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ارائه روشی پویا جهت پاسخ به پرس‌وجوهای پیوسته تجمّعی اقتضایی Providing a Dynamic Technique for Answering Ad-hoc Continuous Aggregate 1 1 جریان‌های داده دنباله‌های نامتناهی، سریع، متغیر با زمان و با نرخ ورود انفجاری از نشان‌های داده هستند که به‌طورمعمول نیاز دارند به‌صورت برخط و به‌طورتقریبی بی‌درنگ پردازش شوند. بر این اساس، الگوریتم‌های پردازش جریان‌های داده و اجرای پرس‌وجوها روی جریان داده‌ها بیش‌تر تک‌گذره هستند. اجرای این الگوریتم‌های تک‌گذره با محدودیت‌ها و چالش‌هایی از قبیل محدودیت در حافظه، زمان‌بندی، و دقت پاسخ‌ها مواجه است. این چالش‌ها به‌ویژه در شرایطی که پرس‌وجوی مورد نظر از قبل تعیین و مشخص نشده باشد و به‌صورت اقتضایی، پس از ارسال جریان داده ارائه شود، به‌مراتب جد‌ی‌تر و حل آن‌ها دشوارتر خواهد بود. در این مقاله، برای پردازش پرس‌وجوهای تجمعی که به‌طور پیوسته روی جریان‌های داده اجرا خواهند شد و البته به‌طور اقتضایی ارائه می‌شوند، راه حلی مبتنی بر ساختار درختواره و نگهداشت نتایج تجمعی معرفی شده است.  نکته مهم در این روش، برقراری برخط بودن در تمام مراحل ساخت، نگهداری و بهره‌برداری از درخت است. برای تأمین برخط بودن فرایند پاسخ به پرس‌وجو، کافی است تمامی پاسخ‌های محتمل را نگهداری کنیم؛ اما برای حفظ برخط‌بودن فرایند ساخت و نگهداری درخت، با توجه به ویژگی‌های ذاتی جریان داده ناچاریم برخی پاسخ‌ها را نگهداری کنیم. بدین ترتیب، هدف و مسئله اساسی آن است که دست‌کم پاسخ‌های انتخابی برای ذخیره‌ در قالب درختواره را به مجموعه پاسخ‌های مورد نیاز برای پرس‌وجوهای اقتضایی رسیده نزدیک‌تر کنیم. ساختار درخت تجمعی پیشوندی پیشنهادی که به‌صورت پویا ایجاد، نگهداری، مدیریت و در پردازش پرس‌وجوها استفاده می‌شود، تشریح و صحت عملکرد آن به‌صورت عملی مورد ارزیابی قرار گرفته که نتایج حاکی از کارآمد‌بودن آن برای به‌کارگیری در پردازش برخط پرس‌وجوهای پیوسته تجمعی اقتضایی روی جریان‌های داده است.   2 Data Streams are infinite, fast, time-stamp data elements which are received explosively. Generally, these elements need to be processed in an online, real-time way. So, algorithms to process data streams and answer queries on these streams are mostly one-pass. The execution of such algorithms has some challenges such as memory limitation, scheduling, and accuracy of answers. They will be more important and serious, chiefly if the queries are not predefined but Ad-hoc, and also should be executed after data stream tuples are gone. Countinous aggregate queries are types of queries with some special characteristics making it possible to perform more specific, efficient qeury processing techniques, specifiaclly beneficient for ad-hoc ones. In this paper, a dynamic efficient techinque is proposed for answering the ad-hoc continiues aggregate queries over data streams. The main idea of the proposed technique is to generate and handle an efficiet tree data structure as the synopse, in the form of  Dynamic Prefix Aggregate Tree. In general, the two following approaches can be used to calculate any function such as ; either implementation of an algorithm for the calculation of function f, or storing the answers of function f for all possible states. When the algorithm runtime is high, the second method strengthened by proper selection of indices can return a proper answer in a very short time (even ). But the major problem of the second method is the total number of possible answers which can be very high and also can be out of the possible storage capacity and processing potential within a certain acceptable time period. For example, suppose that the cardinality of each of the parameters of  is 10. In this case, the total number of possible states will be . As it is evident, the total number of states increases with the number of parameters and their cardinalities.When the total number of states is so great that generating answers with respect to consumed time and space is impossible, a more convenient, practical method should be employed. This more practical approach can be the storing of some of the answers (selectively) with respect to the following conditions: Obtaining un-stored answers from the set of stored answers. Higher probability of utilizing stored answers (i.e. higher probability of submitting requests from stored set). Eliminating (not storing) null answers. The same idea can be implemented for online and almost real time processing of queries, so that by receiving each tuple, all possible answers get obtained and stored. By doing so, in the time of need (when answering to an ad-hoc query) stored answers will be used instead of calculating each answer. Accordingly, some answers are stored in a tree structure to be used at the right time. In this paper, in order to answer ad-hoc continuous aggregate queries over data streams, a method is proposed that uses a tree structure for storing the aggregate results. The important point in this method is that all steps of the construction, maintenance and using of the tree must be online. For these purposes, it is enough to keep all possible answers. But to apply an online construction and maintenance of tree, we must keep some answers, according to the inherent features of data streams. In this way, the main goal is to choose the answers possessing the most overlap with responses answers of received ad-hoc queries. The proposed method, creates the tree structure and maintains it dynamically to answer ad-hoc aggregate continuous queries over data streams. For this purpose, queries at instant  are modeled as in form of , where  or  (when , the aggregate over the whole sliding window is returned) and  is the size of sliding window and  (when , the aggregate over the whole  is returned). In order to increase the overlapping, a statistical task is performed on a dimensions of the received queries. In this way, dimensions are determined with the highest, lowest request. When , means that there is no request for this dimension. Therefore, we select and store the answers related to the dimension with highest request, and ignore those with the lowest. Obviously, these answers should be obtained and presented using stored answers. As the request for dimensions may change, the tree structure must be dynamically constructed and maintenance that will be presented this dynamic structure in this paper. Experimental evaluattion of the proposed method shows that, using the proposed Dynamic Aggregate Tree for ansering countinous Ad-hoc aggregate queies is more cost-effective, in terms of response time and memory usage. 3 22 2016/02/9 1394/11/20 2016/10/29 1395/8/8 مهدی مسافری شرکت جویا افزار ماندگار پرسیا ایران st_m_mosaferi@az.ac.ir علی‌اصغر صفائی Ali Safaei دانشگاه تربیت مدرس ایران aa.safaei@modares.ac.ir Data Stream Continuous ad-hoc aggregate queries Dynamic Prefix Tree Aggregate cell پرس‌وجوی پیوسته تجمعی اقتضایی جریان داده درخت پیشوندی پویا سلول تجمعی [1] M. Chen, S. Mao and Y. Liu, "Big Data: A Survey," Mobile Networks and Applications, vol. 19, no. 2, pp. 171-209, April 2014.## [2] C. L. Philip Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data," Elsevier, vol. 275, pp. 314-347, January 2014.## [3] R. Motwani, J. Widom, A. Arasu, B. Babcock, S. Babu, M. Datar, G. Manku, C. Olston, J. Rosenstein and R. Varma, "Query Processing, Resource Management, and Approximation in a Data Stream Management System," in Proceedings of the 2003 CIDR Conference, Asilomar, 2003.##[4] R. Sabbagh Go and N. Daneshpour, "An Improved View Selection Algorithm in Data Warehouses by Finding Frequent Queries," JSDP, vol. 14, no. 1, pp. 29-40, 2017. ##[5] M. Cho, J. Pei and K. Wang, "Answering ad hoc aggregate queries from data streams using prefix aggregate trees," Knowledge and Information Systems, vol. 12, no. 3, pp. 301 - 329, August 2007. ##[6] C. J. Hahn, S. G. Warren and R. Eastman, "Extended Edited Synoptic Cloud Reports from Ships and Land Stations Over the Globe," 1952-2009. [Online]. Available: http://cdiac.ornl.gov/epubs/ndp/ndp026c/ndp026c.html.##[7] K. Beyer and R. Ramakrishnan, "Bottom-up computation of sparse and Iceberg CUBE," in SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data, New York, 1999. ##[8] K. A. Ross and D. Srivastava, "Fast Computation of Sparse Datacubes," in VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases, San Francisco, 1997. ##[9] Y. Sismanis, A. Deligiannakis, N. Roussopoulos and Y. Kotidis, "Dwarf: Shrinking the PetaCube," in SIGMOD '02 Proceedings of the 2002 ACM SIGMOD international conference on Management of data, New York, 2002. ##[10] W. Wang, J. Feng, H. Lu and J. Yu, "Condensed cube: an effective approach to reducing data cube size," in 18th International Conference on Data Engineering, San Fransisco,CA, 2002. ##[11] J. Li, D. Maier, K. Tufte, V. Papadimos and P. A. Tucker, "Semantics and evaluation techniques for window aggregates in data streams," in International Conference on Management of Data, New York, 2005. ##[12] S. Madden, M. J. Franklin, J. M. Hellerstein and W. Hong, "TAG: a Tiny AGgregation Service for Ad- Hoc Sensor Networks," ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation, vol. 36, no. SI, pp. 131-146, 2002. ##[13] J. Considine, F. Li, G. Kollios and J. Byers, "Approximate aggregation techniques for sensor databases," in ICDE '04 Proceedings of the 20th International Conference on Data Engineering, Washington, DC, USA, 2004.