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پیش‌گویی برخط و تک‌کاناله وقوع حمله‌های صرعی با ارائه الگوی تولید صرع بر روی سیگنال‌های depth-EEG با استفاده از فیلتر کالمن توسعه‌یافته Online Single-Channel Seizure Prediction, Based on Seizure Genesis Model of Depth-EEG Signals Using Extended Kalman Filter 1 1 تاکنون برای پیش‌گویی وقوع حمله­های صرعی تلاش‌های فراوانی انجام‌شده‌است و مؤثرترین این روش‌ها نیز بر پایه چگونگی ایجاد حمله صرعی، هم‌زمانی بین کانال‌های متفاوت، ثبت فعالیت مغز را موردتوجه قرار داده­اند. این روش‌ها، برای رسیدن به‌ دقت پیش‌گویی بالا، به تعداد زیادی از کانال‌های ثبت فعالیت مغز نیاز دارند و به همین دلیل در عمل مورداستفاده بیماران نخواهند‌بود. با توجه به این نکته که عامل ایجاد هم‌زمانی بین بخش‌های متفاوت مغز، میزان فعالیت مهاری و تحریکی در نورون­هاست؛ انتظار می­رود دقت پیش‌گویی وقوع حمله صرعی با توجه به میزان مهار و تحریک در نورون­های مغز بهبود یابد. در این مقاله برای شبیه­سازی تولید خودبه­خودیِ حمله صرعی، یک الگو فیزیولوژیک با یک الگو آماری فضای حالت (SSM) ترکیب شده‌است. شاخصه‌های الگوی فیزیولوژیک، میزان فعالیت مهاری و تحریکی نورون­ها و خروجی آن، سیگنال­های depth-EEG است. در این الگو فیزیولوژیک، تغییر میزان مهار و تحریک، به بروز رفتارهای متفاوتی در سیگنال فعالیت مغز در خروجی الگو منجر می­شود. الگوی SSM برای شبیه­سازی رفتار شاخصه‌های مهار و تحریک در الگو فیزیولوژیک استفاده شده است. با توجه به این الگو و با استفاده از یک فیلتر کالمن توسعه­یافته، می­توان شاخصه‌های مهار و تحریک پنهان در سیگنال­های مغزی نوفه‌ای را به‌صورت برخط استخراج کرد. با در دست داشتن دنباله شاخصه‌های مهار و تحریک (به‌جای سیگنال­های depth-EEG)، رفتار شاخصه‌ها با استفاده از یک طبقه‌بندی‌کننده الگوی مارکوف مخفی پیوسته (CHMM) به دو گروه پیش­ازحمله و میان­حمله­ای دسته‌بندی‌شده است. در انتها با روش پیشنهادی، دنباله شاخصه‌های مهار و تحریک سیگنال ثبت­شده از یک کانال واقع در کانون صرع شش بیمار از پایگاه داده FSPEEG (که برای آن‌ها ثبت depth-EEG وجود دارد) استخراج‌شده است. کانون صرع این شش بیمار در هیپوکامپ و در بخش تمپورال قرار دارد. این سیگنال­ها شامل 24 حمله و حدود 144 ساعت سیگنال میان­حمله­ای هستند. وقوع حمله صرعی در این بیماران در بدترین حالت ده دقیقه پیش از رخداد حمله صرعی پیش­بینی شده است که برای انجام اقدامات درمانی مناسب است. میزان حساسیت و نرخ پیش‌گویی نادرست الگوریتم پیش‌گویی به‌طور میانگین به‌ترتیب برابر با 100% و 2/0 در ساعت است. در مقایسه با روش‌های پر‌محاسبه‌ای که برای رسیدن به ‌دقت بالا به کانال‌های فراوانی نیاز دارند، پیش‌گویی مبتنی بر الگو با استفاده از یک کانال و به‌صورت کاملاً برخط از ویژگی­های این روش است.   2 Many efforts have been done to predict epileptic seizures so far. It seems that some kind of abnormal synchronization among brain areas is responsible for the seizure generation. This is because the synchronization-based algorithms have been the most important methods so far. However, the huge number of EEG channels, which is the main requirement of these methods, make them very difficult to use in practice. In this paper, in order to improve the prediction algorithm, the factor underlying the abnormal brain synchronization, i.e., the imbalance of excitation/inhibition neuronal activity, is taken into account. Accordingly, to extract these hidden excitatory/inhibitory parameters from depth-EEG signals, a realistic physiological model is used. The Output of this model (as a function of model parameters) imitate the depth-EEG signals. On the other hand, based on this model, one can estimate the model parameters behind every real depth-EEG signal, using an identification process. In order to be able to track the temporal variation of the parameter sequences, the model parameters, themselvese, are supposed to behave as a stochastic process. This stochastic process, described by a Hidden Markov Model formerly (HMM) and worked by the current researchists, is now modified to a State Space Model (SSM). The advantage of SSM is that it can be described by some differential equations. By adding these SSM equations to the differential equations producing depth-EEG signals, Kalman filter can be used to identify the parameter sequences underlying signals. Then, these extracted inhibition/excitation sequences can be applied in order to predict seizures. By using the four model parametetrs relevant to excitation/inhibition neuronal activity, extracted from just one channel of depth-EEG signals, the proposed method reached the 100% sensitivity, and 0.2 FP/h, which is very similar to the multi-channel algorithms. The algorithm can be done in an online manner.   3 28 2018/03/2 1396/12/11 2017/10/25 1396/8/3 فرزانه شایق Farzaneh Shayegh دانشگاه صنعتی اصفهان ایران farzaneh.shayegh@gmail.com فهیمه قاسمی Fahimeh Ghasemi دانشگاه علوم پزشکی اصفهان ایران f_ghasemi@amt.mui.ac.ir رسول امیر فتاحی Rasoul Amirfatahi دانشگاه صنعتی اصفهان ایران fattahi@cc.iut.ac.ir سعید صدری Saeed Sadri دانشگاه صنعتی اصفهان ایران sadri@cc.iut.ac.ir کریم انصاری اصل Karim Ansarifard دانشگاه شهید چمران اهواز ایران karim.ansari@gmail.com Physiological model of epileptic seizures Online Single-Channel Seizure Prediction Kalman Filter State Space Model (SSM) epileptic Hidden Markov Model (HMM) الگو فیزیولوژیک حمله صرع پیش‌گویی برخط وقوع حمله صرعی فیلتر کالمن الگو آماری فضای حالت (SSM) صرع الگوی مارکوف مخفی پیوسته (CHMM) [1] F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, "Seizure prediction: the long and winding road," Brain, vol. 130, pp. 314-33, 2007.##[2] L. D. Iasemidis, J. Chris Sackellares, H. P. Zaveri, and W. J. 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تخمین جهت منابع با استفاده از زیرفضای کرونکر Direction of Arrival (DOA) Estimation Using Kronecker Subspace 1 1 این مقاله به بررسی تخمین جهت منابع با استفاده آرایه خطی می­پردازد. تاکنون الگوریتم­های مختلفی برای شرایطی که در آن تعداد منابع بیشتر از تعداد آنتن­ها باشد، ارائه شده است. از آن جمله به الگوریتم ختری­رائو، آرایه تودرتو و آرایه پویا می­توان اشاره کرد. روش‌های یاد‌شده تنها توانایی تخمین منابع ناهمبسته را دارند. الگوریتم تخمین جهت منابع با استفاده از زیرفضای کرونکر برای تخمین جهت منابع همبسته‌ای با تعداد بیشتر از تعداد عنصر آرایه در این مقاله ارائه شده است. شبیه­سازی­های ارائه‌شده در این مقاله تأییدی براین ادعا است. علاوه‌بر‌این، کران کرامررائو که معیار بسیار مهم در بحث تخمین است، بررسی شده است. این کران برای مسأله تخمین جهت منابع پیش از این ارائه شده بود؛ اما برای تعداد منابعِ بیشتر از تعدادِ عنصر آرایه قابل محاسبه نیست. این مقاله بر این مشکل چیره شده و کران کرامررائو را برای تعداد منابع بیشتر از تعداد عنصر آرایه نیز ارائه کرده است. 2 This paper proceeds directions of arrival (DOA) estimation by a linear array. These years, some algorithms, e.g. Khatri-Rao approach, Nested array, Dynamic array have been proposed for estimating more DOAs than sensors. These algorithms can merely estimate uncorrelated sources. For Khatri-Rao approach, this is due to the fact that Khatri-Rao product discard the non-diagonal entries of the correlation matrix in opposed to Kronecker product. In this article, an algorithm named as Direction of Arrival (DOA) Estimation using Kronecker Subspace is proposed to solve more correlated sources than sensors via some properties of vectorization operator and Kronecker product. The simulations in different scenarios are presented considering various numbers of frames and correlation values, here. These verify our mathematical analysis. Furthermore, Cramer-Rao bound (CRB) which is a crucial criterion to estimate, is under investigating for DOA problem. Although, CRB for DOA estimation has been proposed before, it is applicable only for fewer sources than sensors. In this paper, CRB for more sources than sensor is derived by extending the dimensions with using both real and imaginary parts of the parameters. This bound is compared to the error of the presented algorithm. The simulations show that the error of the presented algorithm is merely 7 dB far from the CRB. 29 40 2018/03/22016/06/2 1395/3/13 2017/10/252017/03/5 1395/12/15 سینا مجیدیان Sina Majidian دانشگاه علم و صنعت ایران ایران s_majidian@elec.iust.ac.ir فرزان حدادی Farzan Haddadi دانشگاه علم و صنعت ایران ایران Farzanhaddadi@iust.ac.ir Array signal processing Direction of Arrival (DOA) Correlated sources Kronecker پردازش سیگنال آرایه‌ای تخمین جهت منبع منابع همبسته کرونکر [1] H. Krim and M. Viberg, "Two decades of array signal processing research, the parametric approach," IEEE Signal Process. Mag., pp. 67-94, Jul. 1996.##[2] L. Fulai, et al., "Spatial Differencing Method for DOA Estimation Under the Coexistence of Both Uncorrelated and Coherent Signals," IEEE Trans. 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شناسایی نوع و مدل وسیله نقلیه با استفاده از مجموعه بخش‌های متمایز‌کننده Using Discriminative Parts for Vehicle Make and Model Recognition 1 1 طبقه­بندی دقیق اشیا (Fine-Grained Recognition) چالشی است که جامعه بینایی ماشین در حال حاضر با آن روبه­رو شده است. در این نوع طبقه­بندی گروه کلی شیء مشخص بوده و هدف تعیین زیرگروه دقیق آن است؛ شناسایی نوع و مدل وسیله نقلیه (VMMR) نیز در این حوزه قرار می­گیرد. این مسئله به‌دلیل وجود تعداد طبقه‌های زیاد، تفاوت درون‌طبقه‌ای بسیار و تفاوت بین طبقه‌ای کم از مسائل طبقه­بندی دشوار به‌شمار می­رود. در این مقاله روشی مبتنی بر بخش برای شناسایی نوع و مدل خودرو پیشنهاد شده است. این روش برای طبقه­بندی طبقه‌های مختلف خودرو، ابتدا بخش­های متمایز‌کننده هر یک را به‌صورت خودکار می­یابد؛ سپس با استخراج ویژگی از این بخش­ها و رابطه هندسی بین آن­ها، یک مدل می­آموزد. وزن بخش­های مختلف هر مدل به‌صورت پویا و با استفاده از مجموعه داده­های آموزشی یاد گرفته می­شود. سامانه پیشنهادی با ترکیب این مدل­ها به شناسایی طبقه خودرو می­پردازد. برای آزمایش سامانه پیشنهادی و به‌دلیل عدم وجود مجموعه داده به اشتراک گذاشته‌شده، یک مجموعه داده با بیش از 5000 خودرو از 28 طبقه مختلف تهیه و به‌صورت کامل علامت­گذاری شده است. نتیجه آزمایش‌های انجام‌شده بر روی این تصاویر که دارای تغییرات روشنایی زیاد و تغییرات زاویه اندک هستند، نشان از دقت بالای روش پیشنهادی دارد.   2 In fine-grained recognition, the main category of object is well known and the goal is to determine the subcategory or fine-grained category. Vehicle make and model recognition (VMMR) is a fine-grained classification problem. It includes several challenges like the large number of classes, substantial inner-class and small inter-class distance. VMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. VMMR can also be used when specific models of vehicles are required to be automatically identified by cameras. Few methods have been proposed to cope with limited lighting conditions. A number of recent studies have shown that latent SVM trained on a large-scale dataset using data mining can achieve impressive results on several object classification tasks. In this paper, a novel method has been proposed for VMMR using a modified version of latent SVM. This method finds discriminative parts of each class of vehicles automatically and then learns a model for each class using features extracted from these parts and spatial relationship between them. The parts weights of each model are tuned using training dataset.  Putting this individual models together, our proposed system can classify vehicles make and model. All training and testing steps of the proposed system are done automatically. For training and testing the performance of the system, a new dataset including more than 5000 vehicles of 28 different make and models has been collected. This dataset poses different kind of challenges, including variations in illumination and resolution. The experimental results performed on this dataset show the high accuracy of our system.   41 54 2018/03/22016/06/22016/10/11 1395/7/20 2017/10/252017/03/52017/06/10 1396/3/20 محسن بیگلری Mohsen Biglari دانشگاه صنعتی شاهرود ایران mbt925@gmail.com علی سلیمانی Ali Soleimani دانشگاه صنعتی شاهرود ایران mbt925@gmail.com حمید حسن پور Hamid Hassanpour دانشگاه صنعتی شاهرود ایران mbt925@gmail.com Fine-grained recognition vehicle make and model recognition VMMR part-based approach شناسایی دقیق اشیا شناسایی نوع و مدل وسیله نقلیه VMMR رویکرد مبتنی بر بخش [1] Sun, Z., George, B., Ronald, M. "On-Road Vehicle Detection: A Review.", IEEE Transac-tions on Pattern Analysis and Machine Intellig-ence., vol. 28, no. 5, pp. 694–711, 2006.##[2] Li, X., Guo, X. "A HOG Feature and SVM Based Method for Forward Vehicle Detection with Single Cam-era.", 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 263–266, 2013.##[3] Yousaf, K., Iftikhar, A., Javed, A. "Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey.", International Journal of Image, Graphics and Signal Processing., vol. 4, no. 9, pp. 52, 2012.##[4] Ambardekar, A., Nicolescu, M., Bebis, G., Nicol-escu, M. "Vehicle Classification Framework: A Comparative Study.", EURASIP Journal on Image and Video Processing., vol. 2014, no. 1, pp. 1–13, 2014.##[5] Zhang, B. "Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles.", IEEE Transactions on Intelligent Transportation Systems., vol. 14, no. 1, pp. 322–332, 2013.##[6] Dong, Z., Jia, Y. "Vehicle Type Classification Using Distributions of Structural and Appearance-Based Features.", IEEE International Conference on Image Processing, pp. 4321–4324, 2013.##[7] Choo, S., Mokhtarian, P.L. "What Type of Vehicle Do People Drive? The Role of Attitude and Lifestyle in Influencing Vehicle Type Choice.", Transportation Research Part A: Policy and Practice., vol. 38, no. 3, pp. 201–222, 2004.##[8] Conos, M. "Recognition of Vehicle Make from a Frontal View.", Master Thesis, Czech Tech, 2007.##[9] Dlagnekov, L. "Video-Based Car Surveillance: License Plate, Make, and Model Recognition.", Master Thesis, University of California, San Diego, 2005.##[10] Negri, P., Clady, X., Milgram, M., Poulenard, R. "An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification.", 18th International Conference on Pattern Recognition, pp. 574–577, 2006.##[11] Clady, X., Negri, P., Milgram, M., Poulenard, R. "Multi-Class Vehicle Type Recognition System.", Artificial Neural Networks in Pattern Recogni-tion, Lecture Notes in Computer Science, pp. 228–239, Springer Berlin Heidelberg, 2008.##[12] Huang, H., Zhao, Q., Jia, Y., Tang, S. "A 2DLDA Based Algorithm for Real Time Vehicle Type Recognition.", 11th International IEEE Confer-ence on Intelligent Transportation Systems, pp. 298–303, 2008.##[13] Pearce, G., Pears, N. "Automatic Make and Model Recognition from Frontal Images of Cars.", 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 373–378, 2011.##[14] Saravi, S., Edirisinghe, E. a. "Vehicle Make and Model Recognition in CCTV Footage.", 18th International Conference on Digital Signal Processing, pp. 1–6, 2013.##[15] Lowe, D.G. "Object Recognition from Local Scale-Invariant Features.", 17th IEEE Interna-tional Conference on Computer Vision, pp. 1150–1157, 1999.##[16] Petrovic, V., Cootes, T. "Analysis of Features for Rigid Structure Vehicle Type Recognition.", British Machine Vision Conference, pp. 587–596, 2004.##[17] Munroe, D.T., Madden, M.G. "Multi-Class and Single-Class Classification Approaches to Veh-icle Model Recognition from Images.", AICS '05, pp. 93–102, 2005.