural Networks and Analysis of Individual Differences in Responses of an Identified Neuron," Neural Networks, vol. 75, 2016, pp. 56-65,##[6] Z. Tian, S. Li, Y. Wang, X. Wang, Q. Zhang, "The Time-delay Compensation Method for Networked Control System Based on Improved Fast Implicit GPC," International Journal of Control and Automation, vol. 9, no. 1, 2016, pp. 231-240.##[7] F. Y. Wang, D. Liu, Networked Control Systems: Theory and Applications, Springer Publishing, 2008.##[8] Z. Xian-Ming, H. Qing-Long, Y. Xinghuo, "Survey on Recent Advances in Networked Control Systems," IEEE Transactions on Industrial Informatics, vol. 12, 2016, pp. 1740-1752.##[9] H.-C. Yi, H.-W. Kim, J.-Y Choi, "Design of Networked Control System with Discrete-time State Predictor over WSN," Journal of Advances in Computer Networks, vol. 2, no. 2, 2014, pp. 106-109.##[10] K.-E. You, L.-H. Xie, "Survey of Recent Progress in Networked Control Systems," Acta Automatica Sinica, vol. 39, no. 2, 2013, pp. 101-117.##[11] L. Zhang, H. Gao, O. Kaynak, "Network-induced Constraints in Networked Control Systems-A Survey," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, 2013, pp. 403-416.##[12] T. Yamanaka, K. Yamada, R. Hotchi and R. Kubo, "Simultaneous Time-Delay and Data-Loss Compensation for Networked Control Systems With Energy-Efficient Network Interfaces," in IEEE Access, vol. 8, 2020, pp. 110082-110092.##[13]حاجبی پویا، المدرسی سید محمد تقی، "بهبود عملکرد سامانه‏های کنترل از طریق شبکه با استفاده از چرخش در قوانین کنترلگر منطق فازی "، پردازش علائم و داده‌ها، شماره ۱۱ (۲) ، صفحات ۳۱-۴۲، ۱۳۹۳.##[13] P. Hajebi, S.M.T. AlModarresi, "Improvement of Networked Control Systems Performance Using Rotation in Fuzzy Logic Controller Rules." Journal of Signal and Data Processing (JSDP), vol. 11, no. 2, 2015, pp. 31- 42.## ##

طبقه‌بندی تصاویر پلاریمتری رادار مبتنی بر ماشین بردار پشتیبان و الگوریتم جستجوی گرانشی دودویی Classification of polarimetric radar images based on SVM and BGSA 1 1 هدف از این پژوهش ارائه یک روش بهینه بهه‌منظور طبقه‌بندی تصاویر رادار پلاریمتری است. روش پیشنهادی تلفیقی از ماشین بردار پشتیبان و الگوریتم بهینه‌سازی جستجوی گرانشی دودویی است. در این راستا، ابتدا مجموعه‌ای از ویژگی‌های پلاریمتریک شامل مقادیر داده اصلی، ویژگی‌های تجزیه هدف و تفکیک‌کننده‌های SAR از تصاویر استخراج می‌شوند؛ سپس به‌منظور انتخاب ویژگی‌های مناسب و تعیین پارامترهای بهینه برای طبقه‌بندی‌کننده ماشین بردار پشتیبان از الگوریتم جستجوی گرانشی دودویی استفاده شده است. به‌منظور دست‌یابی به یک سامانه طبقه‌بندی با دقت طبقه‌بندی بالا، انتخاب مقادیر بهینه پارامترهای مدل و زیرمجموعه‌ای از ویژگی‌های بهینه، به‌طور هم‌زمان انجام می‌پذیرد. نتایج پیاده‌سازی الگوریتم پیشنهادی با دو حالت، در‌نظر‌گرفتن تمام ویژگی‌های انتخاب‌شده، و الگوریتم ژنتیک، قیاس شده که نتایج حاصل از تفکیک نواحی برای سه ناحیه مورد بررسی قرار گرفته است. تفکیک نواحی برای مناطق سانفرانسیسکو و مانیل، و تشخیص لکه نفتی سطح اقیانوس منطقه فیلیپین مورد ارزیابی قرار گرفته که به‌ترتیب با بهبود دقت کلی تقریبی 12، 7 و 5/6 درصد در قیاس با الگوریتم ژنتیک بهبود داشته است. 2 Classification of land cover is one of the most important applications of radar polarimetry images. The purpose of image classification is to classify image pixels into different classes based on vector properties of the extractor. Radar imaging systems provide useful information about ground cover by using a wide range of electromagnetic waves to image the Earth's surface. The purpose of this study is to present an optimal method for classifying polarimetric radar images. The proposed method is a combination of support vector machine and binary gravitational search optimization algorithm. In this regard, first a set of polarimetric features including original data values, target parsing features, and SAR separators are extracted from the images. Then, in order to select the appropriate features and determine the optimal parameters for the support vector machine classifier, the binary gravitational search algorithm is used. In order to achieve a classification system with high classification accuracy, the optimal values of the model parameters and a subset of the optimal properties are selected simultaneously. The results of the implementation of the proposed algorithm are compared with two states, taking into account all the selected features, and the genetic algorithm, the results of zoning for the three regions are examined. The separation of areas for the San Francisco and Manila regions, and the detection of oil slicks in the ocean surface of the Philippines, have been evaluated. The comparison with the genetic algorithm was approximately between 6% to 12% and the comparison with the presence of all features was between 13% and 20%. For the San Francisco area, the number of extraction properties was 101, which was selected using the proposed 47 optimal properties algorithm. For the city of Manila, after applying the algorithm, 31 optimal features have been selected from 65 features. For the oil slick of the city of the Philippines, we have reached the stated accuracy by selecting 33 features from 69 features, for the first two regions the number of initial population is 50 and the repetition period is 30, and for the third region with 30 initial population and the repetition period is 10. 102 87 2019/03/32018/11/132019/06/132018/04/222019/02/62018/05/132018/09/3 1397/6/12 2019/11/102020/08/182020/09/232021/02/272020/01/222021/03/12021/02/24 1399/12/6 یاسر رضائی yaser rezaei دانشکده علوم و فنون نوین، دانشگاه تهران ایران rezaei.yaser@ut.ac.ir علیرضا رضائی alirezae rezaee گروه مهندسی سیستم و مکاترونیک دانشکده علوم و فنون نوین، دانشگاه تهران ایران arezaee@ut.ac.ir فاطمه درکه fateme darakeh پژوهشکده برق و فناوری اطلاعات ایران f_darake@yahoo.com زینب آذرخش zeynab azarakhsh دانشکده علوم زمین، دانشگاه شهید بهشتی ایران zi.azarakhsh@Gmail.com Feature Selection Image classification support vector machine optimization binary gravitational search algorithm polarimetric radar انتخاب ویژگی طبقه‌بندی تصویر ماشین بردار پشتیبان بهینه‌سازی الگوریتم جستجوی گرانشی دودویی پلاریمتری رادار [1] J.-S. Lee and E. Pottier, Polarimetric radar imaging: from basics to applications: CRC press, 2009.##[2] V. Alberga, D. Staykova, E. Krogager, A. Danklmayer, and M. Chandra, "Comparison of methods for extracting and utilizing radar target characteristic parameters," in Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS'05., 2005, pp. 2019-2021.##[3] J. L. Alvarez-Perez, "Coherence, polarization, and statistical independence in Cloude-Pottier's radar polarimetry," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 426-441, 2011.##[4] J.-S. Lee and E. Pottier, Polarimetric radar imaging: from basics to applications: CRC press, 2017.##[5] J. Van Zyl and C. Burnette, "Bayesian classification of polarimetric SAR images using adaptive a priori probabilities," International Journal of Remote Sensing, vol. 13, pp. 835-840, 1992.##[6] J.-S. Lee, M. R. Grunes, and R. Kwok, "Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution," International Journal of Remote Sensing, vol. 15, pp. 2299-2311, 1994.##[7] S. R. Cloude and E. Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Transactions on Geoscience and Remote Sensing, vol. 35, pp. 68-78, 1997.##[8] Y. Maghsoudi, "Analysis of Radarsat-2 full polarimetric data for forest mapping," Degree of PhD, Department of Geomatics Engineering, University of Calgary, 2011.##[9] E. Rignot and R. Chellappa, "Segmentation of polarimetric synthetic aperture radar data," IEEE Transactions on Image Processing, vol. 1, pp. 281-300, 1992.##[10] W. An, Y. Cui, and J. Yang, "Three-component model-based decomposition for polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, pp. 2732-2739, 2010.##[11] J. Kong, A. Swartz, H. Yueh, L. Novak, and R. Shin, "Identification of terrain cover using the optimum polarimetric classifier," Journal of Electromagnetic Waves and Applications, vol. 2, pp. 171-194, 1988.##[12] L. Ferro-Famil, E. Pottier, and J.-S. Lee, "Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 2332-2342, 2001.##[13] T. Moriyama, S. Uratsuka, T. Umehara, M. Satake, A. Nadai, H. Maeno, et al., "A study on extraction of urban areas from polarimetric synthetic aperture radar image," in Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International, 2004.##[14] C.-T. Chen, K.-S. Chen, and J.-S. Lee, "The use of fully polarimetric information for the fuzzy neural classification of SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 2089-2100, 2003.##[15] C. Lardeux, P.-L. Frison, J.-P. Rudant, J.-C. Souyris, C. Tison, and B. Stoll, "Use of the SVM classification with polarimetric SAR data for land use cartography," in 2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006, pp. 493-496.##[16] C. Lardeux, P.-L. Frison, C. Tison, J.-C. Souyris, B. Stoll, B. Fruneau, et al., "Support vector machine for multifrequency SAR polarimetric data classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 4143-4152, 2009.##[17] Y. Maghsoudi, M. Collins, and D. G. Leckie, "Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers," International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp. 139-150, 2012.##[18] A. Haddadi G, M. Reza Sahebi, and A. Mansourian, "Polarimetric SAR feature selection using a genetic algorithm," Canadian Journal of Remote Sensing, vol. 37, pp. 27-36, 2011.##[19] M. Salehi, M. R. Sahebi, and Y. Maghsoudi, "Improving the accuracy of urban land cover classification using Radarsat-2 PolSAR data," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol. 7, pp. 1394-1401, 2014.##[20] S. Sarafrazi and H. Nezamabadi-pour, "Facing the classification of binary problems with a GSA-SVM hybrid system," Mathematical and Computer Modelling, vol. 57, pp. 270-278, 2013.##[21] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, pp. 2232-2248, 2009.##[22] J. Geng, X. Ma, J. Fan, and H. Wang, "Semisupervised Classification of Polarimetric SAR Image via Superpixel Restrained Deep Neural Network," IEEE Geoscience and Remote Sensing Letters, vol. 15, pp. 122-126, 2018.##[23] X. Huang, H. Qiao, B. Zhang, and X. Nie, "Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding," IEEE Transactions on Image Processing, 2018.##[24] H. Zhou, X. Feng, Y. Zhang, E. Nilot, M. Zhang, Z. Dong, et al., "Combination of Support Vector Machine and H-Alpha Decomposition for Subsurface Target Classification of GPR," in 2018 17th International Conference on Ground Penetrating Radar (GPR), 2018, pp. 1-4.##[25] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, "Deep learning classification of land cover and crop types using remote sensing data," IEEE Geoscience and Remote Sensing Letters, vol. 14, pp. 778-782, 2017.##[26] D. Li, Y. Gu, S. Gou, and L. Jiao, "Full polarization sar image classification using deep learning with shallow feature," in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 4566-4569.##[27] M. Touafria and Q. Yang, "SAR Image Classification via Capsule Networks," in Proceedings of the 3rd International Conference on Computer Science and Application Engineering, 2019, pp. 1-5.