## [14] C. Intanagonwiwat, R. Govindan and D. Estrin, "Directed diffusion: a scalable and robust communication paradigm for sensor networks," in MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking, New York, NY, USA, 2000.## [15] Y. Yao and J. Gehrke, "The cougar approach to in-network query processing in sensor networks," ACM SIGMOD Record, vol. 31, no. 3, pp. 9-18, September 2002. ##[16] Y. Yao and J. Gehrke, "Query Processing for Sensor Networks," Asilomar, 2003. ##[17] J. Zhao, R. Govindan and D. Estrin, "Computing Aggregates for Monitoring Wireless Sensor Networks," in SNPA, 2003.## [18] S. Madden, M. J. Franklin, J. M. Hellerstein and W. Hong, "The Design of an Acquisitional Query Processor for Sensor Networks," in International Conference on Management of Data, New York, NY, USA, 2003.## [19] A. A. Safaei, M. Haghjoo and F. Abdi, "Semantic Cache Schema for Query Processing in Mobile Databases," in IEEE ICDIM’08, London, UK, 2008. ##[20] A. A. Safaei, K. Mehran, M. Haghjoo and K. Izadi, "Caching Intermediate Results for Multiple-Query Optimization," in 5th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA-2007), Jordan, 2007. ##[21] P. Flajolet and G. N. Martin, "Probabilistic counting algorithms for data base applications," Journal of Computer and System Sciences, vol. 31, no. 2, pp. 182 - 209, 1985. ##[22] N. Alon, Y. Matias and M. Szegedy, "The space complexity of approximating the frequency moments," JOURNAL OF COMPUTER AND SYSTEM SCIENCES, vol. 58, no. 1, pp. 137-147, February 1999. ##[23] Z. Bar-yossef, T. S. Jayram, R. Kumar, D. Sivakumar and L. Trevisan, "Counting Distinct Elements in a Data Stream," in Randomization and Approximation Techniques in Computer Science, Springer-Verlag, 2002, pp. 1-10.##[24] G. Cormode, M. Datar, P. Indyk and S. Muthukrishnan, "Comparing data streams using hamming norms (how to zero in)," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 529-540, March 2003.## [25] P. Flajolet, "On Adaptive Sampling," Computing , vol. 43 , no. 4, pp. 391 - 400, February 1990. ##[26] S. Ganguly, M. Garofalakis and R. Rastogi, "Processing set expressions over continuous update streams," in MOD International Conference on Management of Data, New York, NY, USA, 2003. ##[27] P. B. Gibbons and S. Tirthapura, "Estimating simple functions on the :::::union::::: of data streams," in SPAA '01 Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures, New York, NY, USA, 2001.## [28] F. Dehne, Q. Kong, A. Rau-Chaplin, H. Zaboli and R. Zhou, "Scalable real-time OLAP on cloud architectures," Parallel and Distributed Computing, Vols. 79-80, pp. 31-41, May 2015. ##[29] A. A. Safaei, "Real-time Processing of Streaming Big Data," Journal of Real-Time Systems, August 2016. ##[30] B. Babcock, S. Babu, M. Datar, R. Motwani and J. Widom, "Models and issues in data stream systems," in twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database, New York, 2002. ##[31] A. Arasu, B. Babcock, S. Babu, J. Cieslewicz, K. Ito, R. Motwani, U. Srivastava and J. Widom, "Stream: The stanford data stream management system," Stanford InfoLab, 2004.##[32] D. J. DeWitt and J. Gray, "Parallel Database Systems: The Future of High Performance Database Processing," Communications of the ACM, vol. 36, no. 6, pp. 85-98, June 1992.## [33] A. A. Safaei and M. Haghjoo, "Dispatching of Stream Operators in Parallel Execution of Continuous Queries," Supercomputing, vol. 61, pp. 619-641, 2012.## [34] A. A. Safaei and M. Haghjoo, "Parallel processing of continuous queries over data streams," Distributed and Parallel Databases, vol. 28, no. 2, pp. 93-118, 2010.##[35] Safaei, A. A., and M. S. Haghjoo. "Parallel processing of data streams." J Comput Sci Eng 11.2 (2014): 11-29## [4] صباغ‌گل ریحانه، دانشپور نگین. بهبود الگوریتم انتخاب دید در پایگاه داده تحلیلی با استفاده از یافتن پرس‌جوهای پرتکرار. پردازش علائم و داده‌ها. 1396؛ 14 (1): 29-40.## [35]ع. ا. صفایی و م. حق جو, “پردازش موازی جریان داده ها,” نشریه علمی پژوهشی انجمن کامپیوتر ایران, جلد 11, شماره 1 (الف), pp. 11-29, 1392. ## ##

ریسک سنج: ابزاری برای سنجش دقیق میزان ریسک امنیتی برنامه‌ها در دستگاه‌های همراه RiskMeter: A Tool for Measuring Precise Security Risk Values of Mobile Device Applications 1 1 حفظ امنیت دستگاه‌های همراه به دلیل نگهداری اطلاعات شخصی و کاری برای کاربران آنها بسیار حائز اهمیت است. نصب برنامه‌های جدید و ناشناخته روی این دستگاه‌ها ممکن است، منجر به آسیب‌های امنیتی شود؛ بنابراین محاسبه ریسک امنیتی برنامه‌ها در انجام تصمیم‌گیری درست در انتخاب نرم‌افزار، به کاربران می‌تواند کمک کند. در برخی از سیستم عامل‌های دستگاه‌های همراه، ریسک امنیتی برنامه‌ها از طریق مجوزهایی که درخواست می‌کنند قابل اندازه‌گیری است. در این مقاله، ابزار نرم‌افزاری جدیدی به‌منظور سنجش میزان ریسک امنیتی برنامه‌ها در دستگاه‌های همراه طراحی و پیاده‌سازی شده است. این ابزار از یک معیار جدید به‌منظور اندازه‌گیری ریسک بهره می‌برد. ما به‌منظور ارائه این معیار، مجوزهای درخواستی توسط ده‌ها بدافزار و صدها برنامه تلفن همراه را بررسی و تحلیل کرده‌ایم. علاوه‌بر این، به‌منظور ارزیابی دقیق‌تر، مجموعه داده‌های جدیدی از برنامه‌های ارائه‌شده در فروشگاه‌های داخلی و بدافزارهای جدید را گردآوری کرده‌ایم. آزمایش‌های صورت‌گرفته بر روی بدافزارها و نرم‌افزارهای بی‌خطر شناخته‌شده، نشان‌دهنده دقت روش ارائه‌شده نسبت به معیارهای ارائه‌شده قبلی از نظر تخصیص ریسک امنیتی بالا به بدافزارها و ریسک پایین به نرم‌افزارهای بی‌خطر است.   2 Nowadays smartphones and tablets are widely used due to their various capabilities and features for end users. In these devices, accessing a wide range of services and sensitive information including private personal data, contact list, geolocation, sending and receiving messages, accessing social networks and etc. are provided via numerous application programs. These types of accessibilities, functionalities, and facilities make privacy and security issues more critical. Therefore, traditional security mechanism including biometric authentication, data encryption, access control, and etc. are not adequate. Therefore, danger of installing and using malwares must be taken into account in order to provide practical security for end users. Installing new and unknown applications on these devices might lead to security threats. Recently, smartphones and tablets utilize powerful operating system in which security of application is provided by application permissions. Android and BlackBerry are two examples of operating systems which reduce attack surface by using application permissions. In these operating systems, in order to perform malicious activities, an attacker must deceive users to install a malicious app since other ways of intrusion are almost closed. Recent statistics show that Android is the most popular operating system. For installing an app, Android requires the user to grant privileges through the requested permissions. There is a large number of applications (Apps) developed for this operating system which require various permissions based on their functionalities and provided services. Therefore, measuring security risks of applications can help us to make better decision regarding to apps installation and removal. There exists some research regarding to enhance the Android security model and its security risk communication mechanism. In this mobile operating system, security risk values of applications can be computed using their requested permissions. In this study, a new software tool is designed and implemented to measure security risk values of mobile applications. This tool benefits from a new metric to compute the risk values. This risk metric exploits statistics of permission usages in known malwares and goodwares. However, they can be simply extended to other features of Android apps including static and dynamic ones. Moreover, we have attempted to give a better definition of permission criticality to aim users for making best decision in new apps installation or previously installed ones removal. In