##[18] Kazemi, F.M., Samadi, S., Poorreza, H.R., Akbarzadeh-T, M.-R. "Vehicle Recognition Us-ing Curvelet Transform and SVM.", 4th Interna-tional Conference on Information Techno-logy, pp. 516–521, 2007.##[19] Zafar, I., Edirisinghe, E. a., Acar, B.S. "Localised Contourlet Features in Vehicle Make and Model Recognition.", Image Processing: Machine Vision Applications II, Proc. of SPIE-IS&T Electronic Imaging, pp. 725105–725115, 2009.##[20] Hsieh, J.-W., Chen, L.-C., Chen, D.-Y. "Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition.", IEEE Transactions on Intelligent Transportation Systems., vol. 15, no. 1, pp. 6–20, 2014.##[21] Nazemi, A., Shafiee, M., Azimifar, Z. "On Road Vehicle Make and Model Recognition via Sparse Feature Coding.", 8th Iranian Conference on Machine Vision and Image Processing, pp. 436–440, 2013.##[22] Baran, R., Glowacz, A., Matiolanski, A. "The Efficient Real- and Non-Real-Time Make and Model Recognition of Cars.", Multimedia Tools and Applications., no. June, pp. 1–20, 2013.##[23] Gao, Y., Lee, H.J. "Moving Car Detection and Model Recognition Based on Deep Learning.", Advanced Science and Technology Letters., vol. 90, no. Multimedia, pp. 57–61, 2015.##[24] Siddiqui, A.J.A.M., Boukerche, A. "Towards Efficient Vehicle Classification in Intelligent Transportation Systems.", Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, pp. 19–25, 2015.##[25] Psyllos, A. "Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme.", Intelligent Transportation Systems, IEEE Transactions on., vol. 11, no. 2, pp. 322–328, 2010.##[26] Yang, H., Zhai, L., Liu, Z., Li, L., Luo, Y., Wang, Y., Lai, H., Guan, M. "An Efficient Method for Vehicle Model Identification via Logo Recognition.", International Conference on Computational and Information Sciences, pp. 1080–1083, 2013.##[27] Santos, D., Correia, P.L. "Car Recognition Based on Back Lights and Rear View Features.", 10th International Workshop on Image Analysis for Multimedia Interactive Services, pp. 137–140, 2009.##[28] Sarfraz, M.S., Saeed, A., Khan, M.H., Riaz, Z. "Bayesian Prior Models for Vehicle Make and Model Recognition.", Proceedings of the 6th International Conference on Frontiers of Information Technology - FIT '09, p. 6, ACM Press, New York, New York, USA, 2009.##[29] Psyllos, A., Anagnostopoulos, C.N., Kayafas, E., Loumos, V. "Image Processing & Artificial Neural Networks for Vehicle Make and Model Recognition.", 10th international conference on applications of advanced technologies in transportation, pp. 4229–4243, 2008.##[30] Llorca, D., Colas, D., Daza, I. "Vehicle Model Recognition Using Geometry and Appearance of Car Emblems from Rear View Images.", 17th IEEE International Conference on Intelligent Transportation Systems, pp. 3094–3099, 2014.##[31] Dalal, N., Triggs, B. "Histograms of Oriented Gradients for Human Detection.", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893, 2005.##[32] Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D. "Object Detection with Discriminatively Trained Part-Based Models.", IEEE transactions on pattern analysis and machine intelligence., vol. 32, no. 9, pp. 1627–45, 2010.##[33] Lampert, C., Nickisch, H., Harmeling, S. "Attribute-Based Classification for Zero-Shot Learning of Object Categories.", IEEE Transac-tions on Pattern Analysis and Machine Intellig-ence., vol. 36, no. 3, pp. 453 – 465, 2014.##[34] Felzenszwalb, P.F., Huttenlocher, D.P. "Pictorial Structures for Object Recognition.", International Journal of Computer Vision., vol. 61, no. 1, pp. 55–79, 2005.##[35] Fergus, R., Perona, P., Zisserman, A. "Object Class Recognition by Unsupervised Scale-Invar-iant Learning.", IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271, 2003.##[36] Weber, M., Welling, M., Perona, P. "Towards Automatic Discovery of Object Categories.", IEEE Conference on Computer Vision and Pattern Recognition, pp. 101–108, 2000.##[37] Yang, L., Luo, P., Loy, C.C., Tang, X. "A Large-Scale Car Dataset for Fine-Grained Categoriza-tion and Verification.", Proc. IEEE Conference on Computer Vision and Pattern Recognition., vol. 1, pp. 3973–3981, 2015.##[38] "NTOU-MMR Dataset," http://mmplab.cs.ntou.e-du.tw/mmplab/MMR/MMR.html (Accessed: 8 July 2016).##[39] "The PASCAL Visual Object Classes," [Online] 2008, http://pascallin.ecs.soton.ac.uk/challeng-es/VOC/ (Accessed: 10 March 2015).##[40] Chang, C., Lin, C. "LIBSVM : A Library for Support Vector Machines.", ACM Transactions on Intelligent Systems and Technology., vol. 2, no. 3, pp. 1–27, 2011.##[1] Sun, Z., George, B., Ronald, M. "On-Road Vehicle Detection: A Review.", IEEE Transac-tions on Pattern Analysis and Machine Intellig-ence., vol. 28, no. 5, pp. 694–711, 2006.##[2] Li, X., Guo, X. "A HOG Feature and SVM Based Method for Forward Vehicle Detection with Single Cam-era.", 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 263–266, 2013.##[3] Yousaf, K., Iftikhar, A., Javed, A. "Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey.", International Journal of Image, Graphics and Signal Processing., vol. 4, no. 9, pp. 52, 2012.##[4] Ambardekar, A., Nicolescu, M., Bebis, G., Nicol-escu, M. "Vehicle Classification Framework: A Comparative Study.", EURASIP Journal on Image and Video Processing., vol. 2014, no. 1, pp. 1–13, 2014.##[5] Zhang, B. "Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles.", IEEE Transactions on Intelligent Transportation Systems., vol. 14, no. 1, pp. 322–332, 2013.##[6] Dong, Z., Jia, Y. "Vehicle Type Classification Using Distributions of Structural and Appearance-Based Features.", IEEE International Conference on Image Processing, pp. 4321–4324, 2013.##[7] Choo, S., Mokhtarian, P.L. "What Type of Vehicle Do People Drive? The Role of Attitude and Lifestyle in Influencing Vehicle Type Choice.", Transportation Research Part A: Policy and Practice., vol. 38, no. 3, pp. 201–222, 2004.##[8] Conos, M. "Recognition of Vehicle Make from a Frontal View.", Master Thesis, Czech Tech, 2007.##[9] Dlagnekov, L. "Video-Based Car Surveillance: License Plate, Make, and Model Recognition.", Master Thesis, University of California, San Diego, 2005.##[10] Negri, P., Clady, X., Milgram, M., Poulenard, R. "An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification.", 18th International Conference on Pattern Recognition, pp. 574–577, 2006.##[11] Clady, X., Negri, P., Milgram, M., Poulenard, R. "Multi-Class Vehicle Type Recognition System.", Artificial Neural Networks in Pattern Recogni-tion, Lecture Notes in Computer Science, pp. 228–239, Springer Berlin Heidelberg, 2008.##[12] Huang, H., Zhao, Q., Jia, Y., Tang, S. "A 2DLDA Based Algorithm for Real Time Vehicle Type Recognition.", 11th International IEEE Confer-ence on Intelligent Transportation Systems, pp. 298–303, 2008.##[13] Pearce, G., Pears, N. "Automatic Make and Model Recognition from Frontal Images of Cars.", 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 373–378, 2011.##[14] Saravi, S., Edirisinghe, E. a. "Vehicle Make and Model Recognition in CCTV Footage.", 18th International Conference on Digital Signal Processing, pp. 1–6, 2013.##[15] Lowe, D.G. "Object Recognition from Local Scale-Invariant Features.", 17th IEEE Interna-tional Conference on Computer Vision, pp. 1150–1157, 1999.##[16] Petrovic, V., Cootes, T. "Analysis of Features for Rigid Structure Vehicle Type Recognition.", British Machine Vision Conference, pp. 587–596, 2004.##[17] Munroe, D.T., Madden, M.G. "Multi-Class and Single-Class Classification Approaches to Veh-icle Model Recognition from Images.", AICS '05, pp. 93–102, 2005.##[18] Kazemi, F.M., Samadi, S., Poorreza, H.R., Akbarzadeh-T, M.-R. "Vehicle Recognition Us-ing Curvelet Transform and SVM.", 4th Interna-tional Conference on Information Techno-logy, pp. 516–521, 2007.##[19] Zafar, I., Edirisinghe, E. a., Acar, B.S. "Localised Contourlet Features in Vehicle Make and Model Recognition.", Image Processing: Machine Vision Applications II, Proc. of SPIE-IS&T Electronic Imaging, pp. 725105–725115, 2009.##[20] Hsieh, J.-W., Chen, L.-C., Chen, D.-Y. "Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition.", IEEE Transactions on Intelligent Transportation Systems., vol. 15, no. 1, pp. 6–20, 2014.##[21] Nazemi, A., Shafiee, M., Azimifar, Z. "On Road Vehicle Make and Model Recognition via Sparse Feature Coding.", 8th Iranian Conference on Machine Vision and Image Processing, pp. 436–440, 2013.##[22] Baran, R., Glowacz, A., Matiolanski, A. "The Efficient Real- and Non-Real-Time Make and Model Recognition of Cars.", Multimedia Tools and Applications., no. June, pp. 1–20, 2013.##[23] Gao, Y., Lee, H.J. "Moving Car Detection and Model Recognition Based on Deep Learning.", Advanced Science and Technology Letters., vol. 90, no. Multimedia, pp. 57–61, 2015.##[24] Siddiqui, A.J.A.M., Boukerche, A. "Towards Efficient Vehicle Classification in Intelligent Transportation Systems.", Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, pp. 19–25, 2015.##[25] Psyllos, A. "Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme.", Intelligent Transportation Systems, IEEE Transactions on., vol. 11, no. 2, pp. 322–328, 2010.##[26] Yang, H., Zhai, L., Liu, Z., Li, L., Luo, Y., Wang, Y., Lai, H., Guan, M. "An Efficient Method for Vehicle Model Identification via Logo Recognition.", International Conference on Computational and Information Sciences, pp. 1080–1083, 2013.##[27] Santos, D., Correia, P.L. "Car Recognition Based on Back Lights and Rear View Features.", 10th International Workshop on Image Analysis for Multimedia Interactive Services, pp. 137–140, 2009.##[28] Sarfraz, M.S., Saeed, A., Khan, M.H., Riaz, Z. "Bayesian Prior Models for Vehicle Make and Model Recognition.", Proceedings of the 6th International Conference on Frontiers of Information Technology - FIT '09, p. 6, ACM Press, New York, New York, USA, 2009.##[29] Psyllos, A., Anagnostopoulos, C.N., Kayafas, E., Loumos, V. "Image Processing & Artificial Neural Networks for Vehicle Make and Model Recognition.", 10th international conference on applications of advanced technologies in transportation, pp. 4229–4243, 2008.##[30] Llorca, D., Colas, D., Daza, I. "Vehicle Model Recognition Using Geometry and Appearance of Car Emblems from Rear View Images.", 17th IEEE International Conference on Intelligent Transportation Systems, pp. 3094–3099, 2014.##[31] Dalal, N., Triggs, B. "Histograms of Oriented Gradients for Human Detection.", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893, 2005.##[32] Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D. "Object Detection with Discriminatively Trained Part-Based Models.", IEEE transactions on pattern analysis and machine intelligence., vol. 32, no. 9, pp. 1627–45, 2010.##[33] Lampert, C., Nickisch, H., Harmeling, S. "Attribute-Based Classification for Zero-Shot Learning of Object Categories.", IEEE Transac-tions on Pattern Analysis and Machine Intellig-ence., vol. 36, no. 3, pp. 453 – 465, 2014.##[34] Felzenszwalb, P.F., Huttenlocher, D.P. "Pictorial Structures for Object Recognition.", International Journal of Computer Vision., vol. 61, no. 1, pp. 55–79, 2005.##[35] Fergus, R., Perona, P., Zisserman, A. "Object Class Recognition by Unsupervised Scale-Invar-iant Learning.", IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271, 2003.##[36] Weber, M., Welling, M., Perona, P. "Towards Automatic Discovery of Object Categories.", IEEE Conference on Computer Vision and Pattern Recognition, pp. 101–108, 2000.##[37] Yang, L., Luo, P., Loy, C.C., Tang, X. "A Large-Scale Car Dataset for Fine-Grained Categoriza-tion and Verification.", Proc. IEEE Conference on Computer Vision and Pattern Recognition., vol. 1, pp. 3973–3981, 2015.##[38] "NTOU-MMR Dataset," http://mmplab.cs.ntou.e-du.tw/mmplab/MMR/MMR.html (Accessed: 8 July 2016).##[39] "The PASCAL Visual Object Classes," [Online] 2008, http://pascallin.ecs.soton.ac.uk/challeng-es/VOC/ (Accessed: 10 March 2015).##[40] Chang, C., Lin, C. "LIBSVM : A Library for Support Vector Machines.", ACM Transactions on Intelligent Systems and Technology., vol. 2, no. 3, pp. 1–27, 2011.## ##

تشخیص آریتمی انقباضات زودرس بطنی در سیگنال الکتریکی قلب با استفاده ازترکیب طبقه‌بندها Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers 1 1 یکی از شایع‌ترین آریتمی­‌های قلبی که همواره مورد توجه پزشکان قرار گرفته‌است، آریتمی انقباضات زودرس بطنی‌ست که تشخیص آن با توجه به فراوانی‌اش در همه سنین، اهمیت ویژه­ای دارد. ثبت سیگنال الکتروکاردیوگرام یک روش متداول و غیر­تهاجمی برای بررسی نحوه عملکرد قلب است. توسعه روش‌های سریع و دقیق طبقه‌بندی خودکار الکتروکاردیوگرام برای تشخیص بالینی بیماری‌های قلبی بسیار ضروری است. هدف این پژوهش نیز، تحلیل سیگنال الکتریکی قلب به منظور طبقه‌­بندی آریتمی انقباضات زودرس بطنی‌ست. هیچ طبقه­‌بندی وجود ندارد که برای تمامی مسائل و در تمامی زمان­‌ها بهترین نتیجه را بدهد بنابراین؛ ترکیب طبقه‌­بند­ها باعث می­‌شود تا نتایج سامانه ترکیبی در مقایسه با تک‌تک این تکنیک­‌ها بهبود یابد. در این پژوهش از پایگاه داده MIT-BIH arrhythmia database به‌عنوان منبع داده‌ استفاده شده‌است. در این پژوهش برای تشخیص ضربان­­‌های زودرس بطنی در بیماران از ویژگی­‌های مورفولوژیکی الکتروکاردیوگرام و ویژگی­‌های به‌دست‌آمده از تبدیل موجک استفاده شده‌است و پس از استخراج و انتخاب ویژگی‌ها، برای طبقه‌­بندی ضربان‌­هااز ترکیب متداول­‌ترین روش‌­های طبقه‌­بندی، یعنی شبکه عصبی مصنوعی، ماشین بردار پشتیبان و روش  Kنزدیک‌ترین همسایه استفاده شده‌است. بهترین نتایج، در حالت ترکیب هر 3 طبقه‌بند و با استفاده از ویژگی­‌های هنجارسازی‌شده به‌ دست آمد. در این حالت سامانه ترکیبی طراحی‌شده موفق شد با صحت 2/0±9/98، حساسیت 1/0±0/99 و نرخ اختصاصی‌بودن 2/0±8/98 درصد ضربان­های زودرس بطنی را تشخیص دهد. همچنین، کارایی روش پیشنهادی در شرایط استفاده از نمونه­‌های آموزشی محدود نشان داده‌شد. در مجموع، نتایج نشان‌دهنده موفقیت روش پیشنهادی به‌ویژه در مقایسه با سایر پژوهش‌ها مرتبط است.   2 Cardiovascular diseases are the most dangerous diseases and one of the biggest causes of fatality all over the world. One of the most common cardiac arrhythmias which has been considered by physicians is premature ventricular contraction (PVC) arrhythmia. Detecting this type of arrhythmia due to its abundance of all ages, is particularly important. ECG signal recording is a non-invasive, popular method for an assessment of heart's function. Development of quick, accurate automatic ECG classification methods is essential for the clinical diagnosis of heart disease. This research analyzes the ECG signal to detect PVC arrhythmia. Different techniques are provided in order to detect this type of arrhythmia based on ECG signals. As these techniques use different methods for detection, the reaction of each one will be different to detect this type of arrhythmia. There is no classifier to give the best results for all matters at any time and combining classifiers improve the combined system results in comparison with each of the techniques. In this study, the MIT-BIH arrhythmia database is used as a data source. Two datasets are used for training; the first contains 2400 samples, as in other studies, and the second contains 600 samples, including normal and PVC beats. Morphological features and features obtained from wavelet transform used in a combined classifier were used afterwards, which is the combination of the most common classifiers namely artificial neural network, SVM and KNN for PVC beat classification. Statistical significance features were selected using the p-value approach and normalized them. The best results were obtained when combining all three classifiers and using normalized statistical significance features. The designed hybrid system succeeded to detect PVC beats with 98.9±0.2% accuracy, 99.0±0.1% sensitivity, and 98.8±0.2% specificity. Also, the efficiency of the proposed method was shown when using limited training samples. The results showed the success of the proposed approach, specifically in comparison with other related research studies.   