##[28] C. Yang, B. Hou, B. Ren, Y. Hu, and L. Jiao, "CNN-based polarimetric decomposition feature selection for PolSAR image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8796-8812, 2019.##[29] A. Zhang, X. Yang, L. Jia, J. Ai, and Z. Dong, "SAR image classification using adaptive neighborhood-based convolutional neural network," European Journal of Remote Sensing, vol. 52, pp. 178-193, 2019.##[30] F. M. Bianchi, M. M. Espeseth, and N. Borch, "Large-scale detection and categorization of oil spills from SAR images with deep learning," arXiv preprint arXiv:2006.13575, 2020.##[31] H. Wang, F. Xu, and Y.-Q. Jin, "A Review of Polsar Image Classification: from Polarimetry to Deep Learning," in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 3189-3192.##[32] W. Yang, L. Jiaguo, and Z. Changyao, "Algorithm of target classification based on target decomposition and support vector machine," in Synthetic Aperture Radar, 2007. APSAR 2007. 1st Asian and Pacific Conference on, 2007, pp. 770-774.##[33] W. Zhu, D. Hou, J. Zhang, and J. Zhang, "Optimization of a subset of apple features based on modified particle swarm algorithm," in Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on, 2010, pp. 427-430.##[34] G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, pp. 247-259, 2011.##[35] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "BGSA: binary gravitational search algorithm," Natural Computing, vol. 9, pp. 727-745, 2010.##[36] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "Filter modeling using gravitational search algorithm," Engineering Applications of Artificial Intelligence, vol. 24, pp. 117-122, 2011.##[37] V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, "A model for radar images and its application to adaptive digital filtering of multiplicative noise," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 157-166, 1982.##[38] J.-S. Lee, "Digital image enhancement and noise filtering by use of local statistics," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 165-168, 1980.##[39] J.-S. Lee, "Speckle analysis and smoothing of synthetic aperture radar images," Computer graphics and image processing, vol. 17, pp. 24-32, 1981.##[40] M. A. Shahin, H. R. Maier, and M. B. Jaksa, "Data division for developing neural networks applied to geotechnical engineering," Journal of Computing in Civil Engineering, vol. 18, pp. 105-114, 2004.##[41] A. Matkan, M. Hajeb, and Z. Azarakhsh, "Oil spill detection from SAR image using SVM based classification," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, SMPR, vol. 1, p. W3, 2013.##[42] S. Singha, R. Ressel, D. Velotto, and S. Lehner, "A combination of traditional and polarimetric features for oil spill detection using TerraSAR-X," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, pp. 4979-4990, 2016.##[1] J.-S. Lee and E. Pottier, Polarimetric radar imaging: from basics to applications: CRC press, 2009.##[2] V. Alberga, D. Staykova, E. Krogager, A. Danklmayer, and M. Chandra, "Comparison of methods for extracting and utilizing radar target characteristic parameters," in Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS'05., 2005, pp. 2019-2021.##[3] J. L. Alvarez-Perez, "Coherence, polarization, and statistical independence in Cloude-Pottier's radar polarimetry," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 426-441, 2011.##[4] J.-S. Lee and E. Pottier, Polarimetric radar imaging: from basics to applications: CRC press, 2017.##[5] J. Van Zyl and C. Burnette, "Bayesian classification of polarimetric SAR images using adaptive a priori probabilities," International Journal of Remote Sensing, vol. 13, pp. 835-840, 1992.##[6] J.-S. Lee, M. R. Grunes, and R. Kwok, "Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution," International Journal of Remote Sensing, vol. 15, pp. 2299-2311, 1994.##[7] S. R. Cloude and E. Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Transactions on Geoscience and Remote Sensing, vol. 35, pp. 68-78, 1997.##[8] Y. Maghsoudi, "Analysis of Radarsat-2 full polarimetric data for forest mapping," Degree of PhD, Department of Geomatics Engineering, University of Calgary, 2011.##[9] E. Rignot and R. Chellappa, "Segmentation of polarimetric synthetic aperture radar data," IEEE Transactions on Image Processing, vol. 1, pp. 281-300, 1992.##[10] W. An, Y. Cui, and J. Yang, "Three-component model-based decomposition for polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, pp. 2732-2739, 2010.##[11] J. Kong, A. Swartz, H. Yueh, L. Novak, and R. Shin, "Identification of terrain cover using the optimum polarimetric classifier," Journal of Electromagnetic Waves and Applications, vol. 2, pp. 171-194, 1988.##[12] L. Ferro-Famil, E. Pottier, and J.-S. Lee, "Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 2332-2342, 2001.##[13] T. Moriyama, S. Uratsuka, T. Umehara, M. Satake, A. Nadai, H. Maeno, et al., "A study on extraction of urban areas from polarimetric synthetic aperture radar image," in Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International, 2004.##[14] C.-T. Chen, K.-S. Chen, and J.-S. Lee, "The use of fully polarimetric information for the fuzzy neural classification of SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 2089-2100, 2003.##[15] C. Lardeux, P.-L. Frison, J.-P. Rudant, J.-C. Souyris, C. Tison, and B. Stoll, "Use of the SVM classification with polarimetric SAR data for land use cartography," in 2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006, pp. 493-496.##[16] C. Lardeux, P.-L. Frison, C. Tison, J.-C. Souyris, B. Stoll, B. Fruneau, et al., "Support vector machine for multifrequency SAR polarimetric data classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 4143-4152, 2009.##[17] Y. Maghsoudi, M. Collins, and D. G. Leckie, "Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers," International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp. 139-150, 2012.##[18] A. Haddadi G, M. Reza Sahebi, and A. Mansourian, "Polarimetric SAR feature selection using a genetic algorithm," Canadian Journal of Remote Sensing, vol. 37, pp. 27-36, 2011.##[19] M. Salehi, M. R. Sahebi, and Y. Maghsoudi, "Improving the accuracy of urban land cover classification using Radarsat-2 PolSAR data," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol. 7, pp. 1394-1401, 2014.##[20] S. Sarafrazi and H. Nezamabadi-pour, "Facing the classification of binary problems with a GSA-SVM hybrid system," Mathematical and Computer Modelling, vol. 57, pp. 270-278, 2013.##[21] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, pp. 2232-2248, 2009.##[22] J. Geng, X. Ma, J. Fan, and H. Wang, "Semisupervised Classification of Polarimetric SAR Image via Superpixel Restrained Deep Neural Network," IEEE Geoscience and Remote Sensing Letters, vol. 15, pp. 122-126, 2018.##[23] X. Huang, H. Qiao, B. Zhang, and X. Nie, "Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding," IEEE Transactions on Image Processing, 2018.##[24] H. Zhou, X. Feng, Y. Zhang, E. Nilot, M. Zhang, Z. Dong, et al., "Combination of Support Vector Machine and H-Alpha Decomposition for Subsurface Target Classification of GPR," in 2018 17th International Conference on Ground Penetrating Radar (GPR), 2018, pp. 1-4.##[25] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, "Deep learning classification of land cover and crop types using remote sensing data," IEEE Geoscience and Remote Sensing Letters, vol. 14, pp. 778-782, 2017.##[26] D. Li, Y. Gu, S. Gou, and L. Jiao, "Full polarization sar image classification using deep learning with shallow feature," in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 4566-4569.##[27] M. Touafria and Q. Yang, "SAR Image Classification via Capsule Networks," in Proceedings of the 3rd International Conference on Computer Science and Application Engineering, 2019, pp. 1-5.##[28] C. Yang, B. Hou, B. Ren, Y. Hu, and L. Jiao, "CNN-based polarimetric decomposition feature selection for PolSAR image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8796-8812, 2019.##[29] A. Zhang, X. Yang, L. Jia, J. Ai, and Z. Dong, "SAR image classification using adaptive neighborhood-based convolutional neural network," European Journal of Remote Sensing, vol. 52, pp. 178-193, 2019.##[30] F. M. Bianchi, M. M. Espeseth, and N. Borch, "Large-scale detection and categorization of oil spills from SAR images with deep learning," arXiv preprint arXiv:2006.13575, 2020.##[31] H. Wang, F. Xu, and Y.-Q. Jin, "A Review of Polsar Image Classification: from Polarimetry to Deep Learning," in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 3189-3192.##[32] W. Yang, L. Jiaguo, and Z. Changyao, "Algorithm of target classification based on target decomposition and support vector machine," in Synthetic Aperture Radar, 2007. APSAR 2007. 1st Asian and Pacific Conference on, 2007, pp. 770-774.##[33] W. Zhu, D. Hou, J. Zhang, and J. Zhang, "Optimization of a subset of apple features based on modified particle swarm algorithm," in Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on, 2010, pp. 427-430.##[34] G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, pp. 247-259, 2011.##[35] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "BGSA: binary gravitational search algorithm," Natural Computing, vol. 9, pp. 727-745, 2010.##[36] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "Filter modeling using gravitational search algorithm," Engineering Applications of Artificial Intelligence, vol. 24, pp. 117-122, 2011.##[37] V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, "A model for radar images and its application to adaptive digital filtering of multiplicative noise," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 157-166, 1982.##[38] J.-S. Lee, "Digital image enhancement and noise filtering by use of local statistics," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 165-168, 1980.##[39] J.-S. Lee, "Speckle analysis and smoothing of synthetic aperture radar images," Computer graphics and image processing, vol. 17, pp. 24-32, 1981.##[40] M. A. Shahin, H. R. Maier, and M. B. Jaksa, "Data division for developing neural networks applied to geotechnical engineering," Journal of Computing in Civil Engineering, vol. 18, pp. 105-114, 2004.##[41] A. Matkan, M. Hajeb, and Z. Azarakhsh, "Oil spill detection from SAR image using SVM based classification," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, SMPR, vol. 1, p. W3, 2013.##[42] S. Singha, R. Ressel, D. Velotto, and S. Lehner, "A combination of traditional and polarimetric features for oil spill detection using TerraSAR-X," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, pp. 4979-4990, 2016.## ##