fact, we have designated a new formulation to assign higher risk values to permissions with a higher usage in malwares and very lower usage in benign apps. The idea is quite simple but produces interesting results. That is, the security risk of a permission is directly related to the difference of its usage in malicious and non-malicious apps. Given risk values of permissions, one can compute risk of an Android app based on its permission list. Since the proposed measurement compute the risk values of permissions according to simple statistics of known malwares and useful Android apps, they have good explainability. Users can be informed regarding to danger about approving risky permissions and they can make reasonable decisions based on total risk score of an app which can be simply computed using security risks of its requested permissions. In order to purpose the metric, we have analyzed requested permissions of large number of malicious and ordinary applications. Moreover, for realistic evaluations, we have constructed two new datasets of applications belonging to an Iranian market and new malwares. Experimental evaluations on real known malwares and benign apps reveal the superiority of the proposed criterion with respect to previously proposed method in terms of assigning higher risk values to malwares and lower risk values to the benign applications.     23 36 2016/02/92015/12/30 1394/10/9 2016/10/292017/03/5 1395/12/15 محمود دی پیر Mahmood Deypir دانشگاه علوم و فنون هوایی شهید ستاری ایران mdeypir@gmail.com Security of mobile devices Security risk Malwares Permissions RiskMeter امنیت تلفن همراه ریسک امنیتی بدافزار مجوزهای امنیتی ریسک‌سنج [1] لسانی، فاطمه سادات، فتوحی، قزوینی فرانک، دیانت، روح الله، "لب‌خوانی: روش جدید احراز هویت در برنامه‌های کاربردی گوشی‌های تلفن همراه اندروید"، مجله پردازش علائم و داده ها، جلد ۱۴، شماره ۱، صفحات ۳-۱۴، 1396.##[1] F. Sadat Lesani, F. Fotouhi Ghazvini, and R. Dianat, “ Lip Reading: a New authentication method in Android mobile phone’s applications,” Journal of Signal and Data Processing, vol. 14, no. 1, pp. 3-14, 2017.##[2] A. Armando, A. Merlo, and L. Verderame, “Security considerations related to the use of mobile devices in the operation of critical infrastructures,” International Journal of Critical Infrastructure Protection, vol. 7, no. 4, pp. 247–256, 2014. ##[3] Y. Au, K. Wain, Y. F. Zhou, Z. Huang, and D. Lie, “PScout : Analyzing the Android Permission Specification,” in CCS ’12 Proceedings of the 2012 ACM conference on Computer and communications security, 2012, pp. 217–228. ##[4] D. Barrera, H. G. üne ş Kayacik, P. C. van Oorschot, and A. Somayaji, “A methodology for empirical analysis of permission-based security models and its application to android,” in Proceedings of the 17th ACM conference on Computer and communications security - CCS ’10, 2010, p. 73. ##[5] I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani, “Crowdroid: Behavior-Based Malware Detection System for Android,” in Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices - SPSM ’11, 2011, p. 15.##[6] L. Cen, C. S. Gates, L. Si, and N. Li, “A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code,” IEEE Transactions on Dependable and Secure Computing, vol. 12, no. 4, pp. 400–412, 2015. ##[7] S. Chakradeo, B. Reaves, and W. Enck, “MAST: Triage for Market-scale Mobile Malware Analysis,” in ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), 2013, pp. 13–24. ##[8] E. Chin, A. P. Felt, V. Sekar, and D. Wagner, “Measuring user confidence in smartphone security and privacy,” in Proceedings of the Eighth Symposium on Usable Privacy and Security - SOUPS ’12, 2012, p. 1. ##[9] M. Christodorescu, S. Jha, and C. Kruegel, “Mining specifications of malicious behavior,” in Proceedings of the 1st conference on India software engineering conference - ISEC ’08, 2008, p. 5. ##[10] A. Desnos, “Android: Static analysis using similarity distance,” in Proceedings of the Annual Hawaii International Conference on System Sciences, 2011, no. X, pp. 5394–5403. ##[11] W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A Study of Android Application Security.,” in USENIX Security, 2011, vol. 39, no. August, pp. 21–21. ##[12] W. Enck, M. Ongtang, and P. McDaniel, “On lightweight mobile phone application certifica-tion,” in Proceedings of the 16th ACM conference on Computer and communications security - CCS ’09, 2009, p. 235. ##[13] A. Felt, K. Greenwood, and D. Wagner, “The effectiveness of application permissions,” in WebApps ’11: 2nd USENIX Conference on Web Application Development, 2011, pp. 75–86. ##[14] A. Felt, E. Ha, S. Egelman, and A. Haney, “Android permissions: User attention, comprehension, and behavior,” in Proc. of SOUPS, 2012, pp. 1–14. ##[15] C. S. Gates, J. Chen, N. Li, and R. W. Proctor, “Effective risk communication for Android apps,” IEEE Transactions on Dependable and Secure Computing, vol. 11, no. 3, pp. 252–265, 2014. ##[16] C. S. Gates, N. Li, H. Peng, B. Sarma, Y. Qi, R. Potharaju, C. Nita-Rotaru, and I. Molloy, “Generating summary risk scores for mobile applications,” IEEE Transactions on Dependable and Secure Computing, vol. 11, no. 3, pp. 238–251, 2014. ##[17] P. G. Kelley, S. Consolvo, L. F. Cranor, J. Jung, N. Sadeh, and D. Wetherall, “A conundrum of permissions: Installing applications on an android smartphone,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7398 LNCS, pp. 68–79. ##[18] P. G. Kelley, L. F. Cranor, and N. Sadeh, “Privacy as part of the app decision-making process,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’13, 2013, p. 3393. ##[19] H. Peng, C. Gates, B. Sarma, N. Li, Y. Qi, R. Potharaju, C. Nita-Rotaru, I. Molloy, and I. 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جداسازی طیفی با استفاده از الگوریتم HYCA بهبودیافته Spectral Unmixing Using Improved HYCA Algorithm 1 1 تصویربرداری ابرطیفی ابزاری مهم در کاربردهای سنجش از دور به‌شمار می­رود. حس‌گرهای ابرطیفی، نور منعکس‌شده از سطح زمین را در صدها و یا هزاران باند طیفی اندازه­گیری می‌کنند. در بعضی از کاربردها، بی­درنگ نیاز به داشتن تصویر در سطح زمین داریم که لازمه این موضوع، وجود پهنای باند زیاد بین حس‌گر و ایستگاه زمینی است. در بیش‌تر مواقع، پهنای باند ارتباطی بین ماهواره و ایستگاه زمینی کاهش می­یابد و این امر، ما را مستلزم به استفاده از یک روش فشرده­سازی می­کند. علاوه‌بر حجم بالای داده، مشکل دیگر در این تصاویر، وجود پیسکل­های آمیخته است. تجزیه و تحلیل پیکسل­های آمیخته یا جداسازی طیفی، تجزیه پیکسل‌های آمیخته به مجموعه­ای از اعضای پایانی و فراوانی­های کسری آن­هاست. به­دلیل بالا‌بودن این حجم و به تبع آن، دشواربودن پردازش و تجزیه و تحلیل مستقیم این اطلاعات و البته قابل فشرده‌بودن این تصاویر، در سال­های اخیر روش­هایی تحت عنوان «حس‌گری­فشرده و جداسازی» معرفی شده است. الگوریتم HYCA یکی از الگوریتم‌هایی است که با توجه به ویژگی‌های ذاتی تصاویر، سعی در فشرده­سازی این تصاویر کرده است. یکی از ویژگی‌های بارز این الگوریتم، سعی در استفاده از اطلاعات مکانی به‌منظور بازسازی بهتر داده‌ها است. در این پژوهش، روشی مطرح شده است که علاوه‌بر اطلاعات مکانی، از اطلاعات طیفی (پیکسل­های غیرهمسایه) موجود در تصاویر، آن هم به‌صورت بی‌درنگ استفاده کند. برای اضافه‌کردن اطلاعات غیر از پیکسل­های همسایه، یک روش بخش­بندی بی­درنگ معرفی شده است که برای بخش­بندی درست، میزان شباهت پیکسل­ها در نظر گرفته می­شود و شکل حاصله در هر بخش محدود به هیچ شکل هندسی خاصی نمی­شود. برای ارزیابی میزان کارآیی روش پیشنهادی، در بخش نتایج از هر دو داده ابرطیفی ساختگی و واقعی استفاده شده است. علاوه بر آن، نتایج کار با یک سری روش­های سنتی در این حوزه مقایسه شده است. نتایج به‌دست آمده حاکی از کارآیی بالای روش پیشنهادی در معیار NMSE تا  برای داده ساختگی و  برای داده واقعی است.   2 Hyperspectral (HS) imaging is a significant tool in remote sensing applications. HS sensors measure the reflected light from the surface of objects in hundreds or thousands of spectral bands, called HS images. Increasing the number of these bands produces huge data, which have to be transmitted to a terrestrial station for further processing. In some applications, HS images have to be sent instantly to the station requiring a high bandwidth between the sensors and the station. Most of the time, the bandwidth between the satellite and the station is narrowed limiting the amount of data that can be transmitted, and brings the idea of Compressive Sensing (CS) into the minds. In addition to the large amount of data, in these images, mixed pixels are another issue to be considered. Despite of their high spectral resolution, their spatial resolution is low causing a mixture of spectra in each pixel, but not a pure spectrum. As a result, the analysis of mixed pixels or