55 70 2018/03/22016/06/22016/10/112017/01/20 1395/11/1 2017/10/252017/03/52017/06/102017/10/25 1396/8/3 مسعود رهبری پور Masoud Rahbaripour دانشگاه تربیت مدرس ایران masoud.rahbaripour@gmail.com بابک محمدزاده اصل Babak Mohammadzadeh Asl دانشگاه تربیت مدرس ایران babakmasl@modares.ac.ir Premature ventricular contraction ECG Morphological features Combined classifiers انقباضات زودرس بطنی الکتروکاردیوگرام ویژگی‌های مورفولوژیکی ترکیب طبقه‌بندها [1] Falik R. "Cardiology Essentials in Clinical Practice", JAMA, Vol. 306, no. 19, pp. 2162-3, Nov. 2011.##[2] Thaler MS. The only EKG book you'll ever need, Lippincott Williams & Wilkins, 2010.##[3] Clifford GD, Azuaje F, McSharry P. "Advanced methods and tools for ECG data analysis", Artech House, Inc. Sep. 2006.##[4] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. "A nonlinear Bayesian filtering frame-work for ECG denoising", IEEE Trans. Biom-ed. Eng., Vol. 54, no. 12, pp. 2172-85, Dec. 2007.##[5] Sameni R, Shamsollahi MB, Jutten C. "Model-based Bayesian filtering of cardiac contam-inants from biomedical recordings", Physiolo-gical Measurement, Vol. 29, no. 5, pp. 595, May. 2008.##[6] McSharry PE, Clifford GD, Tarassenko L, Smith LA. "A dynamical model for generating synthetic electrocardiogram signals", IEEE Trans. Biomed. Eng., Vol. 50, no. 3, pp. 289-94, Mar. 2003.##[7] Clifford GD, Shoeb A, McSharry PE, Janz BA. "Model-based filtering, compression and classification of the ECG", International Journal of Bioelectromagnetism, Vol. 7, no. 1, pp. 158-61, May. 2005.##[8] Sayadi O, Sameni R, Shamsollahi MB. "ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter". In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp. 2548-2551, IEEE, Aug. 2007.##[9] Sayadi O, Shamsollahi MB. "ECG denoising and compression using a modified extended Kalman filter structure", IEEE Trans. Biomed. Eng., Vol. 55, no. 9 pp. 2240-8, Sep. 2008.##[10] Sayadi O, Shamsollahi MB. "A model-based Bayesian framework for ECG beat segmenta-tion", Physiological measurement, Vol. 30, no.3, pp. 335, Feb. 2009.##[11] Ghorbanian P, Ghaffari A, Jalali A, Nataraj C. "Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier", IEEE In Computing in Cardiology, pp. 669-672, Sep. 2010.##[12] Inan OT, Giovangrandi L, Kovacs GT. "Robust neural-network-based classification of prema-ture ventricular contractions using wavelet transform and timing interval feature-es", IEEE Trans. Biomed. Eng., Vol. 53, no. 12, pp. 2507-15, Dec. 2006.##[13] Melgani F, Bazi Y. "Classification of electrocardiogram signals with support vector machines and particle swarm optimization", IEEE Trans. Information Technology in Biomedicine, Vol. 12, no. 5, pp. 667-77, Sep. 2008.##[14] Sayadi O, Shamsollahi MB, Clifford GD. "Robust detection of premature ventricular contractions using a wave-based Bayesian framework", IEEE Trans. Biomed. Eng., Vol. 57, no. 2, pp. 353-62, Feb. 2010.##[15] J. A. Gutiérrez-Gnecchi, R. Morfin-Maga˜na, D. Lorias-Espinoza, A. C. Tellez-Anguiano, E. Reyes-Archundia, A. Méndez-Pati˜no, R. Casta˜neda-Miranda, " DSP-based arrhythmia classification using wavelet transform and probabilistic neural network", Biomedical Signal Processing and Control, vol. 32, pp. 44-56, Feb. 2017.##[16] Christov I, Bortolan G. "Ranking of pattern recognition parameters for premature ventri-cular contractions classification by neural networks", Physiological Measurem-ent, Vol. 25, no. 5, pp. 1281, Aug. 2004.##[17] Bortolan G, Jekova I, Christov I. "Comparison of four methods for premature ventricular contraction and normal beat clustering", In Computers in Cardiology, pp. 921-924, IEEE. Sep. 2005.##[18] Ince T, Kiranyaz S, Gabbouj M. "Automated patient-specific classification of premature ventricular contractions", In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE., pp. 5474-5477, Aug. 2008.##[19] Zhou J. "Automatic detection of premature ventricular contraction using quantum neural networks", In Bioinformatics and Bioen-gineering, 2003. Proc. Third IEEE Sympo-sium, pp. 169-173, Mar. 2003.##[20] Osowski S, Linh TH. "ECG beat recognition using fuzzy hybrid neural network", IEEE Trans. Biomed. Eng., Vol. 48, no. 11, pp. 1265-71, Nov. 2001.##[21] Pasolli E, Melgani F. "Active learning methods for electrocardiographic signal classification", IEEE Trans. Information Technology in Biomedicine, Vol. 14, no. 6, pp.1405-16, Nov. 2010.##[22] Alajlan N, Bazi Y, Melgani F, Malek S, Bencherif MA. "Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods", Signal, Image and Video Processing, Vol. 8, no. 5, pp. 931-42, Jul. 2014.##[23] R. Zarei, J. He, G. Huang, Y. Zhang, "Effective and efficient detection of premature ventricular contractions based on variation of principal directions", Digital Signal Proces-sing, vol. 50, pp. 93-102, Mar. 2016.##[24] I. Kaur, R. Rajni, A. Marwaha, "ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform", J. Inst. Eng. India Ser. B., Vol. 97, no. 4, pp. 499-507, Dec. 2016.##[25] The MIT-BIH Arrhythmia Database. (2015, Oct. 8). [Online]. Available: http://physi-onet.org/physiobank/database/mitdb/##[26] L. I. Kuncheva, J. C. Bezdek, R. P. W. Duin, "Decision Templates for Multiple Classifier Fusion: An Experimental Comparison," Patt-ern Recognition, vol. 34, no. 2, pp. 299-314, 2001.##[27] Huang YS, Suen CY. "The behavior-knowledge space method for combination of multiple classifiers", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 347-347, Jun. 1993.##[28] M.A. Bagheri, Gh. Montazer, and E. Kabir, "A Subspace Approach to Error- Correcting Output Coding", Pattern Recognition Letters, vol. 34, pp. 176–184, 2013##[1] Falik R. "Cardiology Essentials in Clinical Practice", JAMA, Vol. 306, no. 19, pp. 2162-3, Nov. 2011.##[2] Thaler MS. The only EKG book you'll ever need, Lippincott Williams & Wilkins, 2010.##[3] Clifford GD, Azuaje F, McSharry P. "Advanced methods and tools for ECG data analysis", Artech House, Inc. Sep. 2006.##[4] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. "A nonlinear Bayesian filtering frame-work for ECG denoising", IEEE Trans. Biom-ed. Eng., Vol. 54, no. 12, pp. 2172-85, Dec. 2007.##[5] Sameni R, Shamsollahi MB, Jutten C. "Model-based Bayesian filtering of cardiac contam-inants from biomedical recordings", Physiolo-gical Measurement, Vol. 29, no. 5, pp. 595, May. 2008.##[6] McSharry PE, Clifford GD, Tarassenko L, Smith LA. "A dynamical model for generating synthetic electrocardiogram signals", IEEE Trans. Biomed. Eng., Vol. 50, no. 3, pp. 289-94, Mar. 2003.##[7] Clifford GD, Shoeb A, McSharry PE, Janz BA. "Model-based filtering, compression and classification of the ECG", International Journal of Bioelectromagnetism, Vol. 7, no. 1, pp. 158-61, May. 2005.##[8] Sayadi O, Sameni R, Shamsollahi MB. "ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter". In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp. 2548-2551, IEEE, Aug. 2007.##[9] Sayadi O, Shamsollahi MB. "ECG denoising and compression using a modified extended Kalman filter structure", IEEE Trans. Biomed. Eng., Vol. 55, no. 9 pp. 2240-8, Sep. 2008.##[10] Sayadi O, Shamsollahi MB. "A model-based Bayesian framework for ECG beat segmenta-tion", Physiological measurement, Vol. 30, no.3, pp. 335, Feb. 2009.##[11] Ghorbanian P, Ghaffari A, Jalali A, Nataraj C. "Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier", IEEE In Computing in Cardiology, pp. 669-672, Sep. 2010.##[12] Inan OT, Giovangrandi L, Kovacs GT. "Robust neural-network-based classification of prema-ture ventricular contractions using wavelet transform and timing interval feature-es", IEEE Trans. Biomed. Eng., Vol. 53, no. 12, pp. 2507-15, Dec. 2006.##[13] Melgani F, Bazi Y. "Classification of electrocardiogram signals with support vector machines and particle swarm optimization", IEEE Trans. Information Technology in Biomedicine, Vol. 12, no. 5, pp. 667-77, Sep. 2008.##[14] Sayadi O, Shamsollahi MB, Clifford GD. "Robust detection of premature ventricular contractions using a wave-based Bayesian framework", IEEE Trans. Biomed. Eng., Vol. 57, no. 2, pp. 353-62, Feb. 2010.##[15] J. A. Gutiérrez-Gnecchi, R. Morfin-Maga˜na, D. Lorias-Espinoza, A. C. Tellez-Anguiano, E. Reyes-Archundia, A. Méndez-Pati˜no, R. Casta˜neda-Miranda, " DSP-based arrhythmia classification using wavelet transform and probabilistic neural network", Biomedical Signal Processing and Control, vol. 32, pp. 44-56, Feb. 2017.##[16] Christov I, Bortolan G. "Ranking of pattern recognition parameters for premature ventri-cular contractions classification by neural networks", Physiological Measurem-ent, Vol. 25, no. 5, pp. 1281, Aug. 2004.##[17] Bortolan G, Jekova I, Christov I. "Comparison of four methods for premature ventricular contraction and normal beat clustering", In Computers in Cardiology, pp. 921-924, IEEE. Sep. 2005.##[18] Ince T, Kiranyaz S, Gabbouj M. "Automated patient-specific classification of premature ventricular contractions", In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE., pp. 5474-5477, Aug. 2008.##[19] Zhou J. "Automatic detection of premature ventricular contraction using quantum neural networks", In Bioinformatics and Bioen-gineering, 2003. Proc. Third IEEE Sympo-sium, pp. 169-173, Mar. 2003.##[20] Osowski S, Linh TH. "ECG beat recognition using fuzzy hybrid neural network", IEEE Trans. Biomed. Eng., Vol. 48, no. 11, pp. 1265-71, Nov. 2001.##[21] Pasolli E, Melgani F. "Active learning methods for electrocardiographic signal classification", IEEE Trans. Information Technology in Biomedicine, Vol. 14, no. 6, pp.1405-16, Nov. 2010.##[22] Alajlan N, Bazi Y, Melgani F, Malek S, Bencherif MA. "Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods", Signal, Image and Video Processing, Vol. 8, no. 5, pp. 931-42, Jul. 2014.##[23] R. Zarei, J. He, G. Huang, Y. Zhang, "Effective and efficient detection of premature ventricular contractions based on variation of principal directions", Digital Signal Proces-sing, vol. 50, pp. 93-102, Mar. 2016.##[24] I. Kaur, R. Rajni, A. Marwaha, "ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform", J. Inst. Eng. India Ser. B., Vol. 97, no. 4, pp. 499-507, Dec. 2016.##[25] The MIT-BIH Arrhythmia Database. (2015, Oct. 8). [Online]. Available: http://physi-onet.org/physiobank/database/mitdb/##[26] L. I. Kuncheva, J. C. Bezdek, R. P. W. Duin, "Decision Templates for Multiple Classifier Fusion: An Experimental Comparison," Patt-ern Recognition, vol. 34, no. 2, pp. 299-314, 2001.##[27] Huang YS, Suen CY. "The behavior-knowledge space method for combination of multiple classifiers", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 347-347, Jun. 1993.##[28] M.A. Bagheri, Gh. Montazer, and E. Kabir, "A Subspace Approach to Error- Correcting Output Coding", Pattern Recognition Letters, vol. 34, pp. 176–184, 2013## ##

حس‌نگار : شبکه واژگان حسی فارسی HesNegar: Persian Sentiment WordNet 1 1 وظیفه اصلی نظرکاوی استخراج و تشخیص حس مثبت یا منفی (رضایت‌مندی) افراد، از روی اطلاعات متنی است. نبود یک واژه‌نامه حسی فارسی عامل یکی از چالش‌‌های اصلی نظرکاوی در زبان فارسی است. در این مقاله روشی جدید برای تولید شبکه واژگان حسی فارسی (حس‌نگار) با استفاده از منابع زبانی فارسی و انگلیسی ارائه می‌شود. همچنین پیکره نظرات فارسی ایجاد‌شده برای انجام پژوهش‌های نظرکاوی، معرفی خواهند‌شد. برای تولید حس‌نگار ابتدا شبکه واژگان جامع زبان فارسی (فردوس‌نت) ساخته شده‌است. سپس میزان حس هر گروه هم‌معنی در شبکه واژگان حسی انگلیسی به کلمات متناظر آنها در حس‌نگار (شبکه واژگان حسی فارسی) نگاشت می‌شود. در آزمایش‌های انجام‌شده، مشخص شد که حس‌نگار دارای دقت 86/0 و نرخ بازیابی 75/0 است و می‌تواند به عنوان واژه‌نامه حسی مرجع برای زبان فارسی استفاده شود 2 Awareness of others' opinions plays a crucial role in the decision making process performed by simple customers to top-level executives of manufacturing companies and various organizations. Today, with the advent of Web 2.0 and the expansion of social networks, a vast number of texts related to people's opinions have been created. However, exploring the enormous amount of documents, various opinion sources and opposing opinions about an entity have made the process of extracting and analyzing opinions very difficult. Hence, there is a need for methods to explore and summarize the existing opinions. Accordingly, there has recently been a new trend in natural language processing science called "opinion mining". The main purpose of opinion mining is to extract and detect people’s positive or negative sentiments (sense of satisfaction) from text reviews. The absence of a comprehensive Persian sentiment lexicon is one of the main challenges of opinion mining in Persian. In this paper, a new methodology for developing Persian Sentiment WordNet (HesNegar) is presented using various Persian and English resources. A corpus of Persian reviews developed for opinion mining studies are introduced. To develop HesNegar, a comprehensive Persian WordNet (FerdowsNet), with high recall and proper precision (based on Princeton WordNet), was first created. Then, the polarity of each synset in English SentiWordNet is mapped to the corresponding words in HesNegar. In the conducted tests, it was found that HesNegar has a precision score of 0.86 a recall score of 0.75 and it can be used as a comprehensive Persian SentiWordNet. The findings and developments made in this study could prove useful in the advancement of opinion mining research in Persian and other similar languages, such as Urdu and Arabic.   71 86 2018/03/22016/06/22016/10/112017/01/202017/02/12 1395/11/24 2017/10/252017/03/52017/06/102017/10/252016/10/24 1395/8/3 احسان عسکریان Ehsan Asgarian دانشگاه فردوسی مشهد ایران ehsan.asgarian@gmail.com محسن کاهانی Mohsen Kahani دانشگاه فردوسی مشهد ایران kahani@um.ac.ir شهلا شریفی Shahla Sharifi دانشگاه فردوسی مشهد ایران sh-sharifi@um.ac.ir Opinion Mining FerdowsNet (Persian WordNet) Sentiment Lexicon Persian Text Processing Tools نظر‌کاوی شبکه واژگان فردوس‌نت واژه‌نامه حسی ابزارهای پردازش متون زبان فارسی [1] P. D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp. 417-424.