همگام‌سازی مجدد کلید در بستر اینترنت اشیای بُرد بلند و توان‌ پایین Key Resynchronizing in Low Power Wide Area Networks 1 1 اینترنت اشیای برد بلند و توان پایین (LPWAN) به دسته‌ای از فناوری‌های ارتباطی در اینترنت اشیا گفته می‌شود که دارای مصرف بسیار پایین و در عین حال برد ارتباطی بلند هستند. در کنار مزایای مختلف، این فناوری‌ها محدودیت‌های بسیاری نیز از جمله پهنای باند کم، ارسال بدون اتصال و قدرت پردازشی پایین دارند که روش‌های رمزنگاری در این بستر را دچار چالش کرده است. یکی از مهم‌ترین این چالش‌ها، زنجیره‌سازی رمز در این بستر است. حجم بسیار کوچک پیام و احتمال از‌دست‌رفتن بسته بدون اطلاع دروازه و دستگاه، باعث می‌شود هیچ از یک روش‌های متداول زنجیره‌سازی رمز مانند CBC، OFB و یا CTC در بستر اینترنت اشیا توان پایین امکان‌پذیر نباشد؛ چون هر یک از این روش‌ها یا باید بر روی یک بستر اتصال‌گرا باشد و یا بخشی از حجم بسته ارسالی را با روش‌های مانند ارسال شمارنده و یا HMAC مصرف کند. در این مقاله، روشی جدیدی جهت همگام‌سازی مجدد فرستنده و گیرنده در صورت از دست رفتن یک بسته ارائه می‌شود که به وسیله آن قادر خواهیم بود در محدویت‌های LPWAN، رمزنگاری را در حالت زنجیره‌ای انجام داد. روش پیشنهادی قادر است بدون استفاده از فضای پیام ارسالی، همگام‌سازی طرفین را انجام دهد. نتایج شبیه‌سازی بیان‌گر این است که روش پیشنهادی در محیط‌های که احتمال از‌دست‌رفتن چند بسته پشت سر هم، پایین است، کارآیی قابل قبولی دارد. 2 LPWANs are a class of technologies that have very low power consumption and high range of communication. Along with its various advantages, these technologies also have many limitations, such as low bandwidth, connectionless transmission and low processing power, which has challenged encryption methods in this technologies. One of the most important of these challenges is encryption. The very small size of the message and the possibility of packet loss without the gateway or device awareness, make any of the cipher chaining methods such as CBC, OFB or CTC impossible in LPWANs, because either they assume a connection oriented media or consume part of the payload for sending counter or HMAC. In this paper, we propose a new way to re-synchronize the key between sender and receiver in the event of a packet being lost that will enable us to perform cipher chaining encryption in LPWAN limitation. The paper provides two encryption synchronization methods for LPWANs. The first method can be synchronized in a similar behavior as the proof of work in the block chain. The second proposed method is able to synchronize the sender and receiver with the least possible used space of the message payload. The proposed method is able to synchronize the parties without using the payload. The proposed method is implemented in the Sigfox platform and then simulated in a sample application. The simulation results show that the proposed method is acceptable in environments where the probability of missing several consecutive packets is low. 118 103 2019/03/32018/11/132019/06/132018/04/222019/02/62018/05/132018/09/32019/01/19 1397/10/29 2019/11/102020/08/182020/09/232021/02/272020/01/222021/03/12021/02/242020/08/18 1399/5/28 امیر جلالی بیدگلی Amir Jalaly Bidgoly دانشگاه قم ایران jalaly@qom.ac.ir عباس دهقانی Abbas Dehghani دانشگاه یاسوج ایران Dehghani@yu.ac.ir LPWAN Encryption Key Re-Synchronization Hashing اینترنت اشیای توان پایین و دوربرد رمزنگاری همگام‌سازی کلید چکیده‌سازی [1] S. Barcelona. (Accessed 2015). Gartner Says 4.9 Billion Connected "Things" Will Be in Use in 2015. Available: http://www.gartner.com/newsroom/id/2905717##[2] C. STAMFORD, "Gartner Says By 2020, More Than Half of Major New Business Processes and Systems Will Incorporate Some Element of the Internet of Things," Accessed 2017 2016.##[3] L. ADLER. (2016). How Smart City Barcelona Brought the Internet of Things to Life. Available: http://datasmart.ash.harvard.edu/news/article/how-smart-city-barcelona-brought-the-internet-of-things-to-life-789##[4] H. Akhavan, M Kashani, S. Ehsani, M. Khoshbakhtian, N. Abdi (2017). Communication Networks in Internet of Things. Availabe: https://iot.itrc.ac.ir/content.##[5] Framework of requirements and expectations of the smart gas meter pilot project for home and commercial subscribers, 2016.##[6] Parsnet, IoT infrastructure. Available: www.parsnet.io/خانه/راهکارها-و-خدمات/نحوه-خدمات/،##[7] A. Bogdanov, L. R. Knudsen, G. Leander, C. Paar, A. Poschmann, M. J. Robshaw, Y. Seurin, C. Vikkelsoe, Present: An ultra-lightweight block cipher, in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2007, pp. 450-466.##[8] D. J. Wheeler, R. M. Needham, Tea, a tiny encryption algorithm, in: International Workshop on Fast Software Encryption, Springer, 1994, pp. 363-366.##[9] R. L. Rivest, "The rc5 encryption algorithm", in: International Workshop on Fast Software Encryption, Springer, 1994, pp. 86-96.##[10] T. K. Goyal, V. Sahula, "Lightweight security algorithm for low power iot devices", in: Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on, IEEE, 2016, pp. 1725-1729.##[11] A. P. J. Guo, T. Peyrin, M. J. B. Robshaw, The led block cipher 6917 ,2011,pp. 326-341.##[12] S. S. M. AlDabbagh, A. Shaikhli, I. F. Taha, M. A. Alahmad, "Hisec: A new lightweight block cipher algorithm", in: Proceedings of the 7th International Conference on Security of Information and Networks, ACM, vol.940, 2014, pp. 151.##[13] D. Hong, J. Sung, S. Hong, J. Lim, S. Lee, B.-S. Koo, C. Lee, D. Chang, J. Lee, K. Jeong, et al., "Hight: A new block cipher suitable for lowresource device," in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2006, pp. 46-59.##[14] S. S. M. Aldabbagh, I. F. T. Al Shaikhli, "Olbca: A new lightweight block cipher algorithm," in: 2014 3rd International Conference on Advanced Computer Science Applications and Technologies (ACSAT), IEEE, 2014, pp. 15-20.##[15] S. S. M. AlDabbagh, Design 32-bit lightweight block cipher algorithm (dlbca), International Journal of Computer Applications 166 (8).##[16] T. Suzaki, K. Minematsu, S. Morioka, E. Kobayashi, Twine: A lightweight, versatile block cipher, in: ECRYPTWorkshop on Lightweight Cryptography, Vol. 2011, 2011.##[17] J. Borgho, A. Canteaut, T. Guneysu, E. B. Kavun, M. Knezevic, L. R. Knudsen, G. Leander, V. Nikov, C. Paar, C. Rechberger, et al., "Prince-a low-latency block cipher for pervasive computing applications", in: International Conference on the Theory and Application of Cryptology and Information Security, Springer, 2012, pp. 208-225.##[18] L. Knudsen, G. Leander, A. Poschmann, M. J. "Robshaw, Printcipher: a block cipher for ic-printing," in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2010, pp. 16-32.##[19] W. Wu, L. Zhang, Lblock: "a lightweight block cipher", International Conference on Applied Cryptography and Network Security, Springer, 2011, pp. 327-344.##[20] Z. Gong, S. Nikova, Y. W. Law, Klein, "a new family of lightweight block ciphers", International Workshop on Radio Frequency Identication: Security and Privacy Issues, Springer, 2011, pp. 1-18.##[21] R. Beaulieu, D. Shors, J. Smith, S. Treatman-Clark, B. Weeks, and L. Wingers, "SIMON and SPECK: Block Ciphers for the Internet of Things," IACR Cryptology ePrint Archive, vol. 2015, pp. 585, 2015.##[22] T. K. Goyal and V. Sahula, "Lightweight security algorithm for low power IoT devices," in Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on, 2016, pp. 1725-1729: IEEE.##[23] J. Kim, J. Song, A dual key-based activation scheme for secure lorawan, Wireless Communications and Mobile Computing 2017.##[24] S. Naoui, M. E. Elhdhili, L. A. Saidane, "Enhancing the security of the iot lorawan architecture, Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)", International Conference on, IEEE 1020, 2016, pp. 1-7.##[25] K. Feichtinger, Y. Nakano, K. Fukushima, S. Kiyomoto, "Enhancing the security of over-the-air-activation of lorawan using a hybrid cryptosystem", INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, vol. 18 (2), 2018, pp.1-9.##[26] K.-L. Tsai, Y.-L. Huang, F.-Y. Leu, I. You, Y.-L. Huang, C.-H. Tsai, Aes-1030 128 based secure low power communication for lorawan iot environments, IEEE Access 6 (2018) 45325-45334.##[27] A. K. Luhach, "Analysis of lightweight cryptographic solutions for Internet of Things," Indian Journal of Science and Technology, vol. 9, no. 28, 2016.##[28] E. Rescorla and N. Modadugu, "Datagram transport layer security version 1.2," 2012.##[29] A. L. Wilson, "Encryption synchronization combined with encryption key identification," ed: Google Patents, 1993.##[30] P. Epstein, "Key distribution system," ed: Google Patents, 1996.##[31] B. Tehranchi, "Encryption apparatus and method for synchronizing multiple encryption keys with a data stream," ed: Google Patents, 2007.##[32] S. B. Mizikovsky and M. A. Soler, "Automatic resynchronization of crypto-sync information," ed: Google Patents, 2004.##[33] K. Akhavan-Toyserkani and M. Beeler, "Method and system for self synchronizing cryptographic parameters," ed: Google Patents, 2014.##[34] M. Briceno, I. Goldberg, D. Wagner, A pedagogical implementation of A5/1, http://www.scard.org, May 1999.##[1] S. Barcelona. (Accessed 2015). Gartner Says 4.9 Billion Connected "Things" Will Be in Use in 2015. Available: http://www.gartner.com/newsroom/id/2905717##[2] C. STAMFORD, "Gartner Says By 2020, More Than Half of Major New Business Processes and Systems Will Incorporate Some Element of the Internet of Things," Accessed 2017 2016.##[3] L. ADLER. (2016). How Smart City Barcelona Brought the Internet of Things to Life. Available: http://datasmart.ash.harvard.edu/news/article/how-smart-city-barcelona-brought-the-internet-of-things-to-life-789##[4] ح. اخوان, م. م. کاشانی, س. ر. احسانی, م. خوشبختیان, and ن. عبدی. (1396). شبکه ارتباطی در اینترنت اشیاء. Available: https://iot.itrc.ac.ir/content/شبکه-ارتباطی-در-اینترنت-اشیاء##[4] H. Akhavan, M Kashani, S. Ehsani, M. Khoshbakhtian, N. Abdi (2017). Communication Networks in Internet of Things. Availabe: https://iot.itrc.ac.ir/content.##[5] ش. م. گ. ایران, "چارچوب الزامات و انتظارات طرح پایلوت کنتورخوانی هوشمند گاز برای مشترکین خانگی و تجاری جزء," 1395.##[5] Framework of requirements and expectations of the smart gas meter pilot project for home and commercial subscribers, 2016.##[6] شرکت پارس نت، زیرساخت اینترنت اشیاء. آدرس: www.parsnet.io/خانه/راهکارها-و-خدمات/نحوه-خدمات/،##[6] Parsnet, IoT infrastructure. Available: www.parsnet.io/خانه/راهکارها-و-خدمات/نحوه-خدمات/،##[7] A. Bogdanov, L. R. Knudsen, G. Leander, C. Paar, A. Poschmann, M. J. Robshaw, Y. Seurin, C. Vikkelsoe, Present: An ultra-lightweight block cipher, in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2007, pp. 450-466.##[8] D. J. Wheeler, R. M. Needham, Tea, a tiny encryption algorithm, in: International Workshop on Fast Software Encryption, Springer, 1994, pp. 363-366.##[9] R. L. Rivest, "The rc5 encryption algorithm", in: International Workshop on Fast Software Encryption, Springer, 1994, pp. 86-96.##[10] T. K. Goyal, V. Sahula, "Lightweight security algorithm for low power iot devices", in: Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on, IEEE, 2016, pp. 1725-1729.##[11] A. P. J. Guo, T. Peyrin, M. J. B. Robshaw, The led block cipher 6917 ,2011,pp. 326-341.##[12] S. S. M. AlDabbagh, A. Shaikhli, I. F. Taha, M. A. Alahmad, "Hisec: A new lightweight block cipher algorithm", in: Proceedings of the 7th International Conference on Security of Information and Networks, ACM, vol.940, 2014, pp. 151.##[13] D. Hong, J. Sung, S. Hong, J. Lim, S. Lee, B.-S. Koo, C. Lee, D. Chang, J. Lee, K. Jeong, et al., "Hight: A new block cipher suitable for lowresource device," in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2006, pp. 46-59.