Spectral Unmixing (SU) technique has been introduced to decompose mixed pixels into a set of endmembers and abundance fraction maps. The endmembers are extracted from spectral signatures related to different materials, and the abundance fractions are the proportions of the endmembers in each pixel. In recent years, due to the large amount of data and consequently the difficulties of real-time signal processing, and also having the ability of image compression, methods of Compressive Sensing and Unmixing (CSU) have been introduced. Two assumptions have been considered in these methods: the finite number of elements in each pixel and the low variation of abundance fractions. HYCA algorithm is one of the methods trying to compress these kinds of data with their inherent features. One of the sensible characteristics of this algorithm is to utilize spatial information for better reconstruction of the data. In fact, HYCA algorithm splits the data cube into non-overlapping square windows and assumes that spectral vectors are similar inside each window. In this study, a real-time method is proposed, which uses the spectral information (non-neighborhood pixels) in addition to the spatial information. The proposed structure can be divided into two parts: transmitting information into the satellites and information recovery into the stations. In the satellites, firstly, to utilize the spectral information, a new real-time clustering method is proposed, wherein the similarity between the entire pixels is not restricted to any specific form such as square window. Figure 3 shows a segmented real HS image. It can be seen that the considering square form limits the capability of the HYCA algorithm and the similarity can be found in the both neighborhood and non-neighborhood pixels. Secondly, to utilize similarity in each cluster, different measurement matrices are used. By doing this, various samples can be achieved for each cluster and further information are extracted. On the other hand, usage of different measurement matrices may affect the system stability. As a matter of fact, generating the different measurement matrices is not simple and increases complexity into the transmitters. Therefore, it conflicts with the aim of CS theory, reducing complexity into the transmitters. As a result, in the proposed method, the number of the clusters is determined by the number of the producible measurement matrices. Figure 4 shows the schematic of the proposed structure in the satellites. In the stations, we follow HYCA procedure in equation 8 and 9, but the different similar pixels are applied to the both equations. By doing this, we reach to the improved HYCA algorithm. Finally, the proposed structure is shown in the Table 1. To evaluate the proposed method, both real and simulated data have been used in this article. In addition, normalized mean-square error is considered as an error criteria. For the simulated data, in constant measurement sizes, the effects of the additive noise, and for real data, the effects of measurement sizes have been investigated. Besides, the proposed method has been compared with HYCA and C-HYCA and some of the traditional CS based methods. The experimental results show the superiority of the proposed method in terms of signal to noise ratios and the measurement sizes, up to  in the simulated data and  in the real data, which makes it suitable in the real-world applications.   37 50 2016/02/92015/12/302015/05/19 1394/2/29 2016/10/292017/03/52016/10/29 1395/8/8 فرشید خواچه راینی Farshid Khajeh Rayeni دانشگاه تربیت مدرس ایران farshid.khajehrayeni@modares.ac.ir محمد حسن قاسمیان Hassan Ghassemian دانشگاه تربیت مدرس ایران ghassemi@modares.ac.ir Compressive Sensing (CS) HYCA algorithm hyperspectral imaging spatial and spectral information spectral unmixing اطلاعات طیفی و مکانی الگوریتم HCYA تصاویر ابرطیفی جداسازی طیفی حس‌گری‌فشرده [1] ف. خواجه‌راینی و ح. قاسمیان، "جداسازی طیفی با استفاده از الگوریتم حریص، کنفرانس مهندسی برق، 1394، دوره 23."##[1] F. KhajehRayeni and H. Ghassemian, "Spectral unmixing using greedy algorithm", 23th Internat-ional Conference on Elelctrical Engineer-ing (ICEE), 2015.##[2] H. Ghassemian, "A review of remote sensing image fusion methods," Information Fusion, vol. 32, pp. 75-89, 2016.##[3] N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE signal processing magazine, vol. 19, pp. 44-57, 2002.##[4] R. Rajabi and H. Ghassemian, "Spectral unmixing of hyperspectral imagery using multilayer NMF," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 38-42, 2015.##[5] H. Ghassemian and D. Landgrebe, "Multispectral image compression by an on-board scene segmentation," in Geoscience and Remote Sens-ing Symposium, 2001. IGARSS'01. IEEE 2001 International, 2001, pp. 91-93.##[6] F. Kowkabi, H. Ghassemian, and A. Keshavarz, "Enhancing hyperspectral endmember extraction using clustering and oversegmentation-based preprocessing," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, pp. 2400-2413, 2016.##[7] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 2014-2039, 2011.##[8] S. K., "Introduction to Data Compression," Elsevi-er, 2006.##[9] قاسمیان حسن، حسینی سید ابوالفضل. استخراج ویژگی در تصاویر ابرطیفی به‌کمک برازش منحنی با توابع گویا. پردازش علائم و داده‌ها. 1395؛ 13 (3): 3-16.##[9] H. Ghassemian and S. A. Hosseini, "Hyper-Spectral Data Feature Extraction Using Rational Function Curve Fitting," Signal and Data Process-ing, vol. 13, No 3, pp. 3-16, 2016.##[10] E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruc-tion from highly incomplete frequency informa-tion," IEEE Transactions on information theory, vol. 52, pp. 489-509, 2006.##[11] E. J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," IEEE transactions on information theory, vol. 52, pp. 5406-5425, 2006.##[12] D. L. 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ارائه روشی ترکیبی برای افزایش دقت پیش‌بینی در کاهش داده با استفاده از مدل مجموعه راف و هوش تجمعی A New Hybrid Method to Increase the Prediction in Data Reduced Using Rough Set and Swarm Intelligence Model 1 1 نیاز به طراحی سامانه‌هایی که قادر به اکتشاف سریع اطلاعات مورد علاقه کاربران با تاکید بر کمینه مداخله انسانی باشند از یک سو و روی‌آوردن به روش‌های تحلیل متناسب با حجم داده‌های حجیم ازسوی دیگر، در دنیای امروزی به‌خوبی احساس می‌شود. از‌این‌رو بهره‌گیری از قدرت فرآیند داده‌کاوی جهت شناسایی الگوها و مدل‌ها و نیز ارتباط عناصر مختلف در پایگاه داده جهت کشف دانش نهفته در داده‌ها روز‌به‌روز ضروری‌تر می‌شود. از سوی دیگر تئوری مجموعه راف را می‌توان به‌عنوان یک ابزار برای کشف وابستگی داده‌ها و کاهش خصیصه‌های موجود در یک مجموعه داده، تنها با استفاده از داده‌ها و بدون نیاز به اطلاعات اضافی برشمرد. در این پژوهش جهت بهبود روند انتخاب ویژگی‌های اصلی و بهبود تئوری مجموعه راف، از ترکیب الگوریتم مورچگان و تئوری مجموعه راف جهت یافتن زیرمجموعه ویژگی‌های اصلی و حذف اطلاعات غیر مفید با از دست رفتن کمینه اطلاعات استفاده شده است. نتایج حاصل از این ترکیب در ارزیابی داده‌های قیمت نفت نشان می دهد که ترکیب الگوریتم مورچگان و تئوری مجموعه راف در انتخاب ویژگی های مفید و بهینه، عملکرد مناسب‌تری نسبت به مدل های اخیر دارد.   2 Designing a system with an emphasis on minimal human intervention helps users to explore information quickly. Adverting to methods of analyzing large data is compulsory as well. Hence, utilizing power of the data mining process to identify patterns and models become more essential from aspect of relationship between the various elements in the database and discover hidden knowledge. Therefore, Rough set theory can be used as a tool to explore data dependencies and reducing features outlined in a data set. The main purpose of the rough theory is to obtain approximate concepts of acquired data. This theory is a powerful mathematical tool for arguing in ambiguous and indeterminate terms that provides methods for remove and reduce unrelated or excessive knowledge information on the data sets. This process of data reduction is based on the main task of the system, and without losing the basic data of the data sets. Rough set theory can play a very effective role to support decision-making systems, but in some cases, with increasing data volumes, there are inconsistent or collisional results which using swarm intelligence-based methods can choose the best of the contradictory, effectless or dummy data. This will bring interesting, unexpected and valuable structures from within a wide range of data. Since the ant colony optimization compares all the exploratory paths generated by each ant and the best route is selected from the existing paths, so considering the improvement of the selecting the main features and improving the theory of the Rough set, paths are not eliminated from the possible paths. In this research, the combination of the ant colony optimization and rough set theory have been used to find the subset of the main features and to delete the inappropriate information with the loss of the minimum information. This research will improve the features reduction technique employment Rough set theory and ant colony optimization. The gist of this research is removing useless information with minimal information loss. The results on petroleum prices data evaluation demonstrate that the hybrid method is more efficient than recent methods.   