##[2] B. Pang and L. Lee, "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales," presented at the Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005.##[3] E. Riloff and J. Wiebe, "Learning extraction patterns for subjective expressions," presented at the Proceedings of the 2003 conference on Empirical methods in natural language process-ing, 2003.##[4] S.-M. Kim and E. Hovy, "Extracting opinions, opinion holders, and topics expressed in online news media text," presented at the Proceedings of the Workshop on Sentiment and Subjectivity in Text, 2006.##[5] C. O. Alm, "Subjective natural language problems: motivations, applications, character-izations, and implications," presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, 2011.##[6] L. Barbosa and J. Feng, "Robust sentiment detection on twitter from biased and noisy data," presented at the Proceedings of the 23rd International Conference on Computational Lin-guistics: Posters, 2010.##[7] M. Abdul-Mageed, M. Diab, and M. Korayem, "Subjectivity and sentiment analysis of modern standard Arabic," presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.##[8] I. Habernal, T. Ptáček, and J. Steinberger, "Supervised sentiment analysis in Czech social media," Information Processing & Management, vol. 50, pp. 693-707, 2014.##[9] H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su, "OpinionIt: a text mining system for cross-lingual opinion analysis," in Proceedings of the 19th ACM international conference on Informa-tion and knowledge management, 2010, pp. 1199-1208.##[10] D. Gao, F. Wei, W. Li, X. Liu, and M. Zhou, "Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation," Computational Linguistics, vol. 41, pp. 21-40, 2015.##[11] M.-T. Martín-Valdivia, E. Martínez-Cámara, J.-M. Perea-Ortega, and L. Alfonso Ure-a-López, "Sentiment polarity detection in Spanish reviews combining supervised and unsupervised appro-aches," Expert Systems with Applications, vol. 40, pp. 3934-3942, 2012.##[12] C. Banea, R. Mihalcea, and J. Wiebe, "Porting Multilingual Subjectivity Resources Across Languages," IEEE Transactions on Affective Computing, vol. 4, 2013.##[13] A. Balahur and M. Turchi, "Comparative Experiments Using Supervised Learning and Machine Translation for Multilingual Sentiment Analysis," Computer Speech & Language, vol. 28, pp. 56–75, 2013.##[14] M. Okada and K. Hashimoto, "Investigation of Preprocessing of Multilingual Online Reviews for Automatic Classification," in Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on, 2012, pp. 306-309.##[15] X. Ding, B. Liu, and P. S. Yu, "A holistic lexicon-based approach to opinion mining," in Proceed-ings of the international conference on Web search and web data mining, 2008, pp. 231-240.##[16] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexicon-based methods for sentiment analysis," Computational linguistics, vol. 37, pp. 267-307, 2011.##[17] J. Kamps, M. Marx, R. J. Mokken, and M. De Rijke, "Using wordnet to measure semantic orientations of adjectives," in Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), 2004, pp. 1115-1118.##[18] A. Fahrni and M. Klenner, "Old wine or warm beer: Target-specific sentiment analysis of adjectives," in Proc. of the Symposium on Affective Language in Human and Machine, AISB, 2008, pp. 60-63.##[19] V. Hatzivassiloglou and K. R. McKeown, "Predicting the semantic orientation of adjec-tives," in Proceedings of the eighth confer-ence on European chapter of the Associa-tion for Computational Linguistics, 1997, pp. 174-181.##[20] N. Kaji and M. Kitsuregawa, "Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents," in EMNLP-CoNLL, 2007, pp. 1075-1083.##[21] L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R. McDonald, "The viability of web-derived polarity lexicons," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 777-785.##[22] H. Takamura, T. Inui, and M. Okumura, "Extracting semantic orientations of words using spin model," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, pp. 133-140.##[23] A. Esuli and F. Sebastiani, "Pageranking wordnet synsets: An application to opinion mining," presented at the Proceedings of the 43rd Annual Meeting on Association for Computational Lin-guistics (ACL), Prague, Czech Republic, 2007.##[24] D. Rao and D. Ravichandran, "Semi-supervised polarity lexicon induction," in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009, pp. 675-682.##[25] S. Poria, A. Gelbukh, A. Hussain, N. Howard, D. Das, and S. Bandyopadhyay, "Enhanced Sentic-Net with affective labels for concept-based opinion mining," IEEE Intelligent Systems, vol. 28, pp. 31-38, 2013.##[26] S. Gindl, A. Weichselbraun, and A. Scharl, "Extracting and Grounding Contextualized Sentiment Lexicons," 2013.##[27] D. Tang, F. Wei, B. Qin, M. Zhou, and T. Liu, "Building Large-Scale Twitter-Specific Senti-ment Lexicon: A Representation Learning Appr-oach," in the 25th International Conference on Computational Linguistics (COLING), 2014, pp. 172-182.##[28] S. Nofersti and M. Shamsfard, "Automatic building a corpus and exploiting it for polarity classification of indirect opinions about drugs.", in Journal of Signal and Data Processing (JSDP), 2016; 13 (2), pp.35-49.##[29] H. Kanayama and T. Nasukawa, "Fully automatic lexicon expansion for domain-oriented sentiment analysis," in Proceedings of the 2006 conference on empirical methods in natural language processing, 2006, pp. 355-363.##[30] A. Hassan and D. Radev, "Identifying text polarity using random walks," in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 395-403.##[31] A. Hassan, A. Abu-Jbara, R. Jha, and D. Radev, "Identifying the semantic orientation of foreign words," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, 2011, pp. 592-597.##[32] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages," Neural Networks, vol. 58, pp. 50-59, 2014.##[33] S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," in LREC, 2010, pp. 2200-2204.##[34] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of 5th International Conference on Language Resources and Evaluation (LREC), Genoa, 2006, pp. 417-422.##[35] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "SentiFul: A lexicon for sentiment ana-lysis," IEEE Transactions on Affective Com-puting, vol. 2, pp. 22-36, 2011.##[36] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "Textual affect sensing for sociable and expressive online communication," in Interna-tional Conference on Affective Computing and Intelligent Interaction, 2007, pp. 218-229.##[37] C. Strapparava and A. Valitutti, "WordNet Affect: an Affective Extension of WordNet," in Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC), 2004, pp. 1083-1086.##[38]] E. Cambria, R. Speer, C. Havasi, and A. Hussain, "SenticNet: A Publicly Available Semantic Resource for Opinion Mining," in AAAI fall symposium: commonsense knowledge, 2010.##[39] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A Framework for Sentiment Analysis in Persian," Open Transactions on Information Processing, vol. 1, pp. 1-14, 2014.##[40] F. Amiri, S. Scerri, and M. H. Khodashahi, "Lexicon-based Sentiment Analysis for Persian Text," in Recent Advances in Natural Language Processing, 2015, pp. 9-16.##[41] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 216-221.##[42] A.Mardani and S.A.Aghaie "A superviesd method for opinion mining in Persian using lexicon and SVM algorithm", in National Journal of Information Technology Management, 2015(7), pp. 345-362.##[43] S. Cerini, V. Compagnoni, A. Demontis, M. Formentelli, and G. Gandini, "Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining," Language resources and linguistic theory: Typo-logy, second language acquisition, English ling-uistics, pp. 200-210, 2007.##[44] A. Montejo-Ráez, E. Martínez-Cámara, M. T. Martín-Valdivia, and L. A. Ure-a-López, "Ranked wordnet graph for sentiment polarity classification in twitter," Computer Speech & Language, vol. 28, pp. 93-107, 2014.##[45] M. Montazery and H. Faili, "Automatic Persian wordnet construction," in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 846-850.##[46] K. N. Lam, F. A. Tarouti, and J. Kalita, "Automatically constructing Wordnet synsets," in 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), Ba-ltimore, USA, 2014.##[47] M. Shamsfard, A. Hesabi, H. Fadaei, N. Mansoory, A. Famian, S. Bagherbeigi, et al., "Semi automatic development of farsnet; the persian wordnet," in Proceedings of 5th Global WordNet Conference, Mumbai, India, 2010.##[48] P. Vossen, "A multilingual database with lexical semantic networks," Computational Linguistics vol. 25, pp. 628-630, 1998.##[49] F. Keyvan, H. Borjian, M. Kasheff, and C. Fellbaum, "Developing persianet: The persian wordnet," in 3rd Global wordnet conference, 2007, pp. 315-318.##[50] A. Famian and D. Aghajaney, "Towards Building a WordNet for Persian Adjectives," International Journal of lexicography, pp. 307-308, 2006.##[51] M. Fadaee, H. Ghader, H. Faili, and A. Shakery, "Automatic WordNet Construction Using Markov Chain Monte Carlo," Polibits, pp. 13-22, 2013.##[52] N. Taghizadeh and H. Faili, "Automatic Wordnet Development for Low-resource Languages using Cross-lingual WSD," Journal of Artificial Intelligence Research, vol. 56, pp. 61-87, 2016.##[53] F. Mahdisoltani, J. Biega, and F. Suchanek, "YAGO3: A knowledge base from multilingual Wikipedias," in 7th Biennial Conference on Innovative Data Systems Research, 2014.##[54] A. AleAhmad, H. Amiri, E. Darrudi, M. Rahgozar, and F. Oroumchian, "Hamshahri: A standard Persian text collection," Knowledge-Based Systems, vol. 22, pp. 382-387, 2009.##[55] H. Eghbalzadeh, B. Hosseini, S. Khadivi, and A. Khodabakhsh, "Persica: A Persian corpus for multi-purpose text mining and Natural language processing," in Telecommunications (IST), 2012 Sixth International Symposium on, 2012, pp. 1207-1214.##[56] A. Balali, A. Rajabi, S. Ghassemi, M. Asadpour, and H. Faili, "Content diffusion prediction in social networks," in 5th Conference on Informa-tion and Knowledge Technology (IKT), 2013, pp. 467-471.##[57] P. Turney, "Mining the web for synonyms: PMI-IR versus LSA on TOEFL," in 12th European Conference on Machine Learning (ECML 2001), Freiburg, Germany, 2001, pp. 491-502.##[58] K. Denecke, "Using sentiwordnet for multi-lingual sentiment analysis," in Data En-gineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on, 2008, pp. 507-512.##[59] C. M. Özsert and A. Özgür, "Word polarity detection using a multilingual approach," in Computational Linguistics and Intelligent Text Processing, ed: Springer, 2013, pp. 75-82.##[60] J. Steinberger, M. Ebrahim, M. Ehrmann, A. Hurriyetoglu, M. Kabadjov, P. Lenkova, et al., "Creating sentiment dictionaries via triangula-tion," Decision Support Systems, vol. 53, pp. 689-694, 2012.##[61] F. L. Cruz, J. A. Troyano, B. Pontes, and F. J. Ortega, "Building layered, multilingual sentim-ent lexicons at synset and lemma levels," Expert Systems with Applications, vol. 41, pp. 5984-5994, 2014.##[62] F. H. Mahyoub, M. A. Siddiqui, and M. Y. Dahab, "Building an Arabic Sentiment Lexicon Using Semi-Supervised Learning," Journal of King Saud University-Computer and Information Sciences, vol. 26, pp. 417-424, 2014.##[63] Y. Chen and S. Skiena, "Building sentiment lexicons for all major languages," in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), 2014, pp. 383-389.##[64] M. Shamsfard, "Challenges and open problems in Persian text processing," Proceedings of LTC, vol. 11, 2011.##[65] W. Feely, M. Manshadi, R. Frederking, and L. Levin, "The CMU METAL Farsi NLP App-roach," in Proceedings of the Ninth Interna-tional Conference on Language Resources and Evaluation (LREC'14), 2014, pp. 4052-4055.##[66] R. Duwairi and M. El-Orfali, "A study of the effects of preprocessing strategies on sentiment analysis for Arabic text," Journal of Information Science, vol. 40, pp. 501-513, 2014.##[67] W. Chamlertwat, P. Bhattarakosol, T. Rung-kasiri, and C. Haruechaiyasak, "Discover-ing Consumer Insight from Twitter via Sentiment Analysis," J. UCS, vol. 18, pp. 973-992, 2012.##[68] M.-T. Martín-Valdivia, E. Martínez-Cámara, J.-M. Perea-Ortega, and L. A. Ure-a-López, "Sentiment polarity detection in Spanish reviews combining supervised and unsupervised appro-aches," Expert Systems with Applications, vol. 40, pp. 3934-3942, 2013.##[69] K. Denecke, "Are SentiWordNet scores suited for multi-domain sentiment classification?," present-ed at the Fourth International Conference on Di-gital Information Management, (ICDIM 2009), 2009.##[1] P. D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp. 417-424.##[2] B. Pang and L. Lee, "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales," presented at the Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005.##[3] E. Riloff and J. Wiebe, "Learning extraction patterns for subjective expressions," presented at the Proceedings of the 2003 conference on Empirical methods in natural language process-ing, 2003.##[4] S.-M. Kim and E. Hovy, "Extracting opinions, opinion holders, and topics expressed in online news media text," presented at the Proceedings of the Workshop on Sentiment and Subjectivity in Text, 2006.##[5] C. O. Alm, "Subjective natural language problems: motivations, applications, character-izations, and implications," presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, 2011.##[6] L. Barbosa and J. Feng, "Robust sentiment detection on twitter from biased and noisy data," presented at the Proceedings of the 23rd International Conference on Computational Lin-guistics: Posters, 2010.##[7] M. Abdul-Mageed, M. Diab, and M. Korayem, "Subjectivity and sentiment analysis of modern standard Arabic," presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.##[8] I. Habernal, T. Ptáček, and J. Steinberger, "Supervised sentiment analysis in Czech social media," Information Processing & Management, vol. 50, pp. 693-707, 2014.##[9] H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su, "OpinionIt: a text mining system for cross-lingual opinion analysis," in Proceedings of the 19th ACM international conference on Informa-tion and knowledge management, 2010, pp. 1199-1208.##[10] D. Gao, F. Wei, W. Li, X. Liu, and M. Zhou, "Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation," Computational Linguistics, vol. 41, pp. 21-40, 2015.##[11] M.-T. Martín-Valdivia, E. Martínez-Cámara, J.-M. Perea-Ortega, and L. Alfonso Ure-a-López, "Sentiment polarity detection in Spanish reviews combining supervised and unsupervised appro-aches," Expert Systems with Applications, vol. 40, pp. 3934-3942, 2012.##[12] C. Banea, R. Mihalcea, and J. Wiebe, "Porting Multilingual Subjectivity Resources Across Languages," IEEE Transactions on Affective Computing, vol. 4, 2013.##[13] A. Balahur and M. Turchi, "Comparative Experiments Using Supervised Learning and Machine Translation for Multilingual Sentiment Analysis," Computer Speech & Language, vol. 28, pp. 56–75, 2013.