##[14] S. S. M. Aldabbagh, I. F. T. Al Shaikhli, "Olbca: A new lightweight block cipher algorithm," in: 2014 3rd International Conference on Advanced Computer Science Applications and Technologies (ACSAT), IEEE, 2014, pp. 15-20.##[15] S. S. M. AlDabbagh, Design 32-bit lightweight block cipher algorithm (dlbca), International Journal of Computer Applications 166 (8).##[16] T. Suzaki, K. Minematsu, S. Morioka, E. Kobayashi, Twine: A lightweight, versatile block cipher, in: ECRYPTWorkshop on Lightweight Cryptography, Vol. 2011, 2011.##[17] J. Borgho, A. Canteaut, T. Guneysu, E. B. Kavun, M. Knezevic, L. R. Knudsen, G. Leander, V. Nikov, C. Paar, C. Rechberger, et al., "Prince-a low-latency block cipher for pervasive computing applications", in: International Conference on the Theory and Application of Cryptology and Information Security, Springer, 2012, pp. 208-225.##[18] L. Knudsen, G. Leander, A. Poschmann, M. J. "Robshaw, Printcipher: a block cipher for ic-printing," in: International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2010, pp. 16-32.##[19] W. Wu, L. Zhang, Lblock: "a lightweight block cipher", International Conference on Applied Cryptography and Network Security, Springer, 2011, pp. 327-344.##[20] Z. Gong, S. Nikova, Y. W. Law, Klein, "a new family of lightweight block ciphers", International Workshop on Radio Frequency Identication: Security and Privacy Issues, Springer, 2011, pp. 1-18.##[21] R. Beaulieu, D. Shors, J. Smith, S. Treatman-Clark, B. Weeks, and L. Wingers, "SIMON and SPECK: Block Ciphers for the Internet of Things," IACR Cryptology ePrint Archive, vol. 2015, pp. 585, 2015.##[22] T. K. Goyal and V. Sahula, "Lightweight security algorithm for low power IoT devices," in Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on, 2016, pp. 1725-1729: IEEE.##[23] J. Kim, J. Song, A dual key-based activation scheme for secure lorawan, Wireless Communications and Mobile Computing 2017.##[24] S. Naoui, M. E. Elhdhili, L. A. Saidane, "Enhancing the security of the iot lorawan architecture, Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)", International Conference on, IEEE 1020, 2016, pp. 1-7.##[25] K. Feichtinger, Y. Nakano, K. Fukushima, S. Kiyomoto, "Enhancing the security of over-the-air-activation of lorawan using a hybrid cryptosystem", INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, vol. 18 (2), 2018, pp.1-9.##[26] K.-L. Tsai, Y.-L. Huang, F.-Y. Leu, I. You, Y.-L. Huang, C.-H. Tsai, Aes-1030 128 based secure low power communication for lorawan iot environments, IEEE Access 6 (2018) 45325-45334.##[27] A. K. Luhach, "Analysis of lightweight cryptographic solutions for Internet of Things," Indian Journal of Science and Technology, vol. 9, no. 28, 2016.##[28] E. Rescorla and N. Modadugu, "Datagram transport layer security version 1.2," 2012.##[29] A. L. Wilson, "Encryption synchronization combined with encryption key identification," ed: Google Patents, 1993.##[30] P. Epstein, "Key distribution system," ed: Google Patents, 1996.##[31] B. Tehranchi, "Encryption apparatus and method for synchronizing multiple encryption keys with a data stream," ed: Google Patents, 2007.##[32] S. B. Mizikovsky and M. A. Soler, "Automatic resynchronization of crypto-sync information," ed: Google Patents, 2004.##[33] K. Akhavan-Toyserkani and M. Beeler, "Method and system for self synchronizing cryptographic parameters," ed: Google Patents, 2014.##[34] M. Briceno, I. Goldberg, D. Wagner, A pedagogical implementation of A5/1, http://www.scard.org, May 1999.## ##

تشخیص وقایع بصری به‌کمک اطلاعات مکانی-زمانی سیگنال ویدئو Recognition of Visual Events using Spatio-Temporal Information of the Video Signal 1 1 در این مقاله، ‏تشخیص وقایع بصری در ویدئو، با بهره‌گیری از اطلاعات زمانی سیگنال، به‌صورت تحلیلی موردتوجه قرار دارد. با استفاده از یادگیری انتقالی‏، توصیف‌گرهای آموزش‌دیده روی تصاویر به ویدئو اعمال می‌شوند تا تشخیص وقایع را با استفاده از منابع محاسباتی محدود‏، ممکن سازند. ‏در این مقاله، یک شبکه عصبی کانولوشنی به‌عنوان استخراج‌کننده نمرات مفاهیم از قاب‌‌های ویدئو به‌کار می‌رود‏. ابتدا پارامترهای این شبکه روی زیرمجموعه‌ای از داده‌های آموزش تنظیم ‏دقیق می‌شوند؛ سپس، توصیف‌گرهای خروجی از لایه‌های تمام‌متصل آن به‌عنوان توصیف‌گر سطح قاب مورداستفاده قرار می‌گیرند. توصیف‌گرهای به‌دست‌آمده، کدگذاری و در‌نهایت نرمالیزه‌سازی و طبقه‌بندی می‌شوند. نوآوری عمده این مقاله‏، ترکیب اطلاعات زمانی ویدئو در کدگذاری توصیف‌گرهای آن است. گنجاندن ساختاری اطلاعات بصری در فرایند کدگذاری توصیف‌گرهای ویدئویی،‏، اغلب نادیده گرفته می‌شود. این موضوع به کاهش دقت منجر می‌شود. برای حل این مسأله، یک روش کدگذاری نوین ارائه می‌شود که مصالحه بین پیچیدگی محاسبات و دقت در شناسایی وقایع ویدیویی را بهبود می‌دهد. در این کدگذاری‏، بعد زمانی سیگنال ویدئویی برای ساخت یک بردار مکانی-زمانی از توصیف‌گرهای مجتمع محلی (‎‎‎VLAD‎) استفاده، سپس نشان داده می‌شود که کدگذاری پیشنهادی ماهیتاً یک مسأله بهینه‌سازی است که با الگوریتم‌های‎ موجود به‌راحتی قابل‌حل است. در مقایسه با بهترین روش‌های موجود در حوزه تشخیص وقایع بصری مبتنی بر توصیف‌گرهای سطح قاب‏، روش پیشنهادی مدل بهتری را از ویدئو ارائه می‌کند. روش ارائه‌شده بر حسب سه معیار میانگین دقت متوسط، میانگین فراخوانی متوسط و معیار F به عملکرد بالاتری بر روی هر دو مجموعه‌‌‌داده آزمون مورد بررسی دست می‌یابد. نتایج به‌دست‌آمده توانمندی روش پیشنهادی را در بهبود عملکرد سامانه‌های تشخیص وقایع بصری تأیید می‌کنند. 2 Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approaches. Convolutional neural networks (CNNs) are among the most popular types of deep networks utilized in both imaga and video recognition tasks. They are initially made up of several convolutional layers, each of which followed by proper activation and possibly pooling layers. They often encompass one or more fully connected layers as the last layers. The favorite property of them in this work is the ability of CNNs to extract mid-level features from video frames. Actually, despite traditional approaches based on low-level visual features, the CNNs make it possible to extract higher level semantic features from the video frames. The focus of this paper is on recognition of visual events in video using CNNs. In this work, ‎image trained descriptor‎s are used to make video recognition can be done with low computational complexity. A tuned CNN is used as the frame descriptor and its fully connected layers are utilized as concept detectors. So, the featue maps of activation layers following fully connected layers act as feature vectors. These feature vectors (concept vectors) are actually the mid-level features which are a better video representation than the low level features. The obtained mid-level features can partially fill the semantic gap between low level features and high level semantics of video. The obtained descriptors from the CNNs for each video are varying length stack of feature vectors. To make the obtained descriptors organized and prepared for clasification, they must be properly encoded. The coded descriptors are then normalized and classified. The normaliztion may consist of conventional and normalization or more advanced power-law normalization. The main purpose of normalization is to change the distribution of descriptor values in a way to make them more uniformly distributed. So, very large or very small descriptors could have a more balanced impact on recognition of events. The main novelty of this paper is that spatial and temporal information in mid-level features are employed to construct a suitable coding procedure. We use temporal information in coding of video descriptors. Such information is often ignored, resulting in reduced coding efficiency. Hence, a new coding is proposed which improves the trade-off between the computation complexity of the recognition scheme and the accuracy in identifying video events. It is also shown that the proposed coding is in the form of an optimization problem that can be solved with existing algorithms. The optimization problem is initially non-convex and not solvable with the existing methods in polynomial time. So, it is transformed to a convex form which makes it a well defined optimization problem. While there are many methods to handle these types of convex optimization problems, we chose to use a strong convex optimization library to efficiently solve the problem and obtain the video descriptors. To confirm the effectiveness of the proposed descriptor coding method, extensive experiments are done on two large public datasets: Columbia consumer video (CCV) dataset and ActivityNet dataset. Both CCV and ActivityNet are popular publically available video event recognition datasets, with standard train/test splits, which are large enough to be used as reasonable benchmarks in video recognition tasks. Compared to the best practices available in the field of detecting visual events, the proposed method provides a better model of video and a much better mean average precision, mean average recall, and F score on the test set of CCV and ActivityNet datasets. The presented method not only improves the performance in terms of accuracy, but also reduces the computational cost with respect to those of the state of the art. The experiments vividly confirm the potential of the proposed method in improving the performance of visual recognition systems, especially in supervised video event detection. 134 119 2019/03/32018/11/132019/06/132018/04/222019/02/62018/05/132018/09/32019/01/192018/11/12 1397/8/21 2019/11/102020/08/182020/09/232021/02/272020/01/222021/03/12021/02/242020/08/182019/02/19 1397/11/30 محمد سلطانیان Mohammad Soltanian دانشگاه صنعتی شریف ایران ms64000@yahoo.com شاهرخ قائم‌مقامی Shahrokh Ghaemmaghami دانشگاه صنعتی شریف ایران ghaemmag@sharif.edu Convolutional neural network Average pooling Max pooling Support vector machine Vector of locally aggregated descriptors شبکه عصبی کانولوشنی‌ ‎‌ادغام میانگین‌ ادغام بیشینه‌ ماشین بردار پشتیبان‌ ‌بردار توصیف‌گرهای مجتمع محلی [1] M. Mohseni and M. Seriani, "Pedestrian Detection in Infrared Image Sequences Using SVM and Histogram Classifiers," JSDP, vol. 6, no. 1, pp. 79-90, 2009.##[2] S. Shafeipour Yourdeshahi, H. Seyedarabi, and A. Aghagolzadeh, "Video based Face Recognition Using Orthogonal Locality Preserving Projection," JSDP, vol. 13, no. 2, pp. 139-149, Sep. 2016.##[3] Z. Xu, Y. Yang, and A. G. Hauptmann, "A discriminative CNN video representation for event detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1798-1807, Accessed: Apr. 08, 2016. [Online].##[4] L. Wang, C. Gao, J. Liu, and D. Meng, "A novel learning-based frame pooling method for event detection," Signal Processing, vol. 140, pp. 45-52, 2017.##[5] S. Kwak, B. Han, and J. H. Han, "Scenario-based video event recognition by constraint flow," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 3345-3352.##[6] Y. Cong, J. Yuan, and J. Luo, "Towards scalable summarization of consumer videos via sparse dictionary selection," IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 66-75, 2012.##[7] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2005, vol. 1, pp. 886-893.##[8] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.##[9] K. E. Van De Sande, T. Gevers, and C. G. Snoek, "Evaluating color descriptors for object and scene recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1582-1596, 2010.