51 64 2016/02/92015/12/302015/05/192015/11/21 1394/8/30 2016/10/292017/03/52016/10/292016/10/29 1395/8/8 الهه میرزائی elahe mirzaee دانشگاه آزاد اسلامی واحد همدان ایران esmaeilpour@iauh.ac.ir منصور اسماعیل پور mansour esmaeilpour دانشگاه آزاد اسلامی واحد همدان ایران ma.esmaeilpour@gmail.com Rough Set Theory Swarm Intelligence Ant Colony Optimization Feature Reduction تئوری مجموعه‌های راف هوش تجمعی الگوریتم مورچگان انتخاب ویژگی پیش‌بینی [1] J. Wang, “Reduction Algorithms Based on Discernibility Matrix: The Ordered Attributes Method,” Journal of Computer Science & Technology, Vol. 16, No. 6, pp. 489–504, 2001.##[2] H. Nakayama, Y. Hattori and R. 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روش جدید متن‌کاوی برای استخراج اطلاعات زمینه کاربر به‌منظور بهبود رتبه‌بندی نتایج موتور جستجو A Novel Text Mining Method for User Context Extraction to Improve Search Engine Results Ranking 1 1 یکی از بزرگ‌ترین مشکلات پیش­روی موتورهای جستجو، رفع ابهاماتی است که در جستار کاربران وجود دارد. این ابهامات می­تواند دلایل متعددی داشته باشد که از جمله آنها تعدد معانی و مفاهیم مرتبط با یک جستار یا کاربردهای مختلف آن جستار است. اگر موتور جستجو نتواند این ابهام را به شکل صحیح برطرف کند، در ارائه نتایج خود به کاربر دچار اختلال و خطا خواهد  شد و نیاز کاربر را برطرف نخواهد کرد. این موضوع نقش مهمی در تعیین میزان کارایی موتور جستجو خواهد داشت. در این مقاله هدف آن است تا با جمع‌آوری اطلاعات زمینه کاربر در طول زمان، به تفسیر جستار کاربر کمک کرده و درنتیجه آن رتبه‌بندی نتایج موتور جستجو را بهبود بخشیم. زمینه کاربر به هر اطلاعاتی گفته می­شود که به شناخت ویژگی­ها و خصوصیات کاربر کمک کند. در این مقاله متن صفحات وبی که کاربر از آن‌ها بازدید می­کند، مورد پردازش قرار می­گیرند تا مفاهیم اصلی و کلیدی آن‌ها استخراج شود. استخراج این مفاهیم (زمینه کاربر) که در سمت کاربر و بر روی سیستم وی اتفاق خواهد افتاد، با افزونه­ای خواهد بود که به همین منظور تولید و بر روی مرورگر نصب می­شود؛ سپس زمینه کاربر، در ساختاری خاص در سمت کاربر و برای هر کاربر به‌صورت خصوصی نگهداری می­شوند. هنگامی که جستجویی انجام می­شود (با توجه به خلاصه­ای که موتور جستجو در ازای معرفی هر پیوند ارائه می­دهد)، میزان شباهت نتایج موتور جستجو با ­زمینه کاربر مورد محاسبه قرار گرفته و به‌ازای هر نتیجه میزان شباهت آن با زمینه کاربر محاسبه می‌شود؛ سپس آن نتایجی به کاربر پیشنهاد می‌شوند (در مرورگر پررنگ‌ می­شوند) که با ­زمینه وی تطبیق بیشتری داشته باشند. همان‌طور‌که از نتایج آزمایش‌های پایان مقاله مشهود است، استفاده از زمینه کاربر در رتبه‌بندی نتایج موتور جستجو تاثیر قابل توجهی دارد. بررسی­ها نشان می­دهد که در ارائه 10 نتیجه اول مربوط به 30 جستار دارای ابهام، به طور میانگین روش پیشنهادی 43% و موتور جستجوی گوگل 16% از نتایج خود را مرتبط با مفهوم اصلی جستار مورد نظر ارائه کرده­اند.   2 Today, the importance of text processing and its usages is well known among researchers and students. The amount of textual, documental materials increase day by day. So we need useful ways to save them and retrieve information from these materials. For example, search engines such as Google, Yahoo, Bing and etc. need to read so many web documents and retrieve the most similar ones to the user query. In this example, necessity of real time ability should be mentioned. Keyphrase extraction and some other fields like Information extraction, natural language processing, text summarization, query understanding, machine translation, and text similarity are subsets of text processing. So many efforts in text processing have been established, but there are still many open problems, especially in semantically document understanding subjects. Although these subjects seem not to be very hard for humankind but they are very complex and confusing for a computer, because there is no standard structure to save documents so that computers be able to extract semantics and contents. Document understanding and keyphrase extraction are some of the most important text processing goals. Many statistical and linguistic approaches are proposed in order to address these complex goals. Some methods work based on multi documents and some others on single document which all are generally more difficult than multi documents methods. Some methods use learning algorithms with training data and some others do not. Using natural language processing tools or resources -like ontologies- are effective ways to improve results, but these tools are not reliable for all languages. There are some articles for keyphrase extraction based on co-occurrence and also some statistical methods. Moreover, sometimes it is an important feature for a method to make real time outputs. Based on these characteristics, many approaches have been proposed in the literature. In this paper, we present a new approach for keyphrase extraction from a single document. We present a language-independent approach based on combination of statistical information extracted from document and some logical rules named fundamental text rules. In this approach, there is no need to any natural language processing, nor to ontology and nor to any document corpus. We illustrate a real time method to understand each document focuses by extracting its phrases from segmented document without using any learning algorithm. Then, the Score for each phrase is calculated based on its occurrence and its related phrases occurrences. Then, fundamental text rules omit some phrases based on their scores and their places in text. Remained phrases shows the document focuses. Evaluation shows that our approach takes a high recall and precision in key phrase extraction with very good accuracy in text focuses understanding. These keyphrases extracted of a text presents the most important concepts of that text and it is used to retrieve documents in search engines more efficiently.   65 82 2016/02/92015/12/302015/05/192015/11/212015/12/25 1394/10/4 2016/10/292017/03/52016/10/292016/10/292016/07/24 1395/5/3 جواد داودی مقدم دانشگاه صنعتی خواجه نصیرالدین طوسی ایران j.davoudi@mail.kntu.ac.ir علی احمدی Ali Ahmadi دانشکده مهندسی کامپیوتر ، دانشگاه صنعتی خواجه نصیرالدین طوسی ایران ahmadi@eetd.kntu.ac.ir text mining information retrieval user context search engine results ranking متن‌کاوی بازیابی اطلاعات زمینه کاربر رتبه بندی نتایج موتور جستجو [1] Hamdi, Mohamed Salah, "SOMSE: A semantic map based meta-search engine for the purpose of web information customization", Applied Soft Computing, vol. 11, no. 1, pp. 1310-1321, 2011.##[2] Mangold, Christoph, "A survey and classification of semantic search approaches", International Journal of Metadata, Semantics and Ontologies, vol. 2, no. 1, pp. 23-34, 2007.##[3] Kirar, Dilip, and Pranita Jain, "Equirs: Explicitly query understanding information retrieval system based on hmm", International Journal of Engineering Inventions, vol. 2, no 1, pp. 31-36, Jan. 2013.##[4] Vaughan, Liwen, and Mike Thelwall, "Search engine coverage bias: evidence and possible causes", Information processing & management, vol. 40, no. 4, pp. 693-707, 2004. ##[5] Jansen, Bernard J., et al, "Real life information retrieval: A study of user queries on the web", In ACM SIGIR Forum, vol. 32, no. 1, pp. 5-17, 1998. ##[6] Jansen, Bernard J., and Danielle Booth, "Classifying web queries by topic and user intent", CHI'10 Extended Abstracts on Human Factors in Computing Systems, pp. 4285-4290, ACM, 2010.##[7] Calderón-Benavides, Liliana, Cristina González-Caro, and Ricardo Baeza-Yates, "Towards a deeper understanding of the user's query intent", SIGIR 2010 Workshop on Query Representation and Understanding, pp. 21-24, 2010.##[8] Abowd, Gregory D., et al, "Towards a Better Understanding of Context and Context-Awareness", Handheld and ubiquitous computing. Springer Berlin Heidelberg, pp. 304-307, 1999. ##[9] Allan, James, and Hema Raghavan, "Using part-of-speech patterns to reduce query ambiguity", In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 307-314, 2002.