##[14] M. Okada and K. Hashimoto, "Investigation of Preprocessing of Multilingual Online Reviews for Automatic Classification," in Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on, 2012, pp. 306-309.##[15] X. Ding, B. Liu, and P. S. Yu, "A holistic lexicon-based approach to opinion mining," in Proceed-ings of the international conference on Web search and web data mining, 2008, pp. 231-240.##[16] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexicon-based methods for sentiment analysis," Computational linguistics, vol. 37, pp. 267-307, 2011.##[17] J. Kamps, M. Marx, R. J. Mokken, and M. De Rijke, "Using wordnet to measure semantic orientations of adjectives," in Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), 2004, pp. 1115-1118.##[18] A. Fahrni and M. Klenner, "Old wine or warm beer: Target-specific sentiment analysis of adjectives," in Proc. of the Symposium on Affective Language in Human and Machine, AISB, 2008, pp. 60-63.##[19] V. Hatzivassiloglou and K. R. McKeown, "Predicting the semantic orientation of adjec-tives," in Proceedings of the eighth confer-ence on European chapter of the Associa-tion for Computational Linguistics, 1997, pp. 174-181.##[20] N. Kaji and M. Kitsuregawa, "Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents," in EMNLP-CoNLL, 2007, pp. 1075-1083.##[21] L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R. McDonald, "The viability of web-derived polarity lexicons," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 777-785.##[22] H. Takamura, T. Inui, and M. Okumura, "Extracting semantic orientations of words using spin model," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, pp. 133-140.##[23] A. Esuli and F. Sebastiani, "Pageranking wordnet synsets: An application to opinion mining," presented at the Proceedings of the 43rd Annual Meeting on Association for Computational Lin-guistics (ACL), Prague, Czech Republic, 2007.##[24] D. Rao and D. Ravichandran, "Semi-supervised polarity lexicon induction," in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009, pp. 675-682.##[25] S. Poria, A. Gelbukh, A. Hussain, N. Howard, D. Das, and S. Bandyopadhyay, "Enhanced Sentic-Net with affective labels for concept-based opinion mining," IEEE Intelligent Systems, vol. 28, pp. 31-38, 2013.##[26] S. Gindl, A. Weichselbraun, and A. Scharl, "Extracting and Grounding Contextualized Sentiment Lexicons," 2013.##[27] D. Tang, F. Wei, B. Qin, M. Zhou, and T. Liu, "Building Large-Scale Twitter-Specific Senti-ment Lexicon: A Representation Learning Appr-oach," in the 25th International Conference on Computational Linguistics (COLING), 2014, pp. 172-182.##[28] س. نوفرستی, س., و م. شمس فرد، "ساخت نیمه‌خودکار یک پیکره از نظرات غیرمستقیم در دامنه دارو و به‌کارگیری آن برای تعیین قطبیت نظرات". چاپ شده در مجله پردازش و علائم داده‌ها، دوره 13 (2)، سال 1395، 35-49.##[28] S. Nofersti and M. Shamsfard, "Automatic building a corpus and exploiting it for polarity classification of indirect opinions about drugs.", in Journal of Signal and Data Processing (JSDP), 2016; 13 (2), pp.35-49.##[29] H. Kanayama and T. Nasukawa, "Fully automatic lexicon expansion for domain-oriented sentiment analysis," in Proceedings of the 2006 conference on empirical methods in natural language processing, 2006, pp. 355-363.##[30] A. Hassan and D. Radev, "Identifying text polarity using random walks," in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 395-403.##[31] A. Hassan, A. Abu-Jbara, R. Jha, and D. Radev, "Identifying the semantic orientation of foreign words," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, 2011, pp. 592-597.##[32] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages," Neural Networks, vol. 58, pp. 50-59, 2014.##[33] S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," in LREC, 2010, pp. 2200-2204.##[34] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of 5th International Conference on Language Resources and Evaluation (LREC), Genoa, 2006, pp. 417-422.##[35] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "SentiFul: A lexicon for sentiment ana-lysis," IEEE Transactions on Affective Com-puting, vol. 2, pp. 22-36, 2011.##[36] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "Textual affect sensing for sociable and expressive online communication," in Interna-tional Conference on Affective Computing and Intelligent Interaction, 2007, pp. 218-229.##[37] C. Strapparava and A. Valitutti, "WordNet Affect: an Affective Extension of WordNet," in Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC), 2004, pp. 1083-1086.##[38]] E. Cambria, R. Speer, C. Havasi, and A. Hussain, "SenticNet: A Publicly Available Semantic Resource for Opinion Mining," in AAAI fall symposium: commonsense knowledge, 2010.##[39] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A Framework for Sentiment Analysis in Persian," Open Transactions on Information Processing, vol. 1, pp. 1-14, 2014.##[40] F. Amiri, S. Scerri, and M. H. Khodashahi, "Lexicon-based Sentiment Analysis for Persian Text," in Recent Advances in Natural Language Processing, 2015, pp. 9-16.##[41] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 216-221.##[42]س.ع. مردانی و ع. آقایی، "ارائۀ روش نظارتی برای نظرکاوی در زبان فارسی با استفاده از لغت‌نامه و الگوریتم SVM"، فصلنامه علمی-پژوهشی مدیریت فناوری اطلاعات, دوره 7 (2)، 1393، 345-362.##[42] A.Mardani and S.A.Aghaie "A superviesd method for opinion mining in Persian using lexicon and SVM algorithm", in National Journal of Information Technology Management, 2015(7), pp. 345-362.##[43] S. Cerini, V. Compagnoni, A. Demontis, M. Formentelli, and G. Gandini, "Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining," Language resources and linguistic theory: Typo-logy, second language acquisition, English ling-uistics, pp. 200-210, 2007.##[44] A. Montejo-Ráez, E. Martínez-Cámara, M. T. Martín-Valdivia, and L. A. Ure-a-López, "Ranked wordnet graph for sentiment polarity classification in twitter," Computer Speech & Language, vol. 28, pp. 93-107, 2014.##[45] M. Montazery and H. Faili, "Automatic Persian wordnet construction," in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 846-850.##[46] K. N. Lam, F. A. Tarouti, and J. Kalita, "Automatically constructing Wordnet synsets," in 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), Ba-ltimore, USA, 2014.##[47] M. Shamsfard, A. Hesabi, H. Fadaei, N. Mansoory, A. Famian, S. Bagherbeigi, et al., "Semi automatic development of farsnet; the persian wordnet," in Proceedings of 5th Global WordNet Conference, Mumbai, India, 2010.##[48] P. Vossen, "A multilingual database with lexical semantic networks," Computational Linguistics vol. 25, pp. 628-630, 1998.##[49] F. Keyvan, H. Borjian, M. Kasheff, and C. Fellbaum, "Developing persianet: The persian wordnet," in 3rd Global wordnet conference, 2007, pp. 315-318.##[50] A. Famian and D. Aghajaney, "Towards Building a WordNet for Persian Adjectives," International Journal of lexicography, pp. 307-308, 2006.##[51] M. Fadaee, H. Ghader, H. Faili, and A. Shakery, "Automatic WordNet Construction Using Markov Chain Monte Carlo," Polibits, pp. 13-22, 2013.##[52] N. Taghizadeh and H. Faili, "Automatic Wordnet Development for Low-resource Languages using Cross-lingual WSD," Journal of Artificial Intelligence Research, vol. 56, pp. 61-87, 2016.##[53] F. Mahdisoltani, J. Biega, and F. Suchanek, "YAGO3: A knowledge base from multilingual Wikipedias," in 7th Biennial Conference on Innovative Data Systems Research, 2014.##[54] A. AleAhmad, H. Amiri, E. Darrudi, M. Rahgozar, and F. Oroumchian, "Hamshahri: A standard Persian text collection," Knowledge-Based Systems, vol. 22, pp. 382-387, 2009.##[55] H. Eghbalzadeh, B. Hosseini, S. Khadivi, and A. Khodabakhsh, "Persica: A Persian corpus for multi-purpose text mining and Natural language processing," in Telecommunications (IST), 2012 Sixth International Symposium on, 2012, pp. 1207-1214.##[56] A. Balali, A. Rajabi, S. Ghassemi, M. Asadpour, and H. Faili, "Content diffusion prediction in social networks," in 5th Conference on Informa-tion and Knowledge Technology (IKT), 2013, pp. 467-471.##[57] P. Turney, "Mining the web for synonyms: PMI-IR versus LSA on TOEFL," in 12th European Conference on Machine Learning (ECML 2001), Freiburg, Germany, 2001, pp. 491-502.##[58] K. Denecke, "Using sentiwordnet for multi-lingual sentiment analysis," in Data En-gineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on, 2008, pp. 507-512.##[59] C. M. Özsert and A. Özgür, "Word polarity detection using a multilingual approach," in Computational Linguistics and Intelligent Text Processing, ed: Springer, 2013, pp. 75-82.##[60] J. Steinberger, M. Ebrahim, M. Ehrmann, A. Hurriyetoglu, M. Kabadjov, P. Lenkova, et al., "Creating sentiment dictionaries via triangula-tion," Decision Support Systems, vol. 53, pp. 689-694, 2012.##[61] F. L. Cruz, J. A. Troyano, B. Pontes, and F. J. Ortega, "Building layered, multilingual sentim-ent lexicons at synset and lemma levels," Expert Systems with Applications, vol. 41, pp. 5984-5994, 2014.##[62] F. H. Mahyoub, M. A. Siddiqui, and M. Y. Dahab, "Building an Arabic Sentiment Lexicon Using Semi-Supervised Learning," Journal of King Saud University-Computer and Information Sciences, vol. 26, pp. 417-424, 2014.##[63] Y. Chen and S. Skiena, "Building sentiment lexicons for all major languages," in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), 2014, pp. 383-389.##[64] M. Shamsfard, "Challenges and open problems in Persian text processing," Proceedings of LTC, vol. 11, 2011.##[65] W. Feely, M. Manshadi, R. Frederking, and L. Levin, "The CMU METAL Farsi NLP App-roach," in Proceedings of the Ninth Interna-tional Conference on Language Resources and Evaluation (LREC'14), 2014, pp. 4052-4055.##[66] R. Duwairi and M. El-Orfali, "A study of the effects of preprocessing strategies on sentiment analysis for Arabic text," Journal of Information Science, vol. 40, pp. 501-513, 2014.##[67] W. Chamlertwat, P. Bhattarakosol, T. Rung-kasiri, and C. Haruechaiyasak, "Discover-ing Consumer Insight from Twitter via Sentiment Analysis," J. UCS, vol. 18, pp. 973-992, 2012.##[68] M.-T. Martín-Valdivia, E. Martínez-Cámara, J.-M. Perea-Ortega, and L. A. Ure-a-López, "Sentiment polarity detection in Spanish reviews combining supervised and unsupervised appro-aches," Expert Systems with Applications, vol. 40, pp. 3934-3942, 2013.##[69] K. Denecke, "Are SentiWordNet scores suited for multi-domain sentiment classification?," present-ed at the Fourth International Conference on Di-gital Information Management, (ICDIM 2009), 2009.## ##

ارایه یک پیکره‌ پرسش و پاسخ مذهبی در زبان فارسی Providing a Religious Corpus of Question Answering System in Persian 1 1 سامانه‌های پرسش و پاسخ، زیرشاخه‌ای از علوم پردازش زبان طبیعی و بازیابی اطلاعات محسوب می‌شوند که در چند دهه‌ اخیر مورد علاقه زیاد پژوهش‌گران قرار‌گرفته‌اند. با توجه به رشد فزاینده‌ علاقه‌مندی به این زمینه‌ پژوهشی، نیاز به در‌اختیار‌داشتن منابع داده‌ای مناسب برای آن، به‌خوبی احساس می‌شود. تاکنون اغلب پژوهش‌های صورت‌گرفته در رابطه با توسعه‌ پیکره پرسش و پاسخ در زبان انگلیسی بوده است؛ درصورتی‌که در زبان‌های دیگر مانند فارسی، نیاز شدیدی به وجود چنین پیکره‌هایی احساس می‌شود. در این مقاله، مراحل کامل توسعه‌ یک پیکره متنی پرسش و پاسخ با نام رسائل و مسائل در زبان فارسی شرح داده خواهد شد. این پیکره شامل 2,118 سؤال غیرحقیقت و 2,051 سؤال حقیقت بوده که برای هر سؤال، متن سؤال، نوع سؤال، سختی سؤال از نظر پرسشگر و پاسخ‌دهنده، طبقه معنایی پاسخ در سطح درشت‌دانه و ریزدانه، پاسخ دقیق سؤال و شماره صفحه و پاراگراف پاسخ، نشانه‌گذاری شده است. پیکره پیشنهادی برای یادگیری کلیه مؤلفه‌های سامانه‌های پرسش و پاسخ شامل دسته‌بندی سؤال، بازیابی اطلاعات و استخراج پاسخ، مورد‌استفاده می‌تواند قرار‌ گیرد و به‌صورت رایگان در دسترس پژوهش‌گران قرار دارد. در ادامه یک سامانه پرسش و پاسخ بر روی پیکره رسائل و مسائل معرفی می‌شود. نتایج نشان می‌دهد که سامانه پیشنهادی توانسته است به دقت 29/82 و میانگین معکوس رتبه 73/56 درصد دست یابد. می‌توان اظهار کرد که پیکره و سامانه پیشنهادی در نوع خود، نخستین پیکره و سامانه مربوط به پرسش و پاسخ با چنین ویژگی‌هایی برای زبان فارسی است. 2 Question answering system is a field in natural language processing and information retrieval noticed by researchers in these decades. Due to a growing interest in this field of research, the need to have appropriate data sources is perceived. Most researches about developing question answering corpus area have been done in English so far, but in other languages as Persian, the lack of these corpora is perceived. In this article, the development of a Persian question answering corpus called Rasayel&massayel will be discussed. This corpus consists of 2,118 non-factoid and 2,051 factoid questions that for each question, question text, question type, question difficulty from questioner and responder’s perspective, expected answer type in coarse-grained and fine-grained level, exact answer, and page and paraghraph number of answer are annotated. The prposed corpus can be applied to learn components of question answering system, including question classification, information retrieval, and answer extraction. This corpus is freely available for the academic purpose as well. In the following, a question answering system is presented on the Rasayel&massayel corpus. Our experimental result represents that the intended proposed system has achieved 82.29 % accuracy and 56.73 % mean reciprocal rank. It could be also claimed that this is the first ever question answering system and corpus with such features in Persian.   87 102 2018/03/22016/06/22016/10/112017/01/202017/02/122017/03/10 1395/12/20 2017/10/252017/03/52017/06/102017/10/252016/10/242018/03/6 1396/12/15 یاسمن برشبان Yasaman Boreshban دانشگاه گیلان ایران yas.boreshban@gmail.com حامد یوسفی نسب Hamed Yousefinasab دانشگاه گیلان ایران hdyousefi@gmail.com سیدابوالقاسم میرروشندل Seyed Abolghasem Mirroshandel دانشگاه گیلان ایران mirroshandel@gmail.com Question answering system Natural language processing Information retrieval Rasayel&massayel corpus سامانه‌های پرسش و پاسخ پردازش زبان طبیعی بازیابی اطلاعات پیکره رسائل و مسائل [1] (Hosseini, P. et al., "Persian Sentiment Analysis Corpus: Developing a textual sentiment corpus for Persia". Third conference on Computational Linguistics, Sharif University of Technology, 2014.)##[2] (Ghaemi, .H, kahani, .M. "Question Classification using nsemble Classifiers ", JSDP, 13 (3), 2016, 99-112)##[3] AleAhmad, A. et al., "Hamshahri: A standard Persian text collection", Knowledge-Based System-s, 22(5), 2009, pp.382–387.##[4] Bijankhan, M. et al., "Lessons from building a Per-sian written corpus: Peykare". Language Resourc-es and Evaluation, 45(2), 2010, pp.143–164.##[5] Greenwood, M.A., "Open-domain question answering", Foundations and Trends in Informa-tion Retrieval, 2005.##[6] Gupta, P. and Gupta, V.,"A survey of text question answering techniques", International Journal of Computer Applications, 53(4), 2012, pp.1–8.