##[10] I. Laptev, "On space-time interest points," International Journal of Computer Vision, vol. 64, no. 2, pp. 107-123, 2005.##[11] M. Chen and A. Hauptmann, "Mosift: Recognizing human actions in surveillance videos," Carnegie Mellon University, Technical Report CMU-CS-09-161, 2009.##[12] H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, "Action recognition by dense trajectories," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2011, pp. 3169-3176.##[13] H. Wang and C. Schmid, "Action recognition with improved trajectories," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Dec. 2013, pp. 3551-3558.##[14] D. Oneata, J. Verbeek, and C. Schmid, "Action and event recognition with Fisher vectors on a compact feature set," in Proceedings of the IEEE International Conference on Computer Vision, Dec. 2013, pp. 1817-1824.##[15] F. Metze, S. Rawat, and Y. Wang, "Improved audio features for large-scale multimedia event detection," in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul. 2014, pp. 1-6.##[16] M.-L. Shyu, Z. Xie, M. Chen, and S.-C. Chen, "Video semantic event/concept detection using a subspace-based multimedia data mining framework," IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 252-259, 2008.##[17] V. S. Tseng, J.-H. Su, J.-H. Huang, and C.-J. Chen, "Integrated mining of visual features, speech features, and frequent patterns for semantic video annotation," IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 260-267, 2008.##[18] X. Peng and C. Schmid, "Encoding feature maps of cnns for action recognition," presented at the CVPR, THUMOS Challenge Workshop, 2015, Accessed: Apr. 08, 2016. [Online].##[19] R. Baradaran, E.Golpar-Raboki, "Feature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context", JSDP, 2019, vol. 16 (3), pp. 88-79.##[20] F. Sherafati, J.Tahmoresnezhad, "Image Classification via Sparse Representation and Subspace Alignment", JSDP, 2020, vol.17 (2) , pp.58-47##[21] R. Aly et al., "The AXES submissions at TrecVid 2013," 2013.##[22] Y. Shi, Y. Tian, Y. Wang, and T. Huang, "Sequential deep trajectory descriptor for action recognition with three-stream CNN," IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1510-1520, 2017.##[23] P. Scovanner, S. Ali, and M. Shah, "A 3-dimensional sift descriptor and its application to action recognition," in Proceedings of the 15th ACM international conference on Multimedia, Sep. 2007, pp. 357-360.##[24] A. Klaser, M. Marszałek, and C. Schmid, "A spatio-temporal descriptor based on 3d-gradients," in Proceedings of the 19th British Machine Vision Conference (BMVC), 2008, pp. 99.1-99.10.##[25] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.##[26] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2009, pp. 248-255.##[27] M. Momeny, M .A Sarram, A. Latif, R. Sheikhpour, "A Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images", JSDP, 2021, vol.17 (4), pp.139-154.##[28] D. Oneata, J. Verbeek, and C. Schmid, "The LEAR submission at Thumos 2014," 2014.##[29] S. Zha, F. Luisier, W. Andrews, N. Srivastava, and R. Salakhutdinov, "Exploiting image-trained cnn architectures for unconstrained video classification," in Proceedings of the 26th British Machine Vision Conference, 2015, p. 60.1-60.13.##[30] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proceedings of the Advances in neural information processing systems, Jan. 2012, pp. 1097-1105.##[31] J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei, "ILSVRC-2012, 2012," 2012, [Online]. Available: http://www. image-net. org/challenges/LSVRC.##[32] M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in Proceedings of the European Conference on Computer Vision, 2014, pp. 818-833.##[33] V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proceedings of the 27th International Conference on Machine Learning (ICML), 2010, pp. 807-814.##[34] M. Soltanian and S. Ghaemmaghami, "Hierarchical Concept Score Post-processing and Concept-wise Normalization in CNN based Video Event Recognition," IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 157-172, 2019.##[35] Y. Han, X. Wei, X. Cao, Y. Yang, and X. Zhou, "Augmenting image descriptions using structured prediction output," IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1665-1676, 2014.##[36] H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating local descriptors into a compact image representation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 3304-3311.##[37] Z. Zhao, Y. Song, and F. Su, "Specific video identification via joint learning of latent semantic concept, scene and temporal structure," Neurocomputing, vol. 208, pp. 378-386, 2016.##[38] F. Perronnin and C. Dance, "Fisher kernels on visual vocabularies for image categorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8, Accessed: Jul. 06, 2016. [Online].##[39] F. Markatopoulou et al., "ITI-CERTH participation to TRECVID 2013," in TRECVID 2013 Workshop, 2013, pp. 12-17.##[40] C. Sun and R. Nevatia, "Large-scale web video event classification by use of fisher vectors," in IEEE Workshop on Applications of Computer Vision (WACV), 2013, pp. 15-22, Accessed: Jul. 06, 2016. [Online].##[41] R. Arandjelovic and A. Zisserman, "All about VLAD," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1578-1585, Accessed: Jul. 06, 2016. [Online].##[42] J. Delhumeau, P.-H. Gosselin, H. Jégou, and P. Pérez, "Revisiting the vlad image representation," in Proceedings of the 21st ACM international conference on Multimedia, Oct. 2013, pp. 653-656.##[43] G. Tolias, Y. Avrithis, and H. Jegou, "To Aggregate or Not to aggregate: Selective Match Kernels for Image Search," in Proceedings of the IEEE International Conference on Computer Vision, Dec. 2013, pp. 1401-1408, doi: 10.1109/ICCV.2013.177.##[44] Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proceedings of the 27th international conference on machine learning (ICML), Jun. 2010, pp. 111-118.##[45] T. De Campos, G. Csurka, and F. Perronnin, "Images as sets of locally weighted features," Computer Vision and Image Understanding, vol. 116, no. 1, pp. 68-85, 2012.##[46] N. Murray and F. Perronnin, "Generalized Max Pooling," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2014, pp. 2473-2480, doi: 10.1109/CVPR.2014.317.##[47] T. Ge, Q. Ke, and J. Sun, "Sparse-Coded Features for Image Retrieval.," in Proceedings of the British Machine Vision Conference (BMVC), 2013, pp. 1-11, Accessed: Nov. 10, 2016. [Online].##[48] H. Jégou and O. Chum, "Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening," in Proceedings of the European Conference on Computer Vision (ECCV), 2012, pp. 774-787, Accessed: Nov. 10, 2016. [Online].##[49] M. K. Reddy, S. Arora, and R. V. Babu, "Spatio-temporal feature based VLAD for efficient video retrieval," in Proceedings of 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2013, pp. 1-4, Accessed: Nov. 10, 2016. [Online].##[50] M. Jain, H. Jegou, and P. Bouthemy, "Better Exploiting Motion for Better Action Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 2555-2562, doi: 10.1109/CVPR.2013.330.##[51] M. Douze, H. Jégou, C. Schmid, and P. Pérez, "Compact video description for copy detection with precise temporal alignment," in Proceedings of the European Conference on Computer Vision, 2010, pp. 522-535, Accessed: Nov. 10, 2016. [Online].##[52] A. Abbas, N. Deligiannis, and Y. Andreopoulos, "Vectors of locally aggregated centers for compact video representation," in 2015 IEEE International Conference on Multimedia and Expo (ICME), 2015, pp. 1-6, Accessed: Nov. 07, 2016. [Online].##[53] J. Revaud, M. Douze, C. Schmid, and H. Jegou, "Event Retrieval in Large Video Collections with Circulant Temporal Encoding," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 2459-2466, doi: 10.1109/CVPR.2013.318.##[54] M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx, 2014.##[55] Y.-G. Jiang, G. Ye, S.-F. Chang, D. Ellis, and A. C. Loui, "Consumer video understanding: A benchmark database and an evaluation of human and machine performance," in Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011, pp. 29.1-29.8.##[56] "Pretrained CNNs - MatConvNet," 2017, Accessed: Jun. 12, 2017. [Online]. Available: http://www.vlfeat.org/matconvnet/pretrained/.##[57] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 27.1-27.27, 2011.##[58] "Matlab VideoUtils," SourceForge, 2015. https://sourceforge.net/projects/videoutils/ (accessed May 29, 2016).##[59] A. Vedaldi and K. Lenc, "MatConvNet: Convolutional neural networks for matlab," in Proceedings of the 23rd Annual ACM Conference on Multimedia, Oct. 2015, pp. 689-692.##[60] A. Vedaldi and B. Fulkerson, "VLFeat: An open and portable library of computer vision algorithms," in Proceedings of the 18th ACM International Conference on Multimedia, 2010, pp. 1469-1472.##[61] Y.-G. Jiang, Q. Dai, T. Mei, Y. Rui, and S.-F. Chang, "Super Fast Event Recognition in Internet Videos," IEEE Transactions on Multimedia, vol. 17, no. 8, pp. 1174-1186, 2015.##[62] P. Napoletano, "Visual descriptors for content-based retrieval of remote-sensing images," International Journal of Remote Sensing, vol. 39, no. 5, pp. 1343-1376, 2018.##[63] C. Goutte and E. Gaussier, "A probabilistic interpretation of precision, recall and F-score, with implication for evaluation," in Proceedings of the European Conference on Information Retrieval, 2005, pp. 345-359.##[64] F. Perronnin, J. Sánchez, and T. Mensink, "Improving the fisher kernel for large-scale image classification," in Proceedings of the European Conference on Computer Vision (ECCV), Sep. 2010, pp. 143-156.##[65] Z. Xu, Y. Yang, I. Tsang, N. Sebe, and A. G. Hauptmann, "Feature weighting via optimal thresholding for video analysis," in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3440-3447.##[66] A. J. Ma and P. C. Yuen, "Reduced analytic dependency modeling: Robust fusion for visual recognition," International journal of computer vision, vol. 109, no. 3, pp. 233-251, 2014.##[67] G. Ye, D. Liu, I.-H. Jhuo, and S.-F. Chang, "Robust late fusion with rank minimization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3021-3028.##[68] I.-H. Jhuo et al., "Discovering joint audio-visual codewords for video event detection," Machine vision and applications, vol. 25, no. 1, pp. 33-47, 2014.##[69] D. Liu, K.-T. Lai, G. Ye, M.-S. Chen, and S.-F. Chang, "Sample-specific late fusion for visual category recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 803-810.##[70] Z. Wu, Y.-G. Jiang, J. Wang, J. Pu, and X. Xue, "Exploring inter-feature and inter-class relationships with deep neural networks for video classification," in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 167-176.##[1] M. Mohseni and M. Seriani, "Pedestrian Detection in Infrared Image Sequences Using SVM and Histogram Classifiers," JSDP, vol. 6, no. 1, pp. 79-90, 2009.##[1] محسن، سریانی محسن. تشخیص عابر پیاده با استفاده از کلاس بندهای SVM و هیستوگرام در توالی تصاویر مادون قرمز. پردازش علائم و داده‌ها. ۱۳۸۸; ۶ (۱).##[2] S. Shafeipour Yourdeshahi, H. Seyedarabi, and A. Aghagolzadeh, "Video based Face Recognition Using Orthogonal Locality Preserving Projection," JSDP, vol. 13, no. 2, pp. 139-149, Sep. 2016.