##[10] Bäurle, Florian, “A user interface for semantic full text search”, Master Thesis, Faculty of Engineerin, University of Freiburg, 2011.##[11] Bing, Lidong, and Wai Lam, "Investigation of web query refinement via Topic Analysis and Learning with Personalization", 2011.##[12] Fonseca, Bruno M., et al, "Concept-based interactive query expansion", In Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 696-703, 2005. ##[13] Song, Wei, et al, "An effective query recommendation approach using semantic strateg-ies for intelligent information retrieval", Expert Systems with Applications, vol. 41, no. 2, pp. 366-372, 2014. ##[14] Bordogna, Gloria, et al, "Disambiguated query suggestions and personalized content-similarity and novelty ranking of clustered results to optimize web searches", Information Processing & Management, vol. 48, no. 3, pp. 419-437, 2012. ##[15] Broccolo, Daniele, et al, "Generating suggestions for queries in the long tail with an inverted index", Information Processing & Management, vol. 48, no. 2, pp. 326-339, 2012. ##[16] González-Caro, Cristina, and Ricardo Baeza-Yates, "A multi-faceted approach to query intent classification", String Processing and Informa-tion Retrieval, Springer Berlin Heidelb-erg, pp. 368-379, 2011.##[17] Jiang, Daxin, Jian Pei, and Hang Li, "Mining Search and Browse Logs for Web Search: A Survey", ACM Transactions on Computational Logic, pp. 1-42, Apr. 2013.##[18] Li, Lin, et al, "A feature-free search query classification approach using semantic dist-ance", Expert Systems with Applications, vol. 39, no. 12, pp. 10739-10748, 2012. ##[19] Bai, Lu, et al, "Exploring the query-flow graph with a mixture model for query recommenda-tion", Proceedings of IGIR Work-shop on Query Representation and Understand-ing, Beijing, China, Jul. 2011.##[20] Beeferman, Doug, and Adam Berger, "Agglomerative clustering of a search engine query log." In Proceedings of the sixth ACM SIGKDD international conference on Knowl-edge discov-ery and data mining, pp. 407-416, 2000. ##[21] Andersen, Casper, and Daniel Christensen, "User Logs for Query Disambiguation", 2013. ##[22] Sondhi, Parikshit, Raman Chandrasekar, and Robert Rounthwaite. "Using query context mod-els to construct topical search engin-es", In Proceed-ings of the third symposium on Information interaction in context, pp. 75-84, 2010. ##[23] Wu, Wei, Bin Zhang, and Mari Ostendorf. "Automatic generation of personalized annota-tion tags for twitter users", In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 689-692, 2010. ##[24] Biancalana, Claudio, and Alessandro Micarelli. "Social tagging in query expansion: A new way for personalized web search", In Computational Science and Engineering, vol. 4, pp. 1060-1065, 2009. ##[25] Kramár, Tomáš, Michal Barla, and Mária Bieliková. "Disambiguating search by leverage-ing a social context based on the stream of user’s activity", In User Modeling, Adaptation, and Personalization, pp. 387-392, 2010. ##[26] Cao, Huanhuan, et al, "Context-aware query classification", In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp. 3-10, 2009. ##[27] Joachims, Thorsten. “A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization”, Carnegie-mellon univ Pittsbur-gh pa dept of computer science, No. CMU-CS-96-118, 1996. ##[28] Luo, Le, and Li Li. 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"ارائه روشی جدید برای شاخص گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشه‎بندی متون". فصلنامه علمی-پژوهشی پردازش علائم و داده‌ها، جلد 13، شماره 1، صفحه 78-100، 1395.##[32] Farhad Rad, Hamid Parvin, Atoosa dahbashi and Behrooz Minaei. “Improved Clustering Persian Text Based on Keyword Using Linguistic and Thesaurus Knowledge”, Signal and Data Processing, Vol. 13, No. 1, P.P. 78-100, 2016.##[32] فرهاد راد، حمید پروین، آتوسا دهباشی و بهروز مینایی. "ارائه روشی جدید برای شاخص گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشه‎بندی متون". فصلنامه علمی-پژوهشی پردازش علائم و داده‌ها، جلد 13، شماره 1، صفحه 78-100، 1395.## ##

تشخیص ناهنجاری روی وب از طریق ایجاد پروفایل کاربرد دسترسی Web Anomaly Detection by Using Access Log Usage Profile 1 1  در پژوهش پیش رو با تمرکز روی شناسایی پیمایش‌های ناهنجار وب، سعی شده است تا از طریق مقایسه پروفایل‌های کاربرد وب با نشست فعلی کاربر رفتارهای بدخواهانه، مورد شناسایی قرار گیرند. در رویکرد پیشنهادی، ابتدا پروفایل های کاربرد وب از لاگ دسترسی وب سرور استخراج می‌شود؛ سپس با محاسبه شباهت هر نشست ورودی کاربر به پروفایل‌های اصلی و استخراج هشدارهای کنترل دسترسی متناظر با همان نشست یک شبکه عصبی فازی جهت تشخیص هنجار یا ناهنجار‌بودن پیمایش کاربر مورد استفاده قرار می‌گیرد. به دلیل فقدان داده استانداردی که هم شامل پیمایش‌های وب صفحات و هم شامل هشدارهای کنترل دسترسی متناظر با آن باشد، رویکردی نیز به منظور شبیه‌سازی پیمایش‌های یک کاربر عادی ارائه شد. ارزیابی‌های صورت گرفته نشان می‌دهد که روش ارائه‌شده در تشخیص پیمایش‌های ناهنجار توانمند عمل می‌کند.   2 Due to increasing in cyber-attacks, the need for web servers attack detection technique has drawn attentions today. Unfortunately, many available security solutions are inefficient in identifying web-based attacks. The main aim of this study is to detect abnormal web navigations based on web usage profiles. In this paper, comparing scrolling behavior of a normal user with an attacker, and simultaneous use of the access control policy alarms provided in web pages crawling with high access level, leads to an attacker to be detected among ordinary users. Indeed, the proposed method in this research includes two main steps: firstly web usage profiles are extracted as web main patterns of users’ behavior. In order to cluster similar web sessions we used a system inspired by artificial immune system. In the employed method, the rate at which a particular web page is visited as well as the time a user spends on the pages, is calculated so as to estimate how interesting a specific page is in a user’s session. Therefore, the similarity in the web page is defined based on the combination of the similarity of web pages URLs and that of the users’ level of interest in visiting them. Secondly, the difference between each current user session from the main profiles is calculated. Additionally, the access control logs are derived from corresponding sessions in this stage. Regarding the noisy nature of web server logs, a method was required so that a slight change in the data would not make a noticeable change in the results validity. Hence, a fuzzy neural network has been applied to distinguish normal and abnormal scrolling behavior in second step. Due to the lack of a standard data that contains both web pages scrolling and access control logs corresponding to it, providing such a data was required. At first, those intended logs were produced. To do so, an Apache web server was run on the platform of a Centos machine. In order to create the logs completely similar to a real server’s log, an e-commerce website was set up on Apache server. This website had about 160 different web pages to be visited by different users. At this point, a novel method is proposed to simulate the behavior of web users when they visit a website. Likewise, the abnormal data was generated by means of a large number of existing attack tools. It should also be noted that the access control policy has been used is SELinux and It has been added to Linux kernel. As mentioned, web server access log varies greatly with changing user behaviors, the stability of the proposed method against noise should be evaluated. For this reason, the results has been investigated on noisy profiles created by making random changes on the main profiles, and only the testing phase is conducted again. Subsequently, the distance from the profiles having noise is compared with the main ones. To demonstrate the ability of this method, the results have been compared with a Support Vector Machine (SVM). The carried out evaluations show that our approach performs efficiently in identifying normal and abnormal scrolling.   83 96 2016/02/92015/12/302015/05/192015/11/212015/12/252015/09/11 1394/6/20 2016/10/292017/03/52016/10/292016/10/292016/07/242017/08/27 1396/6/5 مریم السادات میرهادی تفرشی marm alsadat mirhadi tafreshi دانشگاه الزهرا ایران m.s.mirhadi@gmail.com رضا عزمی دانشگاه الزهرا ایران azmi.reza@gmail.com fuzzy neural networks web usage profile anomaly detection access control پروفایل کاربرد وب کنترل دسترسی شبکه عصبی فازی شناسایی حملات وب تشخیص ناهنجاری وب [1] J. Daniel E. Geer. The shrinking perimeter: Makingthe case for data-level risk case management. Veradsys White Paper, January 2004.