##[7] Hirschman, L. and Gaizauskas, R., "Natural language question answering: the view from here", Natural Language Engineering, 7(04), 2000, pp.275–300.##[8] Kolomiyets, O. and Moens, M.-F., "A survey on question answering technology from an informa-tion retrieval perspective", Information Sciences, 181(24), 2011, pp.5412–5434.##[9] Lee, G. et al., "SiteQ: Engineering High Performance QA System Using", Lexico-Semantic Pattern Matching and Shallow NLP. In TREC, 2001.##[10] Li, X. and Roth, D., "Learning question classifiers", In Proceedings of the 19th interna-tional conference on Computational linguistics., 2002.##[11] Li, X. and Roth, D., "Learning question classifiers: the role of semantic information", Natural Language Engineering, 12(03), 2006, pp.229–249.##[12] Magnini, B. et al., "Creating the DISEQuA corpus: a test set for multilingual question answering", In Comparative Evaluation of Multilingual Information Access Systems, 2004, pp. 487–500.##[13] Manning, C.D., Raghavan, P. and Schütze, H., "Introduction to information retrieval", Cambr-idge university press Cambridge , 2008.##[14] Moghaddas, B.B. et al, "Pasokh: A standard corpus for the evaluation of Persian text summarizers". In Computer and Knowledge Engineering (ICCKE), 2013, pp. 471–475.##[15] Moll, D. and Vicedo, L., "Question Answering in Restricted Domains : An Overview", Computa-tional Linguistics, 2007.##[16] Mollaei, A., Rahati-Quchani, S. and Estaji, A., "Question classification in Persian language based on conditional random fields", 2nd International eConference on Computer and Knowledge Engineering (ICCKE), 2012, pp.295–300.##[17] Rasooli, M.S et al., "Development of a Persian syntactic dependency treebank". In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013, pp. 306–314.##[18] Smith, N.A., Heilman, M. and Hwa, R., "Question generation as a competitive undergraduate course project", In Proceedings of the NSF Workshop on the Question Generation Shared Task and Evaluation Challenge, 2008, pp. 4–6.##[19] Tellex, S. et al., "Quantitative evaluation of passage retrieval algorithms for question answering", In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 41–47.##[20] Tom, D. et al., "TrainQA: a Training Corpus for Corpus-Based Question Answering Systems", In Proc. 8th Int. Conf. on Computational Linguistics and Intelligent Text Processing, 2007, pp. 1–7.##[21] Voorhees, E.M., "Building a question answering test collection", ACM SIGIR, 2000.##[22] Voorhees, E.M., "The TREC-8 Question Answering Track Report", In TREC, 1999, pp. 77–82.##[23]Yaghoobzadeh, Y. et al., "ISO-TimeML Event Extraction in Persian Text",COLING, 2012, pp.2931-2944.##[24]Zhang, D. and Lee, W.S., "Question classification using support vector machines", In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 26–32.##[1] حسینی، پدرام و همکاران، "پیکره فارسی تحلیل احساس سِنتی‌پِرس: توسعه یک پیکره تحلیل احساس متنی برای زبان فارسی". سومین کنفرانس زبانشناسی رایانشی, 1393.##[1] (Hosseini, P. et al., "Persian Sentiment Analysis Corpus: Developing a textual sentiment corpus for Persia". Third conference on Computational Linguistics, Sharif University of Technology, 2014.)##[2] قائمی، هادی، کاهانی، محسن. "دسته‌بندی پرسش‌ها با استفاده از ترکیب دسته‌بندها". پردازش علائم و داده‌ها. ۱۳۹۵; ۱۳ (۳) :۹۹-۱۱۲##[2] (Ghaemi, .H, kahani, .M. "Question Classification using nsemble Classifiers ", JSDP, 13 (3), 2016, 99-112)##[3] AleAhmad, A. et al., "Hamshahri: A standard Persian text collection", Knowledge-Based System-s, 22(5), 2009, pp.382–387.##[4] Bijankhan, M. et al., "Lessons from building a Per-sian written corpus: Peykare". Language Resourc-es and Evaluation, 45(2), 2010, pp.143–164.##[5] Greenwood, M.A., "Open-domain question answering", Foundations and Trends in Informa-tion Retrieval, 2005.##[6] Gupta, P. and Gupta, V.,"A survey of text question answering techniques", International Journal of Computer Applications, 53(4), 2012, pp.1–8.##[7] Hirschman, L. and Gaizauskas, R., "Natural language question answering: the view from here", Natural Language Engineering, 7(04), 2000, pp.275–300.##[8] Kolomiyets, O. and Moens, M.-F., "A survey on question answering technology from an informa-tion retrieval perspective", Information Sciences, 181(24), 2011, pp.5412–5434.##[9] Lee, G. et al., "SiteQ: Engineering High Performance QA System Using", Lexico-Semantic Pattern Matching and Shallow NLP. In TREC, 2001.##[10] Li, X. and Roth, D., "Learning question classifiers", In Proceedings of the 19th interna-tional conference on Computational linguistics., 2002.##[11] Li, X. and Roth, D., "Learning question classifiers: the role of semantic information", Natural Language Engineering, 12(03), 2006, pp.229–249.##[12] Magnini, B. et al., "Creating the DISEQuA corpus: a test set for multilingual question answering", In Comparative Evaluation of Multilingual Information Access Systems, 2004, pp. 487–500.##[13] Manning, C.D., Raghavan, P. and Schütze, H., "Introduction to information retrieval", Cambr-idge university press Cambridge , 2008.##[14] Moghaddas, B.B. et al, "Pasokh: A standard corpus for the evaluation of Persian text summarizers". In Computer and Knowledge Engineering (ICCKE), 2013, pp. 471–475.##[15] Moll, D. and Vicedo, L., "Question Answering in Restricted Domains : An Overview", Computa-tional Linguistics, 2007.##[16] Mollaei, A., Rahati-Quchani, S. and Estaji, A., "Question classification in Persian language based on conditional random fields", 2nd International eConference on Computer and Knowledge Engineering (ICCKE), 2012, pp.295–300.##[17] Rasooli, M.S et al., "Development of a Persian syntactic dependency treebank". In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013, pp. 306–314.##[18] Smith, N.A., Heilman, M. and Hwa, R., "Question generation as a competitive undergraduate course project", In Proceedings of the NSF Workshop on the Question Generation Shared Task and Evaluation Challenge, 2008, pp. 4–6.##[19] Tellex, S. et al., "Quantitative evaluation of passage retrieval algorithms for question answering", In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 41–47.##[20] Tom, D. et al., "TrainQA: a Training Corpus for Corpus-Based Question Answering Systems", In Proc. 8th Int. Conf. on Computational Linguistics and Intelligent Text Processing, 2007, pp. 1–7.##[21] Voorhees, E.M., "Building a question answering test collection", ACM SIGIR, 2000.##[22] Voorhees, E.M., "The TREC-8 Question Answering Track Report", In TREC, 1999, pp. 77–82.##[23]Yaghoobzadeh, Y. et al., "ISO-TimeML Event Extraction in Persian Text",COLING, 2012, pp.2931-2944.##[24]Zhang, D. and Lee, W.S., "Question classification using support vector machines", In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 26–32.## ##

تخمین بدون کاف فشارخون مبتنی بر ویژگی‌های زمانی سیگنال نبض Cuff-less Blood Pressure Estimation Based on Temporal Feature of PPG Signal 1 1 فشارخون به‌عنوان یکی از علائم حیاتی بدن، نیاز به کنترل متناوب و پیوسته دارد. این ضرورت به‌خصوص در شرایط مراقبت ویژه برای بیماران بیشتر احساس می‌شود. در این پژوهش سعی شده است که روشی غیرتهاجمی، بدون استفاده از کاف و بدون نیاز به کالیبراسیون فردی پیشنهاد شود. به این منظور با استفاده از دو حس‌گر نوری بر روی انگشت و مچ دست، سیگنال فتوپلتیسموگرافی از بیست داوطلب سالم در شرائط فشارخون مختلف، ثبت شد و سپس به‌منظور افزایش دقت تخمین و نیز عدم نیاز به کالیبراسیون فردی، علاوه بر زمان گذار نبض، 16 ویژگی زمانی از سیگنال فتوپلتیسموگرافی مچ استخراج شد. در‌نهایت با بررسی تخمین گرهای مختلف، از شبکه عصبی رگرسیون عمومی برای تخمین فشارخون استفاده شد. خطای فشارخون تخمین زده‌شده توسط این تخمین‌گر، در فشار سیستول 11/0  ±18/1  و در فشار دیاستول 3/2 ±15/0 میلی‌متر جیوه است. 2 Blood pressure is one of the vital signs. Specially, it is crucial for some cases such as hypertension patients and it should be monitored continuously in ICU/CCU. It must be noted that current systems to measure blood pressure, often require trained operators. As an example, in post-hospital cares, blood pressure control is difficult except with the presence of a nurse or use of a device that minimizes the patient's involvement in the measurements. In this way, Photoplotysmography (PPG), which is a noninvasive method for pulse wave recording, seems to be ideal to make simple tools for blood pressure measurement in home care. In other words, it is so helpful or rather necessary to design a non-invasive, cuff-less, subject-independent system for blood pressure measurement. In this study, two optical sensors were located on the finger and the wrist. Twenty healthy volunteers in different situations were examined to record PPG signals. Also, blood pressure values were measured by cuff-based noninvasive blood pressure system on left arm as a reference value. Recorded signals were filtered and processed in MATLAB R2014a software. To promote the estimation accuracy and subject-independency, 16 temporal features in addition to the pulse transit time (PTT) were extracted from the wrist PPG signal. To estimate blood pressure values, three neural networks were used as the estimator: Feedforward Neural Network (FFN), Redial Basis Function Neural Network (RBFN) and General Regression Neural Network (GRNN). After comparison of their results; the General Regression Neural Network was used for blood pressure estimation. The MSE errors estimated by the best estimator, were 0.11±1.18 mmHg and 0.15±2.3 mmHg for systole and diastole pressure respectively. 103 114 2018/03/22016/06/22016/10/112017/01/202017/02/122017/03/102016/09/20 1395/6/30 2017/10/252017/03/52017/06/102017/10/252016/10/242018/03/62017/03/5 1395/12/15 مینا شهابی mina shahabi سازمان پژوهش های علمی و صنعتی ایران- پژوهشکده برق و فناوری اطلاعات ایران shahabi87_m@yahoo.com وحیدرضا نفیسی Vahid reza Nafisi سازمان پژوهش های علمی و صنعتی ایران- پژوهشکده برق و فناوری اطلاعات ایران vr_nafisi@irost.org Blood Pressure Monitoring Photoplethysmography (PPG) Pulse Transit Time (PTT) Neural Network مانیتورینگ فشارخون فتوپلتیسموگرافی زمان گذار نبض شبکه عصبی [1] Shelley, Kirk H. "Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate." Anesthesia & Analgesia 105, no. 6 (2007): 31-36.##[2] Alnaeb, Mohamad E., Nasser Alobaid, Alexander M. Seifalian, Dimitri P. Mikhailidis, and George Hamilton. "Optical techniques in the assessment of peripheral arterial disease." Current vascular pharmacology 5, no. 1 (2007): 53-59.##[3] Tamura, Toshiyo, Yuka Maeda, Masaki Sekine, and Masaki Yoshida. "Wearable photoplethysmographic sensors—past and present." Electronics3, no. 2 (2014): 282-302.##[4] Kamalzade Shiva, Thesis "PTT-Based Method for Noninvasive Beat-to-Beat Estimation of Systolic and Diastolic Blood Pressure", 2016.##[5] YounessiHeravi Mohamad Amin, Khalilzade Mohamad Ali, "Designing and Constructing an Optical System to measure Continuous and Cuffless Blood Pressure Using Two Pulse Signals", Iranian Journal of Medical Physics, Vol. 10, no. 4 (2014): 215-223.##[6] McCombie, Devin B., Andrew T. Reisner, and H. Harry Asada. "Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics." In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 3521-3524. IEEE, 2006.##[7] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.##[8] Khalilzade M A, DustdarNughabi H, "Evaluation of blood perfusion of the trapezius muscle with wavelet analysis of photoplethysmography signal using neural network", JSDP,2016, vol. 13 (2):25-33.##[9] Shahabi Mina, Msc Thesis, Biomedical Group, E;IT Department, Iranian Research Organization for Science and Technology (IROST).##[10] Wang, K, Q., L. S. Xu, L. Wang, Z. G. Li, and Y. Z. Li. "Pulse baseline wander removal using wavelet approximation." In Computers in Cardiology, 2003, pp. 605-608. IEEE, 2003.##[11] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.##[12] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.##[1] Shelley, Kirk H. "Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate." Anesthesia & Analgesia 105, no. 6 (2007): 31-36.##[2] Alnaeb, Mohamad E., Nasser Alobaid, Alexander M. Seifalian, Dimitri P. Mikhailidis, and George Hamilton. "Optical techniques in the assessment of peripheral arterial disease." Current vascular pharmacology 5, no. 1 (2007): 53-59.##[3] Tamura, Toshiyo, Yuka Maeda, Masaki Sekine, and Masaki Yoshida. "Wearable photoplethysmographic sensors—past and present." Electronics3, no. 2 (2014): 282-302.##[4] Kamalzade Shiva, Thesis "PTT-Based Method for Noninvasive Beat-to-Beat Estimation of Systolic and Diastolic Blood Pressure", 2016.##[5] YounessiHeravi Mohamad Amin, Khalilzade Mohamad Ali, "Designing and Constructing an Optical System to measure Continuous and Cuffless Blood Pressure Using Two Pulse Signals", Iranian Journal of Medical Physics, Vol. 10, no. 4 (2014): 215-223.##[6] McCombie, Devin B., Andrew T. Reisner, and H. Harry Asada. "Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics." In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 3521-3524. IEEE, 2006.##[7] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.##[8] خلیل زاده محمد علی، دوستدار نوقابی حجت، ارزیابی خونرسانی به بافت در ناحیه عضلات دوزنقه با تحلیل موجک سیگنال حجم سنجی نوری به کمک شبکه عصبی، پردازش علائم و داده ها، 1395، جلد 13 (2) : 25-33.##[8] Khalilzade M A, DustdarNughabi H, "Evaluation of blood perfusion of the trapezius muscle with wavelet analysis of photoplethysmography signal using neural network", JSDP,2016, vol. 13 (2):25-33.##[9] شهابی مینا، پایان نامه کارشناسی ارشد، گروه مهندسی پزشکی، پژوهشکده برق و مخابرات، سازمان پژوهش های علمی و صنعتی ایران.##[9] Shahabi Mina, Msc Thesis, Biomedical Group, E;IT Department, Iranian Research Organization for Science and Technology (IROST).##[10] Wang, K, Q., L. S. Xu, L. Wang, Z. G. Li, and Y. Z. Li. "Pulse baseline wander removal using wavelet approximation." In Computers in Cardiology, 2003, pp. 605-608. IEEE, 2003.##[11] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.##[12] Shahabi Mina, Nafisi Vahid Reza, Pak Fateme, "Prediction of Intradialytic Hypotention Using PPG Signal Features",22nd Iranian Conference on Biomedical Engineering, Tehran, November 25-27, 2015.## ##