##[2] شفیع‌پور یوردشاهی سجاد، سید‌عربی هادی، آقاگل‌زاده علی. شناسایی چهره در رشته‌های ویدیویی با استفاده از افکنش متعامد با حفظ ساختار محلی . پردازش علائم و داده‌ها. ۱۳۹۵; ۱۳ (۲) :۱۳۹-۱۴۹.##[3] Z. Xu, Y. Yang, and A. G. Hauptmann, "A discriminative CNN video representation for event detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1798-1807, Accessed: Apr. 08, 2016. [Online].##[4] L. Wang, C. Gao, J. Liu, and D. Meng, "A novel learning-based frame pooling method for event detection," Signal Processing, vol. 140, pp. 45-52, 2017.##[5] S. Kwak, B. Han, and J. H. Han, "Scenario-based video event recognition by constraint flow," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 3345-3352.##[6] Y. Cong, J. Yuan, and J. Luo, "Towards scalable summarization of consumer videos via sparse dictionary selection," IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 66-75, 2012.##[7] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2005, vol. 1, pp. 886-893.##[8] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.##[9] K. E. Van De Sande, T. Gevers, and C. G. Snoek, "Evaluating color descriptors for object and scene recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1582-1596, 2010.##[10] I. Laptev, "On space-time interest points," International Journal of Computer Vision, vol. 64, no. 2, pp. 107-123, 2005.##[11] M. Chen and A. Hauptmann, "Mosift: Recognizing human actions in surveillance videos," Carnegie Mellon University, Technical Report CMU-CS-09-161, 2009.##[12] H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, "Action recognition by dense trajectories," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2011, pp. 3169-3176.##[13] H. Wang and C. Schmid, "Action recognition with improved trajectories," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Dec. 2013, pp. 3551-3558.##[14] D. Oneata, J. Verbeek, and C. Schmid, "Action and event recognition with Fisher vectors on a compact feature set," in Proceedings of the IEEE International Conference on Computer Vision, Dec. 2013, pp. 1817-1824.##[15] F. Metze, S. Rawat, and Y. Wang, "Improved audio features for large-scale multimedia event detection," in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul. 2014, pp. 1-6.##[16] M.-L. Shyu, Z. Xie, M. Chen, and S.-C. Chen, "Video semantic event/concept detection using a subspace-based multimedia data mining framework," IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 252-259, 2008.##[17] V. S. Tseng, J.-H. Su, J.-H. Huang, and C.-J. Chen, "Integrated mining of visual features, speech features, and frequent patterns for semantic video annotation," IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 260-267, 2008.##[18] X. Peng and C. Schmid, "Encoding feature maps of cnns for action recognition," presented at the CVPR, THUMOS Challenge Workshop, 2015, Accessed: Apr. 08, 2016. [Online].##[19] R. Baradaran, E.Golpar-Raboki, "Feature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context", JSDP, 2019, vol. 16 (3), pp. 88-79.##[19] برادران راضیه، گلپر رابوکی عفت. استخراج ویژگی‌ و بررسی کارآیی روش‌های کاهش بُعد در زمینه تحلیل احساس. پردازش علائم و داده‌ها. ۱۳۹۸; ۱۶ (۳) :۸۸-۷۹##[20] F. Sherafati, J.Tahmoresnezhad, "Image Classification via Sparse Representation and Subspace Alignment", JSDP, 2020, vol.17 (2) , pp.58-47##[20] شرافتی فریماه، طهمورث نژاد جعفر. طبقه‌بندی تصاویر با استفاده از نمایش تُنُک و تطبیق زیرفضا. پردازش علائم و داده‌ها. ۱۳۹۹; ۱۷ (۲) :۵۸-۴۷.##[21] R. Aly et al., "The AXES submissions at TrecVid 2013," 2013.##[22] Y. Shi, Y. Tian, Y. Wang, and T. Huang, "Sequential deep trajectory descriptor for action recognition with three-stream CNN," IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1510-1520, 2017.##[23] P. Scovanner, S. Ali, and M. Shah, "A 3-dimensional sift descriptor and its application to action recognition," in Proceedings of the 15th ACM international conference on Multimedia, Sep. 2007, pp. 357-360.##[24] A. Klaser, M. Marszałek, and C. Schmid, "A spatio-temporal descriptor based on 3d-gradients," in Proceedings of the 19th British Machine Vision Conference (BMVC), 2008, pp. 99.1-99.10.##[25] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.##[26] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2009, pp. 248-255.##[27] M. Momeny, M .A Sarram, A. Latif, R. Sheikhpour, "A Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images", JSDP, 2021, vol.17 (4), pp.139-154.##[27] مومنی محمد، صرام مهدی آقا، لطیف علی محمد، شیخ‌پور راضیه. ارائه یک شبکه عصبی کانولوشنال مبتنی بر ادغام تطبیقی پویا برای طبقه‌بندی تصاویر نوفه‌ای. پردازش علائم و داده‌ها. ۱۳۹۹; ۱۷ (۴) :۱۳۹-۱۵۴##[28] D. Oneata, J. Verbeek, and C. Schmid, "The LEAR submission at Thumos 2014," 2014.##[29] S. Zha, F. Luisier, W. Andrews, N. Srivastava, and R. Salakhutdinov, "Exploiting image-trained cnn architectures for unconstrained video classification," in Proceedings of the 26th British Machine Vision Conference, 2015, p. 60.1-60.13.##[30] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proceedings of the Advances in neural information processing systems, Jan. 2012, pp. 1097-1105.##[31] J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei, "ILSVRC-2012, 2012," 2012, [Online]. Available: http://www. image-net. org/challenges/LSVRC.##[32] M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in Proceedings of the European Conference on Computer Vision, 2014, pp. 818-833.##[33] V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proceedings of the 27th International Conference on Machine Learning (ICML), 2010, pp. 807-814.##[34] M. Soltanian and S. Ghaemmaghami, "Hierarchical Concept Score Post-processing and Concept-wise Normalization in CNN based Video Event Recognition," IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 157-172, 2019.##[35] Y. Han, X. Wei, X. Cao, Y. Yang, and X. Zhou, "Augmenting image descriptions using structured prediction output," IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1665-1676, 2014.##[36] H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating local descriptors into a compact image representation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 3304-3311.##[37] Z. Zhao, Y. Song, and F. Su, "Specific video identification via joint learning of latent semantic concept, scene and temporal structure," Neurocomputing, vol. 208, pp. 378-386, 2016.##[38] F. Perronnin and C. Dance, "Fisher kernels on visual vocabularies for image categorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8, Accessed: Jul. 06, 2016. [Online].##[39] F. Markatopoulou et al., "ITI-CERTH participation to TRECVID 2013," in TRECVID 2013 Workshop, 2013, pp. 12-17.##[40] C. Sun and R. Nevatia, "Large-scale web video event classification by use of fisher vectors," in IEEE Workshop on Applications of Computer Vision (WACV), 2013, pp. 15-22, Accessed: Jul. 06, 2016. [Online].##[41] R. Arandjelovic and A. Zisserman, "All about VLAD," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1578-1585, Accessed: Jul. 06, 2016. [Online].##[42] J. Delhumeau, P.-H. Gosselin, H. Jégou, and P. Pérez, "Revisiting the vlad image representation," in Proceedings of the 21st ACM international conference on Multimedia, Oct. 2013, pp. 653-656.##[43] G. Tolias, Y. Avrithis, and H. Jegou, "To Aggregate or Not to aggregate: Selective Match Kernels for Image Search," in Proceedings of the IEEE International Conference on Computer Vision, Dec. 2013, pp. 1401-1408, doi: 10.1109/ICCV.2013.177.##[44] Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proceedings of the 27th international conference on machine learning (ICML), Jun. 2010, pp. 111-118.##[45] T. De Campos, G. Csurka, and F. Perronnin, "Images as sets of locally weighted features," Computer Vision and Image Understanding, vol. 116, no. 1, pp. 68-85, 2012.##[46] N. Murray and F. Perronnin, "Generalized Max Pooling," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2014, pp. 2473-2480, doi: 10.1109/CVPR.2014.317.##[47] T. Ge, Q. Ke, and J. Sun, "Sparse-Coded Features for Image Retrieval.," in Proceedings of the British Machine Vision Conference (BMVC), 2013, pp. 1-11, Accessed: Nov. 10, 2016. [Online].##[48] H. Jégou and O. Chum, "Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening," in Proceedings of the European Conference on Computer Vision (ECCV), 2012, pp. 774-787, Accessed: Nov. 10, 2016. [Online].##[49] M. K. Reddy, S. Arora, and R. V. Babu, "Spatio-temporal feature based VLAD for efficient video retrieval," in Proceedings of 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2013, pp. 1-4, Accessed: Nov. 10, 2016. [Online].##[50] M. Jain, H. Jegou, and P. Bouthemy, "Better Exploiting Motion for Better Action Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 2555-2562, doi: 10.1109/CVPR.2013.330.##[51] M. Douze, H. Jégou, C. Schmid, and P. Pérez, "Compact video description for copy detection with precise temporal alignment," in Proceedings of the European Conference on Computer Vision, 2010, pp. 522-535, Accessed: Nov. 10, 2016. [Online].##[52] A. Abbas, N. Deligiannis, and Y. Andreopoulos, "Vectors of locally aggregated centers for compact video representation," in 2015 IEEE International Conference on Multimedia and Expo (ICME), 2015, pp. 1-6, Accessed: Nov. 07, 2016. [Online].##[53] J. Revaud, M. Douze, C. Schmid, and H. Jegou, "Event Retrieval in Large Video Collections with Circulant Temporal Encoding," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 2459-2466, doi: 10.1109/CVPR.2013.318.##[54] M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx, 2014.##[55] Y.-G. Jiang, G. Ye, S.-F. Chang, D. Ellis, and A. C. Loui, "Consumer video understanding: A benchmark database and an evaluation of human and machine performance," in Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011, pp. 29.1-29.8.##[56] "Pretrained CNNs - MatConvNet," 2017, Accessed: Jun. 12, 2017. [Online]. Available: http://www.vlfeat.org/matconvnet/pretrained/.##[57] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 27.1-27.27, 2011.##[58] "Matlab VideoUtils," SourceForge, 2015. https://sourceforge.net/projects/videoutils/ (accessed May 29, 2016).##[59] A. Vedaldi and K. Lenc, "MatConvNet: Convolutional neural networks for matlab," in Proceedings of the 23rd Annual ACM Conference on Multimedia, Oct. 2015, pp. 689-692.##[60] A. Vedaldi and B. Fulkerson, "VLFeat: An open and portable library of computer vision algorithms," in Proceedings of the 18th ACM International Conference on Multimedia, 2010, pp. 1469-1472.##[61] Y.-G. Jiang, Q. Dai, T. Mei, Y. Rui, and S.-F. Chang, "Super Fast Event Recognition in Internet Videos," IEEE Transactions on Multimedia, vol. 17, no. 8, pp. 1174-1186, 2015.##[62] P. Napoletano, "Visual descriptors for content-based retrieval of remote-sensing images," International Journal of Remote Sensing, vol. 39, no. 5, pp. 1343-1376, 2018.##[63] C. Goutte and E. Gaussier, "A probabilistic interpretation of precision, recall and F-score, with implication for evaluation," in Proceedings of the European Conference on Information Retrieval, 2005, pp. 345-359.##[64] F. Perronnin, J. Sánchez, and T. Mensink, "Improving the fisher kernel for large-scale image classification," in Proceedings of the European Conference on Computer Vision (ECCV), Sep. 2010, pp. 143-156.##[65] Z. Xu, Y. Yang, I. Tsang, N. Sebe, and A. G. Hauptmann, "Feature weighting via optimal thresholding for video analysis," in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3440-3447.##[66] A. J. Ma and P. C. Yuen, "Reduced analytic dependency modeling: Robust fusion for visual recognition," International journal of computer vision, vol. 109, no. 3, pp. 233-251, 2014.##[67] G. Ye, D. Liu, I.-H. Jhuo, and S.-F. Chang, "Robust late fusion with rank minimization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3021-3028.##[68] I.-H. Jhuo et al., "Discovering joint audio-visual codewords for video event detection," Machine vision and applications, vol. 25, no. 1, pp. 33-47, 2014.##[69] D. Liu, K.-T. Lai, G. Ye, M.-S. Chen, and S.-F. Chang, "Sample-specific late fusion for visual category recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 803-810.##[70] Z. Wu, Y.-G. Jiang, J. Wang, J. Pu, and X. Xue, "Exploring inter-feature and inter-class relationships with deep neural networks for video classification," in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 167-176.## ##