##[2] C.Squicciarini, Elisa Bertino. Lorenzo D.Martino.Fedrica Paci.Anna .Security for Web Services and Service-Oriented Architectures .Springer, 2010.##[3] R. Azmi, B. Pishgoo, H. 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ماشین بینایی تشخیص‌گر باروری تخم‌مرغ و ارزیابی کارایی شبکه‌های عصبی و ماشین بردار پشتیبان در آن A Vision Machine for Detecting Fertile Eggs and Performance Evaluation of Neural Networks and Support Vector Machines in This Machine 1 1 در این پژوهش یک سامانه تشخیص­گر باروری تخم­مرغ ارائه شده است. این سامانه شامل دو بخش سخت­افزاری و نرم­افزاری است. سخت‌افزار ساخته‌شده امکان تصویربرداری دقیق از محتوی درون تخم‌مرغ­ها بدون آسیب­رسانی به نطفه یا جنین داخل آنها را فراهم می‌کند. بخش نرم­افزاری نیز عبارتست از مجموعه­ای از فرایندهای پردازش تصویر و بینایی ماشین که بدون حساسیت به تصاویر تخم‌مرغ‌های مختلف (به‌عنوان مثال با ضخامت پوسته متفاوت) قادر به شناسایی نطفه درون آنها است. برای جداسازی تخم­مرغ­های نطفه‌دار و بدون نطفه، دو نوع طبقه­بند شبکه عصبی و ماشین بردار پشتیبان طراحی و مورد مطالعه قرار گرفته است. برای ارزیابی سامانه، یک بانک تصاویر مشتمل بر 1200 تصویر از تخم‌مرغ­های قرار‌داده‌شده در فرایند جوجه‌کشی تهیه شده است. آزمایش‌های جامعی بر روی این بانک تصاویر انجام گرفته، که نتایج آنها مؤید عملکرد بسیار مناسب سامانه است. در ارزیابی­های انجام‌شده برای مقایسه کارایی دو طبقه­بند، نشان داده شده است که طبقه­بند ماشین بردار پشتیبان با میانگین دقت تشخیص %57/50، %67/83، %20/94، %03/98 و %91/98 به‌ترتیب در روزهای نخست، دوم، سوم، چهارم و پنجم فرایند جوجه­کشی از کارایی بهتری نسبت به طبقه­بند شبکه عصبی برخوردار است و همچنین حساسیت بسیار کمتری در برابر کاهش تعداد نمونه­های آموزشی از خود نشان داده است.   2 In this research, a system is proposed for detecting fertility of eggs. The system is composed of two parts: hardware and software. The fabricated hardware provides a platform to obtain accurate images from inner side of the eggs, without harming their embryos. The software part includes a set of image processing and machine vision processes, which is able to detect the fertility of eggs from captured images, without any sensitivities to different types of eggs (e.g. with different thickness of the eggshell). In order to classify the fertile and infertile eggs, two classifiers based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are designed and tested. It means that, to have a fully automatic fertility detection machine, we design two machine learning approaches using SVMs and ANNs to classify fertile and infertile eggs. That is, instead of using a predefined threshold values for distinguishing fertile pixels of egg images from infertile ones, we try to train the machine to do the job automatically. After training the machine using both classification algorithms, the performance of them are accurately investigated and measured in order to select the appropriate one. To evaluate the system, an egg image dataset is provided including 1200 images captured from incubated eggs. Extensive experiments are performed using the provided dataset, which confirm the reliable performance of the system. Comparisons with other fertility detection approaches applying different methods and algorithms confirm that the proposed machine outperforms more complex systems. Performance evaluations of the two proposed classifiers confirm that the SVM based classifier, with average detection accuracy of 50.57% at day 1 of incubation, 83.67% at day 2, 94.20% at day 3, 98.03% at day 4, and 98.91% at day 5, performs better than ANN based classifier, and it is also less sensitive against the reductions in training samples, which can be a serious issue when we are not able to provide more training samples.   97 112 2016/02/92015/12/302015/05/192015/11/212015/12/252015/09/112016/02/14 1394/11/25 2016/10/292017/03/52016/10/292016/10/292016/07/242017/08/272017/03/5 1395/12/15 مهدی هاشم زاده Mahdi Hashemzadeh دانشگاه شهید مدنی آذربایجان ایران meh_hashemzadeh@yahoo.com Machine Vision Fertile Eggs Classification Neural Networks Support Vector Machines بینایی ماشین تخم‌مرغ نطفه‌دار طبقه‌بندی شبکه‌های عصبی ماشین بردار پشتیبان ##]1[ پوررضا، جواد، (1379). اصول علمی و عملی پرورش طیور. انتشارات دانشگاه صنعتی اصفهان.##[1] J. 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افزایش سرعت نگهداری افزایشی دید با استفاده از الگوریتم فاخته Increasing the Speed of Incremental View Maintenance Using the Cuckoo Algorithm 1 1 پایگاه‌ داده ‌تحلیلی مخزنی از اطلاعات یکپارچه شده است که از منابع مختلف جمع‌آوری می‌شود. در پایگاه‌داده‌تحلیلی داده‌های استخراج‌شده از منابع مختلف، به فرم دید ذخیره می‌شوند؛ بنابراین دیدها باید نگهداری شوند و در هنگام تغییر منابع داده، دیدها نیز به‌روز شوند. از آن‌جایی که افزایش به‌روزرسانی‌ها ممکن است سربار و هزینه زیادی داشته باشد، ضروری است که به‌روزرسانی دیدها با دقت بالایی صورت گیرد. الگوریتمی که در این مقاله ارائه می‌شود، ترکیب یک روش گروه‌بندی، با الگوریتم فراابتکاری فاخته است که باعث کاهش زمان نگهداری دید و در‌نتیجه افزایش سرعت نگهداری دید افزایشی می‌شود. الگوریتم بهینه‌سازی فاخته با یک جمعیت اولیه آغاز می‌شود. تلاش برای زنده‌ماندن این فاخته‌ها اساس الگوریتم بهینه‌سازی است. نتایج پیاده‌سازی نشان می‌دهد که الگوریتم فاخته در مقایسه با روش‌های قبلی از سرعت بالاتری به‌منظور به‌روزرسانی دید افزایشی برخوردار است.   2 Data warehouse is a repository of integrated data that is collected from various sources. Data warehouse has a capability of maintaining data from various sources in its view form. So, the view should be maintained and updated during changes of sources. Since the increase in updates may cause costly overhead, it is necessary to update views with high accuracy. Optimal Delta Evaluation method is one of the incremental view maintenance method that can maintain materialized views efficiently in the data warehouse environment. This method is one of the incremental view maintenance grouping methods. In this method incremental maintenance expression is divided into groups, as a result access to some repeated relations is minimized. As a final result, Optimal Delta Evaluation method can minimize the total accesses to relations. The algorithm proposed in this paper, is the combination of optimal Delta Evaluation with Cuckoo heuristic Algorithm that reduces maintenance time of views and thus speeds up this process. Cuckoo optimization algorithm begins with an initial population. Trying to survive the Cuckoo makes the base to optimize the algorithm. The results show that the Cuckoo algorithm is faster in order to update its incremental views compared with previous methods.   113 126 2016/02/92015/12/302015/05/192015/11/212015/12/252015/09/112016/02/142015/11/20 1394/8/29 2016/10/292017/03/52016/10/292016/10/292016/07/242017/08/272017/03/52016/10/29 1395/8/8 عفیفه کریمی مصدق Afifeh Karimi Mosadegh دانشگاه آزاد اسلامی‌‌‌ قزوین ایران karimi.mosadegh@yahoo.com نگین دانشپور Negin Daneshpour دانشگاه تربیت دبیر شهید رجایی، دانشکده مهندسی کامپیوتر ایران ndaneshpour@srttu.edu data warehouse Cuckoo algorithm random search optimization delta tree incremental view maintenance پایگاه‌داده‌تحلیلی الگوریتم فاخته جستجوی تصادفی درخت دلتای بهینه نگهداری افزایشی دید [1] صباغ گل ریحانه ، دانشپور نگین ، "بهبود الگوریتم انتخاب دید در پایگاه‌داده‌تحلیلی با استفاده از یافتن پرس‌وجو‌های پر تکرار"، فصل‌نامه علمی‌پژوهشی ((پردازش علائم و داده‌ها))، دوره 14، شماره 1، 3-1396.##[1] Sabbagh Gol. R, and Daneshpour. N. 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پیکره اعلام: یک پیکره استاندارد واحدهای اسمی برای زبان فارسی A’laam Corpus: A Standard Corpus of Named Entity for Persian Language 1 1 تشخیص واحدهای اسمی یکی از مسائل مطرح در پردازش زبان طبیعی است. کاربرد عمده شناسایی واحدهای اسمی در سامانه‌های خلاصه‌ساز متون، استخراج اطلاعات، پرسش و پاسخ، ترجمه ماشینی و دسته‌بندی اسناد است. یکی از روش‌های تهیه سامانه تشخیص واحدهای اسمی، استفاده از روش‌های مبتنی بر پیکره است. این مقاله نحوه و مراحل تهیه پیکره اَعلام – یک پیکره استاندارد با برچسب واحدهای اسمی برای زبان فارسی- را شرح می‌دهد. مجموعه تهیه‌شده با داشتن سیزده برچسب واحدهای اسمی و حجم 250 هزار کلمه نیاز سامانه‌های برچسب‌گذاری خودکار در حوزه پردازش زبان طبیعی فارسی را برآورده می‌کند. با استفاده از این پیکره و به‌کارگیری روش یادگیری ماشین میدان تصادفی شرطی، سامانه‌ای برای شناسایی واحدهای اسمی جملات فارسی تهیه شده که دارای دقت 94/92 درصد و فراخوانی 48/78 درصد است.   2 Named entity recognition (NER) is a natural language processing (NLP) problem that is mainly used for text summarization, data mining, data retrieval, question and answering, machine translation, and document classification systems. A NER system is tasked with determining the border of each named entity, recognizing its type and classifying it into predefined categories. The categories of named entities include the names of persons, organizations, locations (e.g. city and country), expressions of times, quantities, monetary expressions, and percentages. In general, corpus-based NER approaches have been proved to be well suited for NER problem. Using a NER corpus, recognition of named entities can be done through ruled-based or machine-learning methods.       