مدل ترجمه عبارت-مرزی با استفاده از برچسب‌های کم‌عمق نحوی Phrase-Boundary Translation Model Using Shallow Syntactic Labels 1 1 مدل عبارت-مرزی برای ترجمه ماشینی آماری، قواعد را با طبقه کلمات مرزی عبارات پیکره مقصد برچسب می­زند. در این مقاله مدل عبارت-مرزی را با استفاده از برچسب­های کم‌عمق نحوی شامل برچسب POS و برچسب قطعات توسعه می­دهیم. با اولویت برچسب قطعات، مدل پیشنهادی، غیرپایانه­ها را با برچسب­های کم‌عمق نحوی در مرز عبارات مقصد نام­‌گذاری می­‌کند. در قیاس با مدل  SAMT که قواعد را با درخت تجزیه نحوی جملات مقصد برچسب می­‌زند، مدل پیشنهادی به تجزیه عمیق نحوی نیاز ندارد. همچنین، هرچه تفاوت ترتیب کلمات زبان مبداء و مقصد ترجمه بیشتر باشد، عبارات تراز‌شده قابل انطباق با درخت تجزیه نحوی، کمتر خواهد بود. تعدادی آزمایش در ترجمه از فارسی و آلمانی به انگلیسی به‌عنوان جفت‌زبان­‌هایی با تفاوت زیاد در ترتیب کلمات انجام شد. در این آزمایش‌ها، مدل عبارت-مرزی پیشنهادی نسبت به مدل SAMT در حدود 5/0 واحد BLEU کیفیت ترجمه بهتری به‌دست آورد. 2 Phrase-boundary model for statistical machine translation labels the rules with classes of boundary words on the target side phrases of training corpus. In this paper, we extend the phrase-boundary model using shallow syntactic labels including POS tags and chunk labels. With the priority of chunk labels, the proposed model names non-terminals with shallow syntactic labels on the boundaries of the target side phrases. In comparison to the base phrase-boundary model, our variant uses phrase labels in addition to word classes. In other words, if there is no chunk label in one boundary, the labeler uses the word POS tag. The boundary labels are concatenated where there is no label for the whole target span. Using chunks as phrase labels, the proposed model generalizes the rules to decrease the model sparseness. The sparseness has more importance in the language pairs with a lot of differences in the word order because they have less number of aligned phrase pairs for extraction of rules. Compared with Syntax Augmented Machine Translation (SAMT) that labels rules with the syntax trees of the target side sentences, the proposed model does not need deep syntactic parsing. Thus, it is applicable even for low-resource languages having no syntactic parser. Some translation experiments are performed from Persian and German to English as the source and target languages with different word orders. In the experiments, our model achieved improvements of about 0.5 point of BLEU over a variant of SAMT. 115 126 2018/03/22016/06/22016/10/112017/01/202017/02/122017/03/102016/09/202016/08/31 1395/6/10 2017/10/252017/03/52017/06/102017/10/252016/10/242018/03/62017/03/52017/03/5 1395/12/15 شهرام سلامی Shahram Salami دانشگاه شهید بهشتی ایران shsalami@gmail.com مهرنوش شمس فرد Mehrnoush Shamsfard دانشگاه شهید بهشتی ایران m-shams@sbu.ac.ir Statistical machine translation Hierarchical models Word tag Chunk label ترجمه ماشینی آماری مدل سلسله‌مراتبی برچسب کلمه برچسب قطعه [1] Z. Rahimi, M. H. Samani, and S. Khadivi, "Extracting parallel corpus from comparable documents to improve the quality of translation in machine translation systems," Signal data Proc-ess., vol. 12, no. 2, pp. 55–72, 2015.##[2] D. Chiang, "A hierarchical phrase-based model for statistical machine translation," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, pp. 263–270.##[3] A. Zollmann and A. Venugopal, "Syntax augmented machine translation via chart parsing," in Proceedings of the Workshop on Statistical Machine Translation, 2006, pp. 138–141.##[4] S. Salami, M. Shamsfard, and S. Khadivi, "Phrase-boundary model for statistical machine transla-tion," Comput. Speech Lang., vol. 38, pp. 13–27, 2016.##[5] T. Watanabe, H. Tsukada, and H. Isozaki, "Left-to-right target generation for hierarchical phrase-based translation," in Proceedings of the 21st International Conference on Computational Lin-guistics and the 44th annual meeting of the Association for Computational Linguistics, 2006, pp. 777–784.##[6] M. Huck, S. Peitz, M. Freitag, and H. Ney, "Discriminative reordering extensions for hierarchical phrase-based machine translation," in Proc. of the 16th Annual Conf. of the European Assoc. for Machine Translation, 2012, pp. 313–320.##[7] M. de B. Wenniger and K. Sima'an, "Labeling hierarchical phrase-based models without ling-uistic resources," Mach. Transl., vol. 29, no. 3–4, pp. 225–265, 2015.##[8] Z. He, Q. Liu, and S. Lin, "Improving statistical machine translation using lexicalized rule selec-tion," in Proceedings of the 22nd In-ternational Conference on Computational Lin-guistics-Volume 1, 2008, pp. 321–328.##[9] R. Haque, S. Kumar Naskar, A. Van Den Bosch, and A. Way, "Supertags as source language context in hierarchical phrase-based SMT," in Association for Machine Translation in the Americas (AMTA 2010), 2010.##[10] B. Zhou, X. Zhu, B. Xiang, and Y. Gao, "Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels," in Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation, 2008, pp. 19–27.##[11] H. Almaghout, J. Jiang, and A. Way, "CCG augmented hierarchical phrase based machine-translation," 2010.##[12] H. Mino, T. Watanabe, and E. Sumita, "Syntax-Augmented Machine Translation using Syntax-Label Clustering.," in EMNLP, 2014, pp. 165–171.##[13] J. Li, Z. Tu, G. Zhou, and J. van Genabith, "Using syntactic head information in hierarchical phrase-based translation," in Proceedings of the Seventh Workshop on Statistical Machine Translation, 2012, pp. 232–242.##[14] C. Cherry, "Improved Reordering for Phrase-Based Translation using Sparse Features.," in HLT-NAACL, 2013, pp. 22–31.##[15] A. Zollmann and S. Vogel, "A word-class approach to labeling pscfg rules for machine translation," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 2011, pp. 1–11.##[16] A. Zollmann, A. Venugopal, F. Och, and J. Ponte, "A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT," in Proceedings of the 22nd International Conference on Computational Linguistics-Vo-lume 1, 2008, pp. 1145–1152.##[17] Z. He, Y. Meng, and H. Yu, "Discarding monotone composed rule for hierarchical phrase-based statistical machine translation," in Proceedings of the 3rd International Universal Communication Symposium, 2009, pp. 25–29.##[18] G. Iglesias, A. de Gispert, E. R. Banga, and W. Byrne, "Rule filtering by pattern for efficient hierarchical translation," in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009, pp. 380–388.##[19] S.-W. Lee, D. Zhang, M. Li, M. Zhou, and H.-C. Rim, "Translation model size reduction for hierarchical phrase-based statistical machine translation," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, 2012, pp. 291–295.##[20] B. Sankaran, G. Haffari, and A. Sarkar, "Bayesian extraction of minimal scfg rules for hierarchical phrase-based translation," in Procee-dings of the Sixth Workshop on Statistical Mach-ine Translation, 2011, pp. 533–541.##[21] B. Sankaran, G. Haffari, and A. Sarkar, "Compact rule extraction for hierarchical phrase-based translation," in The 10th biennial conference of the Association for Machine Translation in the Americas (AMTA), San Diego, CA. Association for Computational Linguistics, 2012.##[22] S. C. of ICT, "Mizan English-Persian Parallel C-orpus," 2013.##[23] P. Koehn, "Europarl: A parallel corpus for statistical machine translation," in MT summit, 2005, vol. 5, pp. 79–86.##[24] D. Chiang, "Hierarchical phrase-based transla-tion," Comput. Linguist., vol. 33, no. 2, pp. 201–228, 2007.##[25] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, "BLEU: a method for automatic evaluation of machine translation," in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp. 311–318.##[26] P. Koehn, Statistical machine translation. Cam-bridge University Press, 2009.##[27] Z. Li, C. Callison-Burch, C. Dyer, J. Ganitkevitch, S. Khudanpur, L. Schwartz, W. N. G. Thornton, J. Weese, and O. F. Zaidan, "Joshua: An open source toolkit for parsing-based machine translation," in Proceedings of the Fourth Workshop on Statistical Machine Translation, 2009, pp. 135–139.##[28] F. J. Och and H. Ney, "Improved statistical alignment models," in Proceedings of the 38th Annual Meeting on Association for Computa-tional Linguistics, 2000, pp. 440–447.##[29] A. Pauls and D. Klein, "Faster and smaller n-gram language models," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 2011, pp. 258–267.##[30] F. J. Och, "Minimum error rate training in statistical machine translation," in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, 2003, pp. 160–167.##[31] M. Post, J. Ganitkevitch, L. Orland, J. Weese, Y. Cao, and C. Callison-Burch, "Joshua 5.0: Sparser, better, faster, server," in Proceedings of the Eighth Workshop on Statistical Machine Tra-nslation, 2013, pp. 206–212.##[32] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," J. Mach. Learn. Res., vol. 12, pp. 2493–2537, 2011.##[33] D. Klein and C. D. Manning, "Accurate unlexicalized parsing," in Proceedings of the 41st Annual Meeting on Association for Computa-tional Linguistics-Volume 1, 2003, pp. 423–430.##[34] P. Koehn, F. J. Och, and D. Marcu, "Statistical phrase-based translation," in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, 2003, pp. 48–54.##https://doi.org/10.3115/1073445.1073462##[35] P. Koehn, "Statistical Significance Tests for Machine Translation Evaluation.," in EMNLP, 2004, pp. 388–395.##[36] P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, and others, "Moses: Open source toolkit for statistical machine translation," in Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, 2007, pp. 177–180.##[1] رحیمی زینب، ثمنی محمد حسین، خدیوی شهرام. استخراج پیکره موازی از اسناد قابل مقایسه برای بهبود کیفیت ترجمه در سامانه‌های ترجمه ماشینی. پردازش علائم و داده‌ها،12(2)، 55-72، 1394##[1] Z. Rahimi, M. H. Samani, and S. Khadivi, "Extracting parallel corpus from comparable documents to improve the quality of translation in machine translation systems," Signal data Proc-ess., vol. 12, no. 2, pp. 55–72, 2015.##[2] D. Chiang, "A hierarchical phrase-based model for statistical machine translation," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, pp. 263–270.##[3] A. Zollmann and A. Venugopal, "Syntax augmented machine translation via chart parsing," in Proceedings of the Workshop on Statistical Machine Translation, 2006, pp. 138–141.##[4] S. Salami, M. Shamsfard, and S. Khadivi, "Phrase-boundary model for statistical machine transla-tion," Comput. Speech Lang., vol. 38, pp. 13–27, 2016.##[5] T. Watanabe, H. Tsukada, and H. Isozaki, "Left-to-right target generation for hierarchical phrase-based translation," in Proceedings of the 21st International Conference on Computational Lin-guistics and the 44th annual meeting of the Association for Computational Linguistics, 2006, pp. 777–784.##[6] M. Huck, S. Peitz, M. Freitag, and H. Ney, "Discriminative reordering extensions for hierarchical phrase-based machine translation," in Proc. of the 16th Annual Conf. of the European Assoc. for Machine Translation, 2012, pp. 313–320.##[7] M. de B. Wenniger and K. Sima'an, "Labeling hierarchical phrase-based models without ling-uistic resources," Mach. Transl., vol. 29, no. 3–4, pp. 225–265, 2015.##[8] Z. He, Q. Liu, and S. Lin, "Improving statistical machine translation using lexicalized rule selec-tion," in Proceedings of the 22nd In-ternational Conference on Computational Lin-guistics-Volume 1, 2008, pp. 321–328.##[9] R. Haque, S. Kumar Naskar, A. Van Den Bosch, and A. Way, "Supertags as source language context in hierarchical phrase-based SMT," in Association for Machine Translation in the Americas (AMTA 2010), 2010.##[10] B. Zhou, X. Zhu, B. Xiang, and Y. Gao, "Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels," in Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation, 2008, pp. 19–27.##[11] H. Almaghout, J. Jiang, and A. Way, "CCG augmented hierarchical phrase based machine-translation," 2010.##[12] H. Mino, T. Watanabe, and E. Sumita, "Syntax-Augmented Machine Translation using Syntax-Label Clustering.," in EMNLP, 2014, pp. 165–171.##[13] J. Li, Z. Tu, G. Zhou, and J. van Genabith, "Using syntactic head information in hierarchical phrase-based translation," in Proceedings of the Seventh Workshop on Statistical Machine Translation, 2012, pp. 232–242.##[14] C. Cherry, "Improved Reordering for Phrase-Based Translation using Sparse Features.," in HLT-NAACL, 2013, pp. 22–31.##[15] A. Zollmann and S. Vogel, "A word-class approach to labeling pscfg rules for machine translation," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 2011, pp. 1–11.##[16] A. Zollmann, A. Venugopal, F. Och, and J. Ponte, "A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT," in Proceedings of the 22nd International Conference on Computational Linguistics-Vo-lume 1, 2008, pp. 1145–1152.##[17] Z. He, Y. Meng, and H. Yu, "Discarding monotone composed rule for hierarchical phrase-based statistical machine translation," in Proceedings of the 3rd International Universal Communication Symposium, 2009, pp. 25–29.##[18] G. Iglesias, A. de Gispert, E. R. Banga, and W. Byrne, "Rule filtering by pattern for efficient hierarchical translation," in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009, pp. 380–388.##[19] S.-W. Lee, D. Zhang, M. Li, M. Zhou, and H.-C. Rim, "Translation model size reduction for hierarchical phrase-based statistical machine translation," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, 2012, pp. 291–295.##[20] B. Sankaran, G. Haffari, and A. Sarkar, "Bayesian extraction of minimal scfg rules for hierarchical phrase-based translation," in Procee-dings of the Sixth Workshop on Statistical Mach-ine Translation, 2011, pp. 533–541.##[21] B. Sankaran, G. Haffari, and A. Sarkar, "Compact rule extraction for hierarchical phrase-based translation," in The 10th biennial conference of the Association for Machine Translation in the Americas (AMTA), San Diego, CA. Association for Computational Linguistics, 2012.##[22] S. C. of ICT, "Mizan English-Persian Parallel C-orpus," 2013.##[23] P. Koehn, "Europarl: A parallel corpus for statistical machine translation," in MT summit, 2005, vol. 5, pp. 79–86.##[24] D. Chiang, "Hierarchical phrase-based transla-tion," Comput. Linguist., vol. 33, no. 2, pp. 201–228, 2007.##[25] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, "BLEU: a method for automatic evaluation of machine translation," in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp. 311–318.##[26] P. Koehn, Statistical machine translation. Cam-bridge University Press, 2009.##[27] Z. Li, C. Callison-Burch, C. Dyer, J. Ganitkevitch, S. Khudanpur, L. Schwartz, W. N. G. Thornton, J. Weese, and O. F. Zaidan, "Joshua: An open source toolkit for parsing-based machine translation," in Proceedings of the Fourth Workshop on Statistical Machine Translation, 2009, pp. 135–139.##[28] F. J. Och and H. Ney, "Improved statistical alignment models," in Proceedings of the 38th Annual Meeting on Association for Computa-tional Linguistics, 2000, pp. 440–447.##[29] A. Pauls and D. Klein, "Faster and smaller n-gram language models," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 2011, pp. 258–267.##[30] F. J. Och, "Minimum error rate training in statistical machine translation," in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, 2003, pp. 160–167.##[31] M. Post, J. Ganitkevitch, L. Orland, J. Weese, Y. Cao, and C. Callison-Burch, "Joshua 5.0: Sparser, better, faster, server," in Proceedings of the Eighth Workshop on Statistical Machine Tra-nslation, 2013, pp. 206–212.##[32] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," J. Mach. Learn. Res., vol. 12, pp. 2493–2537, 2011.##[33] D. Klein and C. D. Manning, "Accurate unlexicalized parsing," in Proceedings of the 41st Annual Meeting on Association for Computa-tional Linguistics-Volume 1, 2003, pp. 423–430.##[34] P. Koehn, F. J. Och, and D. Marcu, "Statistical phrase-based translation," in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, 2003, pp. 48–54.##https://doi.org/10.3115/1073445.1073462##[35] P. Koehn, "Statistical Significance Tests for Machine Translation Evaluation.," in EMNLP, 2004, pp. 388–395.##[36] P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, and others, "Moses: Open source toolkit for statistical machine translation," in Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, 2007, pp. 177–180.## ##