بهبود و موازی‌سازی سازوکار تشخیص نفوذ شبکه Snort با استفاده از واحد پردازش گرافیکی Improvement and parallelization of Snort network intrusion detection mechanism using graphics processing unit 1 1 سامانه تشخیص نفوذ شبکه به‌منظور برقراری امنیت کامل در شبکه‌های رایانه‌ای به‌طور گسترده مورداستفاده قرار می‌گیرد. سامانه تشخیص نفوذ شبکه مبتنی بر امضا نسبت به نوع مبتنی بر ناهنجاری به‌دلیل نرخ هشدار اشتباه پایین‌تر، از عمومیت بالاتری برخوردار است. فرآیند تطبیق الگو در چنین دستگاهی نیازمند پردازش محاسباتی بالا است. از سوی دیگر توسعه سریع پهنای باند شبکه و سرعت‌های بالای پیوند که خود موجب از‌دست‌رفتن تعداد زیادی از بسته‌های ورودی در سامانه تشخیص نفوذ شبکه می‌‌شود، به‌عنوان عوامل کلیدی محدودکننده کارایی این نوع سامانه، آن را با چالش‌هایی روبه‌رو کرده است. Snort یک سامانه تشخیص نفوذ شبکه مبتنی بر امضا بوده که به‌دلیل متن‌باز، رایگان و سبک‌بودن بسیار پرکاربرد است. در این مقاله جهت بهبود کارایی سامانه تشخیص نفوذ شبکه snort، از ایده کلیدی فیلتر‌کردن بسته‌های غیرضروری شبکه بر اساس فهرست سیاه نشانی‌‌ها، به‌عنوان یک سازوکار پیش‌پردازش استفاده‌ شده است. یکی از چالش‌های مهم این سازوکار کاهش سرعت فیلتر‌کردن بسته‌ها، با افزایش حجم ترافیک شبکه است؛ بنابراین به‌عنوان بهبود دوم، جهت تسریع عملکرد این سامانه ارائه‌شده، نسخه موازی آن را روی بستر رمز جهت اجرا روی واحد پردازش گرافیکی ارائه کردیم. الگوریتم پیشنهادی را بر روی مجموعه‌داده DARPA در یک پردازنده گرافیکی آزمایش شد. نتایج ارزیابی نشان می‌دهد که روش پیشنهادی با تسریعی بیش از سی برابر نسبت به نسخه متوالی، باعث بهبود قابل‌توجهی در عملکرد فیلتر بسته مبتنی بر فهرست سیاه می‌‌شود. همچنین، بهره‌وری روش پیشنهادی در استفاده از منابع پردازنده گرافیکی برای اجرای موازی تشخیص نفوذ نسبت به بهترین روش موجود حدود 81 درصد بیشتر است. 2 Nowadays, Network Intrusion Detection Systems (NIDS) are widely used to provide full security on computer networks. IDS are categorized into two primary types, including signature-based systems and anomaly-based systems. The former is more commonly used than the latter due to its lower error rate. The core of a signature-based IDS is the pattern matching. This process is inherently a computationally intensive task, and in the worst case, about 80% of the total processing time of an IDS is spent on it. On the other hand, the rapid development of network bandwidth and high link speeds, which in turn leads to a loss of a large number of inbound packets in the network intrusion detection system, has posed challenges as crucial factors limiting the performance of this type of system. Snort is a signature-based NIDS that is highly interested due to being open-source, free, and easy to use. To resolve the challenges mentioned above, we propose an enhanced version of Snort, which is enriched by exploiting two key ideas. The first idea is the filtering of unnecessary packets based on a blacklist of source IP addresses. This filter is used as a preprocessing mechanism to improve the efficiency of the Snort. However, the packet filtering speed is decreased by increasing the network traffic volumes. Therefore, to accelerate the function of this mechanism, we have proposed a second crucial idea. The data-parallel nature of snort functions lets us parallelize two main computationally intensive functions of it on the graphical processing unit. These functions include the lookup on the blacklist filter in the preprocessing stage and the signature matching of Snort, which completes the intrusion detection process. For parallelizing the preprocessing step of Snort, first, a blacklist is provided from the DARPA dataset. Next, this blacklist is transferred together with the Snort ruleset to the global memory of the GPU. Finally, each thread concurrently matches each packet against the blacklist filters. For parallelizing the signature matching step of Snort, the well-known pattern matching algorithm of Boyer-Moore is parallelized similarly. Evaluation results show that the proposed method, by up to 30 times faster than the sequential version, significantly improves the blacklist-based filtering performance. Also, the efficiency of the proposed method in using GPU resources for parallel intrusion detection is 81 percent higher than the best state-of-the-art method. 150 135 2019/03/32018/11/132019/06/132018/04/222019/02/62018/05/132018/09/32019/01/192018/11/122019/02/1 1397/11/12 2019/11/102020/08/182020/09/232021/02/272020/01/222021/03/12021/02/242020/08/182019/02/192021/01/30 1399/11/11 مهدی عباسی Mahdi Abbasi دانشگاه بوعلی سینا ایران abbasi@basu.ac.ir مطهره افشاری حقدوست Motahareh Afshari Haghdoost دانشگاه بوعلی سینا ایران a.afshari1371@gmail.com Network intrusion detection system packet filter black list pattern matching graphics processing unit سامانه تشخیص نفوذ شبکه فیلتر بسته فهرست سیاه تطبیق الگو واحد پردازش گرافیکی [1] R. Chi, "Intrusion detection system based on snort," Lecture Notes in Electrical Engineering, vol. 272, pp. 657-664, 2014.##[2] Y. Meng and L.-F. Kwok, "Adaptive blacklist-based packet filter with a statistic-based approach in network intrusion detection," J. Netw. Comput. Appl., vol. 39, pp. 83-92, 2014.##[3] T. Ho, S. Cho, and S. Oh, "Parallel multiple pattern matching schemes based on cuckoo filter for deep packet inspection on graphics processing units," IET Information Security, vol. 12, pp. 381-388, 2018.##[4] C. Hung, P. Wu, H. Wang, and C. Lin, "Efficient Parallel Muti-pattern Matching Using GPGPU Acceleration for Packet Filtering," in 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, 2015, pp. 1843-1847.##[5] C.-L. Hung, C.-Y. Lin, and H.-H. Wang, "An efficient parallel-network packet pattern-matching approach using GPUs," Journal of Systems Architecture, vol. 60, pp. 431-439, 2014/05/01/ 2014.##[6] K. RahimiZadeh, M. Torkamani, and A. Dehghani, "Mapping of McGraw Cycle to RUP Methodology for Secure Software Developing," Signal and Data Processing, vol. 17, pp. 33-46, 2020.##[7] K. Kendall, "A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems," Electrical Engineering and Computer Science, MASSACHUSETTS INSTITUTE OF TECHNOLOGY, MIT Lincoln, 1999.##[8] P. Innella and O. McMillan, An Introduction to Intrusion Detection Systems, 2001.##[9] P. Bunel, "An introduction to Intrusion Detection Systems," in Sans Security Essentials v1.4c, ed LONDON, 2004.##[10] P. Bunel, "Host- vs. Network-Based Intrusion Detection Systems," in Sans Security Essentials, ed, 2004.##[11] X. Luo, "Model design artificial intelligence and research of adaptive network intrusion detection and defense system using fuzzy logic," Journal of Intelligent & Fuzzy Systems, pp. 1-9.##[12] A. S. Almogren, "Intrusion detection in Edge-of-Things computing," Journal of Parallel and Distributed Computing, vol. 137, pp. 259-265, 2020.##[13] F. Erlacher and F. Dressler, "On high-speed flow-based intrusion detection using snort-compatible signatures," IEEE Transactions on Dependable and Secure Computing, 2020.##[14] A. Thakkar and R. Lohiya, "A review of the advancement in intrusion detection datasets," Procedia Computer Science, vol. 167, pp. 636-645, 2020.##[15] B. Caswell, J. Beale, and A. R Baker, Snort IDS and IPS Toolkit. Syngress Publishing, Inc. Elsevier, Inc.: Williams, Andrew 2007.##[16] R. Chi, Intrusion Detection System Based on Snort. China, 2014.##[17] M. Roesch, C. Green, and S. Team, SNORT Users Manual 2.9.9, 2016.##[18] S. Sharma and M. Dixit, "A Review on Network Intrusion Detection System Using Open Source Snort," vol. 9, pp. 61-70, 2016.##[19] C.-H. Lin, "Accelerating String Matching Algorithms on Multicore Processors."##[20] R. S. Boyer and J. S. Moore, "A fast string searching algorithm," Commun. ACM, vol. 20, pp. 762-772, 1977.##[21] A. V. Aho and M. J. Corasick, "Efficient string matching: an aid to bibliographic search," Commun. ACM, vol. 18, pp. 333-340, 1975.##[22] C. L. Hung, C. Y. Lin, H. h. Wang, and C. Y. Chang, "Efficient Packet Pattern Matching for Gigabit Network Intrusion Detection Using GPUs," in High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, 2012, pp. 1612-1617.##[23] C. S. Kouzinopoulos and K. G. Margaritis, "String Matching on a Multicore GPU Using CUDA," in Informatics, 2009. PCI '09. 13th Panhellenic Conference on, 2009, pp. 14-18.##[24] h. sadeghi and A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks," Signal and Data Processing, vol. 14, pp. 19-30, 2018.##[25] G. Vasiliadis, M. Polychronakis, and S. Ioannidis, "MIDeA: a multi-parallel intrusion detection architecture," presented at the Proceedings of the 18th ACM conference on Computer and communications security, Chicago, Illinois, USA, 2011.##[26] S. Soroushnia, M. Daneshtalab, T. Pahikkala, and J. Plosila, "Parallel Implementation of Fuzzified Pattern Matching Algorithm on GPU," in 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2015, pp. 341-344.##[27] N. P. Tran, M. Lee, S. Hong, and J. Choi, "High Throughput Parallel Implementation of Aho-Corasick Algorithm on a GPU," in Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2013 IEEE 27th International, 2013, pp. 1807-1816.##[28] G. Vasiliadis, M. Polychronakis, and S. Ioannidis, "Parallelization and characterization of pattern matching using GPUs," in Workload Characterization (IISWC), 2011 IEEE International Symposium on, 2011, pp. 216-225.##[29] C. H. Lin, C. H. Liu, L. S. Chien, and S. C. Chang, "Accelerating Pattern Matching Using a Novel Parallel Algorithm on GPUs," IEEE Transactions on Computers, vol. 62, pp. 1916-06, 20313.##[30] L. Vokorokos, M. Ennert, M. >Čajkovský, and J. Radušovský, "A Survey of parallel intrusion detection on graphical processors," Central European Journal of Computer Science, vol. 4, pp. 222-230, 2014.##[31] A. P. M.S., "Parallelizing a network intrusion detection system using a GPU," Master of Science, Computer Science and Engineering, Louisville, UK, 2012.