Corpus-based NER systems need standard and appropriate annotated corpora. However, such corpora mainly exist in languages such as English, and are rarely found in Persian/Farsi or limited in volume. So, this paper is dedicated to describe the producing procedure of a standard named entity (NE) corpus - A’laam corpus - for Persian language. A’laam corpus contains about 250,000 tokens tagged with 13 NE tags. This corpus has been developed in the Research Center for Development of Advanced Technologies (RCDAT). Tokens of A’laam corpus are a part of Farsi Text Corpus. The Farsi Text Corpus is a standard Farsi corpus. This corpus, containing more than 100 million Farsi words, has been developed by the Research Center of Intelligent Signal Processing (changed to the Research Center for Development of Advanced Technologies in 2013). The words of this corpus, selected from diverse written and spoken sources, was tokenized and corrected manually. In addition, a part of the Farsi Text Corpus with 8 million words has part-of-speech (POS) tags at word level. Totally, about 8,400 sentences of the Farsi Text Corpus have been randomly selected to obtain about 250,000 tokens of A’laam Corpus. This corpus included words, POS tags, and named entity tags.       To evaluate A’laam corpus, a Persian NER system was trained based on this corpus. This corpus was so divided into the train and test sections. The train section accounted for 90% of the corpus and the remaining 10% belonged to the test section. Using Conditional Random Fields (CRF) method, the Persian NER system resulted in a 92.94% Precision and 78.48% Recall.   127 142 2016/02/92015/12/302015/05/192015/11/212015/12/252015/09/112016/02/142015/11/202016/01/17 1394/10/27 2016/10/292017/03/52016/10/292016/10/292016/07/242017/08/272017/03/52016/10/292017/03/5 1395/12/15 شادی حسین‌نژاد Shadi Hosseinnejad پژوهشگاه توسعه فناوری‌های پیشرفته خواجه نصیرالدین طوسی ایران hosseinnjad@rcdat.ir یاسر شکفته Yasser Shekofteh دانشکده مهندسی و علوم کامپیوتر، دانشگاه شهید بهشتی ایران shekofteh@rcdat.ir طاهره امامی آزادی Tahereh Emami Azadi پژوهشگاه توسعه فناوری‌های پیشرفته خواجه نصیرالدین طوسی ایران t.emami@rcdat.ir Natural language Processing Named Entity Recognition Named Entity Corpus Machine learning Conditional Random Field پردازش زبان طبیعی تشخیص واحدهای اسمی پیکره واحدهای اسمی یادگیری ماشین میدان تصادفی شرطی [1] س. ع. اصفهانی، س. راحتی قوچانی و ن. جهانگیری. «سیستم شناسایی و طبقه‌بندی اسامی در متون فارسی»، پردازش علائم و داده‌ها، دوره 13، شماره 1 (پیاپی 13) ; صص. 77-88. 1389. ##[1] S. 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مدل‌سازی صفحه‌ای محیط‌های داخلی با استفاده از تصاویر RGB-D Indoor Planar Modeling Using RGB-D Images 1 1 در رباتیک و به‌طور خاص برای ساخت نقشه‌های سه‌بعدی از محیط‌های داخلی، تفسیر تصاویر RGB-D به مسئله مهمی تبدیل شده است. در این مقاله جهت کاهش حجم داده‌ها و تسریع ساخت نقشه سه‌بعدی، تصاویر عمق به ابرهای نقطه‌ای تبدیل و سپس آن‌ها بر مبنای صفحات تصویر قطعه‌بندی می‌شوند. پس از برازش مدل صفحه‌‌ای متناظر با هر قطعه، تعداد مشخصی از نقاط روی صفحات تولید و سپس با اجرای الگوریتم تکراری نزدیک‌ترین نقطه (ICP) روی این نقاط، ماتریس‌های دوران و انتقال بین هر دو فریم تخمین زده شده و تصویر تثبیت می‌شود. نتایج نشان می‌دهد که روش ارائه‌شده، به‌طور متوسط سرعت را در صورت استفاده از فریم‌های متوالی 55 درصد و در صورت استفاده از فریم‌های غیرمتوالی 91 درصد افزایش می‌دهد. روش پیشنهادی می‌تواند منجر به کاهش حجم محاسبات در مسئله مکان‌یابی و تهیه نقشه همزمان (SLAM)  شود. 2 In robotic applications and especially 3D map generation of indoor environments, analyzing RGB-D images have become a key problem. The mapping problem is one of the most important problems in creating autonomous mobile robots. Autonomous mobile robots are used in mine excavation, rescue missions in collapsed buildings and even planets’ exploration. Furthermore, indoor mapping is beneficial in finding and rescuing missions. With recent advances, mobile robots are used in hazardous missions such as radioactive areas or collapsing buildings. Having the environment’s map beforehand can boost efficiency and effectiveness of the mission. In order to digitize the environment, several 3D scans are needed. However, these scans should be merged according to a global coordination system to create a correct, consistent model. This process is called image registration. If the robot with 3D scanner is able to accurately localize itself, the registration can be done directly by robots pose. However, due to imprecise robot sensors, self-localization is error prone. Therefore, the geometric structure of overlapping 3D scans is considered. In order to registering various points sets, Iterative Closest Point (ICP) algorithm is used. ICP is the most common approach to align point clouds in two consecutive image frames. This algorithm uses a point to point approach. RGB and depth images which are captured by Kinect are used in this study. In order to reducing data points and performing faster 3D map creation, depth images are converted to point clouds and then segmentation is done according to image planes. For this purpose RGB images are segmented by region growing segmentation algorithm. In this algorithm, the image was initially over segmented. This algorithm uses stack data structure and Euclidean distance in Lab color space to segment the image. Euclidean distance in Lab color space describes the resemblance of two colors to each other. In this algorithm, the aim is to label each pixel to a segment. To this end, each unlabeled pixels Euclidean distance to its neighboring mean color is checked to be within a threshold. For over-segmentation, if the distance satisfies the smaller threshold, the more pixels will be merged to the segment. Afterwards a plane was fit to each segment. After segmentation, each segment should be represented by a plane. Eventually, the segments were merged based on the product of normal vectors and plane fitting error criteria. After segmentation, planes were fit to the new segments again. A given number of points were generated on the plane. ICP algorithm was executed on these points and transfer and rotation matrices were obtained. Generating points on the plane results in fewer points. Therefore, the points were reduced and algorithms performance was increased. The results show that the proposed method increases the speed up to 55 and 91 percent in consecutive and non-consecutive frames on average, respectively.   143 160 2016/02/92015/12/302015/05/192015/11/212015/12/252015/09/112016/02/142015/11/202016/01/172016/02/16 1394/11/27 2016/10/292017/03/52016/10/292016/10/292016/07/242017/08/272017/03/52016/10/292017/03/52017/03/5 1395/12/15 مقداد پاک نژاد Meghdad Paknezhad دانشگاه یزد ایران packnezhad@stu.yazd.ac.ir مهدی رضائیان Mehdi Rezaeian دانشگاه یزد ایران mrezaeian@yazd.ac.ir Mapping Problem RGB-D Images Kinect sensor مسئله تهیه نقشه تصاویر RGB-D حس‌گر کینکت [1] S. Thrun, "Robotic mapping: a survey," in Exploring artificial intelligence in the new millennium, L. Gerhard and N. Bernhard, Eds., ed: Morgan Kaufmann Publishers Inc., 2003, pp. 1-35.##[2] V. HÖGMAN, "Building a 3D map from RGB-D sensors," Master dissertation, Dept. Computer Science, Computer Vision and Active Perception Laboratory Royal Institute of Technology (KTH), Stockholm, Sweden, 2011.##[3] P. Vieira and R. Ventura, "Interactive mapping in 3D using RGB-D data," IEEE International Symposium on Safety Security and Rescue Robotics (SSRR), 2012, pp. 1-6.##[4] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, "RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments," in Experimental Robotics, ed: Springer Berlin Heidelberg, 2010, pp. 477-491.##[5] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, "Indoor segmentation and support inference from RGBD images," in Proc. 12th European conference on Computer Vision - Volume Part V, Florence, Italy, 2012.##[6] C. Couprie, C. e. Farabet, L. Najman, and Y. LeCun, "Indoor Semantic Segmentation using depth information,", International Conference on Learning Representations (ICLR2013), 2013.##[7] P. J. Besl and N. D. 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