##[32] G. Vasiliadis, S. Antonatos, M. Polychronakis, E. P. Markatos, and S. Ioannidis, "Gnort: High Performance Network Intrusion Detection Using Graphics Processors," in Recent Advances in Intrusion Detection: 11th International Symposium, RAID 2008, Cambridge, MA, USA, September 15-17, 2008. Proceedings, R. Lippmann, E. Kirda, and A. Trachtenberg, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 116-134.##[33] G. Vasiliadis, M. Polychronakis, S. Antonatos, E. P. Markatos, and S. Ioannidis, "Regular Expression Matching on Graphics Hardware for Intrusion Detection," in Recent Advances in Intrusion Detection: 12th International Symposium, RAID 2009, Saint-Malo, France, September 23-25, 2009. Proceedings, E. Kirda, S. Jha, and D. Balzarotti, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 265-283.##[34] M. A. Jamshed, J. Lee, S. Moon, I. Yun, D. Kim, S. Lee, et al., "Kargus: a highly-scalable software-based intrusion detection system," presented at the Proceedings of the 2012 ACM conference on Computer and communications security, Raleigh, North Carolina, USA, 2012.##[35] H. Song and J. W. Lockwood, "Efficient packet classification for network intrusion detection using FPGA," presented at the Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays, Monterey, California, USA, 2005.##[36] Y. Meng and L. f. Kwok, "Adaptive context-aware packet filter scheme using statistic-based blacklist generation in network intrusion detection," in Information Assurance and Security (IAS), 2011 7th International Conference on, 2011, pp. 74-79.##[37] M. Ramesh and H. Jeon, "Parallelizing Deep Packet Inspection on GPU," in 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), 2018, pp. 248-253.##[38] S. Hakak, A. Kamsin, P. Shivakumara, G. A. Gilkar, W. Z. Khan, and M. Imran, "Exact String Matching Algorithms: Survey, Issues, and Future Research Directions," IEEE Access, 2019.##[39] C.-L. Hung, T.-H. Hsu, H.-H. Wang, and C.-Y. Lin, A GPU-based Bit-Parallel Multiple Pattern Matching Algorithm, 2018.##[40] C. Thomas, V. Sharma, and N. Balakrishnan, Usefulness of DARPA dataset for intrusion detection system evaluation, 2008.##[41] P. P, M. T, and L. Raj, A Comparative Study on String Matching Algorithm of Biological Sequences, 2014.##[42] N. Tuck, T. Sherwood, B. Calder, and G. Varghese, Deterministic Memory-Efficient String Matching Algorithms for Intrusion Detection vol. 4, 2004.##[43] "Exact Matching: Classical Comparison-Based Methods," in Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, D. Gusfield, Ed., ed Cambridge: Cambridge University Press, 1997, pp. 16-34.##[44] C. H. Lin, "Accelerating String Matching Algorithms on Multicore Processors," vol. 2, pp. 52-59, 6 June- 2016 2016.##[45] J. Yu, Y. Xue, and J. Li, "Memory efficient string matching algorithm for network intrusion management system," Tsinghua Science and Technology, vol. 12, pp. 585-593, 2007.##[1] R. Chi, "Intrusion detection system based on snort," Lecture Notes in Electrical Engineering, vol. 272, pp. 657-664, 2014.##[2] Y. Meng and L.-F. Kwok, "Adaptive blacklist-based packet filter with a statistic-based approach in network intrusion detection," J. Netw. Comput. Appl., vol. 39, pp. 83-92, 2014.##[3] T. Ho, S. Cho, and S. Oh, "Parallel multiple pattern matching schemes based on cuckoo filter for deep packet inspection on graphics processing units," IET Information Security, vol. 12, pp. 381-388, 2018.##[4] C. Hung, P. Wu, H. Wang, and C. Lin, "Efficient Parallel Muti-pattern Matching Using GPGPU Acceleration for Packet Filtering," in 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, 2015, pp. 1843-1847.##[5] C.-L. Hung, C.-Y. Lin, and H.-H. Wang, "An efficient parallel-network packet pattern-matching approach using GPUs," Journal of Systems Architecture, vol. 60, pp. 431-439, 2014/05/01/ 2014.##[6] کیوان رحیمی‌زاده، محمدعلی ترکمانی، عباس دهقانی، "نگاشت چرخه McGraw به متدولوژی RUP برای توسعه نرم‌افزار امن"، پردازش علائم و داده‌ها، 1399 ، دوره 17، شماره 2، صفحات 33-46.##[6] K. RahimiZadeh, M. Torkamani, and A. Dehghani, "Mapping of McGraw Cycle to RUP Methodology for Secure Software Developing," Signal and Data Processing, vol. 17, pp. 33-46, 2020.##[7] K. Kendall, "A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems," Electrical Engineering and Computer Science, MASSACHUSETTS INSTITUTE OF TECHNOLOGY, MIT Lincoln, 1999.##[8] P. Innella and O. McMillan, An Introduction to Intrusion Detection Systems, 2001.##[9] P. Bunel, "An introduction to Intrusion Detection Systems," in Sans Security Essentials v1.4c, ed LONDON, 2004.##[10] P. Bunel, "Host- vs. Network-Based Intrusion Detection Systems," in Sans Security Essentials, ed, 2004.##[11] X. Luo, "Model design artificial intelligence and research of adaptive network intrusion detection and defense system using fuzzy logic," Journal of Intelligent & Fuzzy Systems, pp. 1-9.##[12] A. S. Almogren, "Intrusion detection in Edge-of-Things computing," Journal of Parallel and Distributed Computing, vol. 137, pp. 259-265, 2020.##[13] F. Erlacher and F. Dressler, "On high-speed flow-based intrusion detection using snort-compatible signatures," IEEE Transactions on Dependable and Secure Computing, 2020.##[14] A. Thakkar and R. Lohiya, "A review of the advancement in intrusion detection datasets," Procedia Computer Science, vol. 167, pp. 636-645, 2020.##[15] B. Caswell, J. Beale, and A. R Baker, Snort IDS and IPS Toolkit. Syngress Publishing, Inc. Elsevier, Inc.: Williams, Andrew 2007.##[16] R. Chi, Intrusion Detection System Based on Snort. China, 2014.##[17] M. Roesch, C. Green, and S. Team, SNORT Users Manual 2.9.9, 2016.##[18] S. Sharma and M. Dixit, "A Review on Network Intrusion Detection System Using Open Source Snort," vol. 9, pp. 61-70, 2016.##[19] C.-H. Lin, "Accelerating String Matching Algorithms on Multicore Processors."##[20] R. S. Boyer and J. S. Moore, "A fast string searching algorithm," Commun. ACM, vol. 20, pp. 762-772, 1977.##[21] A. V. Aho and M. J. Corasick, "Efficient string matching: an aid to bibliographic search," Commun. ACM, vol. 18, pp. 333-340, 1975.##[22] C. L. Hung, C. Y. Lin, H. h. Wang, and C. Y. Chang, "Efficient Packet Pattern Matching for Gigabit Network Intrusion Detection Using GPUs," in High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, 2012, pp. 1612-1617.##[23] C. S. Kouzinopoulos and K. G. Margaritis, "String Matching on a Multicore GPU Using CUDA," in Informatics, 2009. PCI '09. 13th Panhellenic Conference on, 2009, pp. 14-18.##[24] حامد صادقی، امیر اخوان بی تقصیر، "آشکارسازی سیگنال بر اساس پردازش موازی مبتنی بر جی‌پی‌یو در شبکه‌های حس‌گری صوتی دارای زیرساخت"، پردازش علائم و داده ها، 1396 ، دوره 14، شماره 4، صفحات 19-30.##[24] h. sadeghi and A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks," Signal and Data Processing, vol. 14, pp. 19-30, 2018.##[25] G. Vasiliadis, M. Polychronakis, and S. Ioannidis, "MIDeA: a multi-parallel intrusion detection architecture," presented at the Proceedings of the 18th ACM conference on Computer and communications security, Chicago, Illinois, USA, 2011.##[26] S. Soroushnia, M. Daneshtalab, T. Pahikkala, and J. Plosila, "Parallel Implementation of Fuzzified Pattern Matching Algorithm on GPU," in 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2015, pp. 341-344.##[27] N. P. Tran, M. Lee, S. Hong, and J. Choi, "High Throughput Parallel Implementation of Aho-Corasick Algorithm on a GPU," in Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2013 IEEE 27th International, 2013, pp. 1807-1816.##[28] G. Vasiliadis, M. Polychronakis, and S. Ioannidis, "Parallelization and characterization of pattern matching using GPUs," in Workload Characterization (IISWC), 2011 IEEE International Symposium on, 2011, pp. 216-225.##[29] C. H. Lin, C. H. Liu, L. S. Chien, and S. C. Chang, "Accelerating Pattern Matching Using a Novel Parallel Algorithm on GPUs," IEEE Transactions on Computers, vol. 62, pp. 1916-06, 20313.##[30] L. Vokorokos, M. Ennert, M. >Čajkovský, and J. Radušovský, "A Survey of parallel intrusion detection on graphical processors," Central European Journal of Computer Science, vol. 4, pp. 222-230, 2014.##[31] A. P. M.S., "Parallelizing a network intrusion detection system using a GPU," Master of Science, Computer Science and Engineering, Louisville, UK, 2012.##[32] G. Vasiliadis, S. Antonatos, M. Polychronakis, E. P. Markatos, and S. Ioannidis, "Gnort: High Performance Network Intrusion Detection Using Graphics Processors," in Recent Advances in Intrusion Detection: 11th International Symposium, RAID 2008, Cambridge, MA, USA, September 15-17, 2008. Proceedings, R. Lippmann, E. Kirda, and A. Trachtenberg, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 116-134.##[33] G. Vasiliadis, M. Polychronakis, S. Antonatos, E. P. Markatos, and S. Ioannidis, "Regular Expression Matching on Graphics Hardware for Intrusion Detection," in Recent Advances in Intrusion Detection: 12th International Symposium, RAID 2009, Saint-Malo, France, September 23-25, 2009. Proceedings, E. Kirda, S. Jha, and D. Balzarotti, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 265-283.##[34] M. A. Jamshed, J. Lee, S. Moon, I. Yun, D. Kim, S. Lee, et al., "Kargus: a highly-scalable software-based intrusion detection system," presented at the Proceedings of the 2012 ACM conference on Computer and communications security, Raleigh, North Carolina, USA, 2012.##[35] H. Song and J. W. Lockwood, "Efficient packet classification for network intrusion detection using FPGA," presented at the Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays, Monterey, California, USA, 2005.##[36] Y. Meng and L. f. Kwok, "Adaptive context-aware packet filter scheme using statistic-based blacklist generation in network intrusion detection," in Information Assurance and Security (IAS), 2011 7th International Conference on, 2011, pp. 74-79.##[37] M. Ramesh and H. Jeon, "Parallelizing Deep Packet Inspection on GPU," in 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), 2018, pp. 248-253.##[38] S. Hakak, A. Kamsin, P. Shivakumara, G. A. Gilkar, W. Z. Khan, and M. Imran, "Exact String Matching Algorithms: Survey, Issues, and Future Research Directions," IEEE Access, 2019.##[39] C.-L. Hung, T.-H. Hsu, H.-H. Wang, and C.-Y. Lin, A GPU-based Bit-Parallel Multiple Pattern Matching Algorithm, 2018.##[40] C. Thomas, V. Sharma, and N. Balakrishnan, Usefulness of DARPA dataset for intrusion detection system evaluation, 2008.##[41] P. P, M. T, and L. Raj, A Comparative Study on String Matching Algorithm of Biological Sequences, 2014.##[42] N. Tuck, T. Sherwood, B. Calder, and G. Varghese, Deterministic Memory-Efficient String Matching Algorithms for Intrusion Detection vol. 4, 2004.##[43] "Exact Matching: Classical Comparison-Based Methods," in Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, D. Gusfield, Ed., ed Cambridge: Cambridge University Press, 1997, pp. 16-34.##[44] C. H. Lin, "Accelerating String Matching Algorithms on Multicore Processors," vol. 2, pp. 52-59, 6 June- 2016 2016.##[45] J. Yu, Y. Xue, and J. Li, "Memory efficient string matching algorithm for network intrusion management system," Tsinghua Science and Technology, vol. 12, pp. 585-593, 2007.## ##