ضرب‌کننده و ضرب‌جمع‌کننده پیمانه 2n+1 برای پردازنده سیگنال دیجیتال Modulo 2n+1 Multiply and MAC Units Specified for DSPs 1 1 یکی از مهم­ترین عملیات پردازنده­های سیگنال دیجیتال  فیلتر‌کردن است که معادل عملیات جمع و ضرب متوالی است. ادغام دو واحد ضرب­کننده و جمع­کننده موجود در ساختار این پردازنده­­ها منجر به ایجاد یک واحد جدید به نام ضرب‌جمع­کننده می­شود. جهت بهبود کارایی واحد ضرب‌جمع­کننده، از سامانه‌های اعداد مانده­ای می­توان بهره گرفت. این سامانه به‌دلیل انجام عملیات به‌صورت موازی روی پیمانه­ها و محدود‌کردن انتشار رقم نقلی به داخل هر پیمانه، سرعت و توان مصرفی مدارهای محاسباتی مانند ضرب­کننده و ضرب‌جمع­کننده را بهبود می­بخشند. از میان مجموعه پیمانه {2n+1,2n,2n-1}، مدارهای پیمانه 2n+1 به‌دلیل نیاز به مسیر داده (n+1) بیتی، مسیر بحرانی خواهند بود. در این مقاله، ابتدا یک واحد ضرب‌جمع­کننده برای پیمانه 2n+1 ارائه شده و سپس، برای بهبود بیشتر کارایی از روش خط لوله و چند‌ولتاژی استفاده می­شود. نتایج شبیه­سازی بیان‌گر بهبود تأخیر، توان مصرفی و PDP مدارهای پیشنهادی بدون کاهش کارایی نسبت به مدارهای موجود است.   2 Nowadays, digital signal processors (DSPs) are appropriate choices for real-time image and video processing in embedded multimedia applications not only due to their superior signal processing performance, but also of the high levels of integration and very low-power consumption. Filtering which consists of multiple addition and multiplication operations, is one of the most fundamental operations of DSPs. Therefore, there is a need for an additional unit just after the multiplication unit in DSPs. By combining multiply and add units, new structure named MAC (Multiply and ACcumulate) unit is provided. Residue Number System (RNS) can improve speed and power consumption of arithmetic circuits as it offers parallel arithmetic operations on each moduli and confines carry propagation to each moduli. In order to improve the efficiency of the MAC unit, RNS could be utilized. RNS divides large numbers to smaller numbers, called residues, according to a moduli set and enables performing arithmetic operations on each moduli independently. The moduli set {2n-1,2n,2n+1} is the most famous among others because of its simple and efficient implementation. Among this moduli set, modulo 2n+1 circuits are the critical path due to (n+1)-bit wide data path despite other two modules which all have n-bit wide operands. In order to overcome the problem of (n+1) bits operands, three representations has been suggested: diminished-1, Signed-LSB and Stored-Unibit. Although different multipliers have been proposed for diminished-1 representation, no multiplication structure has been proposed for the last two ones. Modulo 2n+1 multipliers are divided into 3 categories depending on their inputs and outputs types: both operands use standard (weighted) representation; one input uses standard representation, while the other one utilizes diminished-1 representation; both inputs use diminished-1 representation. Although several multiply and add units have been proposed for the first 2 categories, no MAC unit is proposed for the multipliers of a third category which outperform multipliers of other categories. In this article at first, one modulo 2n+1 MAC unit for the third category is proposed and then for further improvement, pipeline and multi-voltage techniques are utilized. Pipeline structure enables a trade-off between power consumption and delay. Whenever high-performance with least delay is desirable, nominal supply voltage can be chosen (high performance mode) otherwise by reducing supply voltage to the amount at which pipeline circuit and normal circuit without pipeline would have the same performance, power consumption decreases significantly (low power mode). Simulations are performed in two phases. At first phase, proposed MAC unit without pipeline structure is described via VHDL code and synthesized with synopsys design vision tool. Results indicate that the proposed structure outperforms PDP (Power-Delay-Product) up to 39% compared to the state of the art MAC units. At second phase, CMOS transistor level implementation in two modes i.e. low power and high performance modes with Cadence Design Systems tool is provided. Simulation results indicate that at low power condition, proposed pipeline MAC unit yields to 71% power savings compared to existing circuits without declining efficiency. Furthermore, at high performance condition, however power consumption has increased, reducing delay up to 54% yields to 39% PDP savings for proposed pipeline MAC unit.   127 138 2018/03/22016/06/22016/10/112017/01/202017/02/122017/03/102016/09/202016/08/312017/03/1 1395/12/11 2017/10/252017/03/52017/06/102017/10/252016/10/242018/03/62017/03/52017/03/52017/10/25 1396/8/3 نگار اکبرزاده Negar Akbarzadeh دانشگاه صنعتی شریف ایران ne.akbarzadeh@mail.sbu.ac.ir سمیه تیمارچی Somayeh Timarchi دانشگاه شهید بهشتی ایران s_timarchi@sbu.ac.ir Digital signal processor MAC Residue number system diminished-1 representation multiplier پردازنده سیگنال دیجیتال ضرب‌جمع‌کننده سامانه اعداد مانده‌ای نمایش diminished-1 ضرب‌کننده [1] Timarchi, S. Design and Implementation of Efficient Redundant Residue Number Systems, Ph.D dissertation. Shahid Beheshti University, chapter 1-2, 2010.##[2] Rajaeian, A., Grailu, H. Implementation of a Driver Drowsiness Detection System Based on TMAS320C5505A DSP Processor. JSDP; 14 (1) :83-98, 2017.##[3] Timarchi, S., Ghayour, P. and Shahbahrami, A. A novel high-speed low-power binary signed-digit adder. In Computer Architecture and Digital Systems (CADS), 2012 16th CSI International Symposium on (pp. 70-74). IEEE, May, 2012.##[4] Timarchi, S., Fazlali, M. and Cotofana, S.D. A unified addition structure for moduli set {2 n− 1, 2 n, 2 n+ 1} based on a novel RNS representation. In Computer Design (ICCD), 2010 IEEE International Conference on (pp. 247-252). IEEE, October, 2010.##[5] Ramirez, J., Garcia, A., Lopez-Buedo, S. and Lloris, A. RNS-enabled digital signal processor design. Electronics Letters, 38(6), pp.266-268, 2002.##[6] Bernocchi, G.L., Cardarilli, G.C., Del Re, A., Nannarelli, A. and Re, M. Low-power adaptive filter based on RNS components. In Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on (pp. 3211-3214). IEEE, May, 2007.##[7] Marino, F., Stella, E., Branca, A., Veneziani, N. and Distante, A. Specialized hardware for real-time navigation. Real-Time Imaging, 7(1), pp.97-108, 2001.##[8] Meyer-Bäse, U., García, A. and Taylor, F. Implementation of a communications channeliz-er using FPGAs and RNS arithme-tic. Journal of VLSI signal processing systems for signal, image and video techno-logy, 28(1-2), pp.115-128, 2001.##[9] Bajard, J.C. and Imbert, L. A full RNS implementation of RSA. IEEE Transactions on Computers, 53(6), pp.769-774, 2004.##[10] Leibowitz, L. A simplified binary arithmetic for the Fermat number transform. IEEE Transac-tions on acoustics, speech, and signal process-ing, 24(5), pp.356-359, 1976.##[11] Zimmermann, R. Efficient VLSI implementation of modulo (2/sup n//spl plusmn/1) addition and multiplication. In Computer Arithmetic, 1999. Proceedings. 14th IEEE Symposium on (pp. 158-167). IEEE, 1999.##[12] Efstathiou, C., Pekmestzi, K. and Axelos, N. August. On the Design of Modulo 2^ n+ 1 Multipliers. In Digital System Design (DSD), 2011 14th Euromicro Conference on (pp. 453-459). IEEE, 2011.##[13] Efstathiou, C., Moshopoulos, N., Axelos, N. and Pekmestzi, K. Efficient modulo 2n+ 1 multiply and multiply-add units based on modified Booth encoding. Integration, the VLSI Journal, 47(1), pp.140-147, 2014.##[14] Efstathiou, C., Vergos, H.T., Dimitrakopoulos, G. and Nikolos, D. Efficient diminished-1 modulo 2/sup n/+ 1 multipliers. IEEE Transac-tions on Computers, 54(4), pp.491-496, 2005.##[15] Lv, X. and Yao, R. Efficient diminished-1 modulo 2 n+ 1 multiplier architectures. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 481-486). IEEE, July, 2014.##[16] Chen J.W., Yao R.H., Wu W.J. Efficient Modulo 2n+1 multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, pp. 2149–2157, 2011.##[17] Efstathiou, C. and Voyiatzis, I. On the diminished-1 modulo 2 N+ 1 fused multiply-add units. In Design & Technology of Integrated Systems in Nanoscale Era (DTIS), 2011 6th International Conference on (pp. 1-5). IEEE, April, 2011.##[18] Illgner, K. DSPs for image and video process-ing. Signal Processing, 80(11), pp.2323-2336, 2000.##[19] Timarchi, S., Kavehei, O. and Navi, K. Low Power Modulo 2 n+ 1 Adder Based on Carry Save Diminished-One Number System. Amer-ican Journal of Applied Sciences, 5(4), pp.312-319, 2008.##[20] Piguet, C. Low-power CMOS circuits: techn-ology, logic design and CAD tools. CRC Press, 2005.##[21] Zimmermann, R. and Fichtner, W. Low-power logic styles: CMOS versus pass-transistor lo-gic. IEEE journal of solid-state circuits, 32(7), pp.1079-1090, 1997.##[22] Strollo, A.G.M. and De Caro, D. Low power flip-flop with clock gating on master and slave latches. Electronics Letters, 36(4), pp.294-295, 2000.##[1] تیمارچی سمیه. طراحی و پیاده‌سازی سیستم‌های اعداد مانده‌ای افزونه کارا. رساله‌ی دکتری، دانشگاه شهید بهشتی، فصل‌های 1 و 2، 1388.##[2] رجائیان علی، گرایلو هادی. طراحی و ساخت یک سیستم تشخیص خواب آلودگی راننده مبتنی بر پردازش‌گر سیگنال TMS320C5509A. پردازش علائم و داده‌ها، 14 (1) :98-83، 1396.##[1] Timarchi, S. Design and Implementation of Efficient Redundant Residue Number Systems, Ph.D dissertation. Shahid Beheshti University, chapter 1-2, 2010.##[2] Rajaeian, A., Grailu, H. Implementation of a Driver Drowsiness Detection System Based on TMAS320C5505A DSP Processor. JSDP; 14 (1) :83-98, 2017.##[3] Timarchi, S., Ghayour, P. and Shahbahrami, A. A novel high-speed low-power binary signed-digit adder. In Computer Architecture and Digital Systems (CADS), 2012 16th CSI International Symposium on (pp. 70-74). IEEE, May, 2012.##[4] Timarchi, S., Fazlali, M. and Cotofana, S.D. A unified addition structure for moduli set {2 n− 1, 2 n, 2 n+ 1} based on a novel RNS representation. In Computer Design (ICCD), 2010 IEEE International Conference on (pp. 247-252). IEEE, October, 2010.##[5] Ramirez, J., Garcia, A., Lopez-Buedo, S. and Lloris, A. RNS-enabled digital signal processor design. Electronics Letters, 38(6), pp.266-268, 2002.##[6] Bernocchi, G.L., Cardarilli, G.C., Del Re, A., Nannarelli, A. and Re, M. Low-power adaptive filter based on RNS components. In Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on (pp. 3211-3214). IEEE, May, 2007.##[7] Marino, F., Stella, E., Branca, A., Veneziani, N. and Distante, A. Specialized hardware for real-time navigation. Real-Time Imaging, 7(1), pp.97-108, 2001.##[8] Meyer-Bäse, U., García, A. and Taylor, F. Implementation of a communications channeliz-er using FPGAs and RNS arithme-tic. Journal of VLSI signal processing systems for signal, image and video techno-logy, 28(1-2), pp.115-128, 2001.##[9] Bajard, J.C. and Imbert, L. A full RNS implementation of RSA. IEEE Transactions on Computers, 53(6), pp.769-774, 2004.##[10] Leibowitz, L. A simplified binary arithmetic for the Fermat number transform. IEEE Transac-tions on acoustics, speech, and signal process-ing, 24(5), pp.356-359, 1976.##[11] Zimmermann, R. Efficient VLSI implementation of modulo (2/sup n//spl plusmn/1) addition and multiplication. In Computer Arithmetic, 1999. Proceedings. 14th IEEE Symposium on (pp. 158-167). IEEE, 1999.##[12] Efstathiou, C., Pekmestzi, K. and Axelos, N. August. On the Design of Modulo 2^ n+ 1 Multipliers. In Digital System Design (DSD), 2011 14th Euromicro Conference on (pp. 453-459). IEEE, 2011.##[13] Efstathiou, C., Moshopoulos, N., Axelos, N. and Pekmestzi, K. Efficient modulo 2n+ 1 multiply and multiply-add units based on modified Booth encoding. Integration, the VLSI Journal, 47(1), pp.140-147, 2014.##[14] Efstathiou, C., Vergos, H.T., Dimitrakopoulos, G. and Nikolos, D. Efficient diminished-1 modulo 2/sup n/+ 1 multipliers. IEEE Transac-tions on Computers, 54(4), pp.491-496, 2005.##[15] Lv, X. and Yao, R. Efficient diminished-1 modulo 2 n+ 1 multiplier architectures. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 481-486). IEEE, July, 2014.##[16] Chen J.W., Yao R.H., Wu W.J. Efficient Modulo 2n+1 multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, pp. 2149–2157, 2011.##[17] Efstathiou, C. and Voyiatzis, I. On the diminished-1 modulo 2 N+ 1 fused multiply-add units. In Design & Technology of Integrated Systems in Nanoscale Era (DTIS), 2011 6th International Conference on (pp. 1-5). IEEE, April, 2011.##[18] Illgner, K. DSPs for image and video process-ing. Signal Processing, 80(11), pp.2323-2336, 2000.##[19] Timarchi, S., Kavehei, O. and Navi, K. Low Power Modulo 2 n+ 1 Adder Based on Carry Save Diminished-One Number System. Amer-ican Journal of Applied Sciences, 5(4), pp.312-319, 2008.##[20] Piguet, C. Low-power CMOS circuits: techn-ology, logic design and CAD tools. CRC Press, 2005.##[21] Zimmermann, R. and Fichtner, W. Low-power logic styles: CMOS versus pass-transistor lo-gic. IEEE journal of solid-state circuits, 32(7), pp.1079-1090, 1997.##[22] Strollo, A.G.M. and De Caro, D. Low power flip-flop with clock gating on master and slave latches. Electronics Letters, 36(4), pp.294-295, 2000.## ##

انتخاب مناسب‌ترین زبان پرس‌وجو برای استفاده از فرا‌‌پیوندها جهت استخراج داده‌ها در حالت دیتالوگ در سامانه پایگاه داده استنتاجی DES Choosing the most appropriate query language to use Outer Joins for data extraction in Datalog mode in the Deductive Database System DES 1 1 سامانه‌های پایگاه داده استنتاجی بر اساس یک مدل داده منطقی طراحی می‌شوند. داده‌ها در یک سامانه پایگاه داده استنتاجی (برخلاف سامانه پایگاه داده‌های رابطه‌ای که داده‌ها به‌صورت جداول ذخیره می‌شوند) به‌صورت حقایق ذخیره می‌شوند. سامانه آموزشی دیتالوگ (DES) یک سامانه پایگاه داده استنتاجی است که حالت دیتالوگ، حالت پیش‌فرض آن است. در حالت دیتالوگ برای استفاده از فرا‌پیوندها با سه زبان پرس‌وجو (دیتالوگ، SQL و RA) داده‌ها را می‌توان استخراج کرد. در پژوهش‌های قبلی انتخاب مناسب‌ترین زبان پرس‌وجو برای استفاده از فرا‌‌پیوندها جهت استخراج داده‌ها در حالت دیتالوگ در سامانه DES بررسی نشده است. در این پژوهش با در‌نظر‌گرفتن دو مشخصه (هزینه نوشتن پرس‌وجو و حافظه مصرفی پرس‌وجو) انتخاب مناسب‌ترین زبان پرس‌وجو برای استفاده از فرا‌پیوندها جهت استخراج داده‌ها در حالت دیتالوگ در DES بررسی می‌شود. نتایج پژوهش نشان می‌دهد که برای همه پرس‌وجوها استفاده از یک‌زبان می‌تواند مناسب نباشد و لذا برای پرس‌وجوهای مختلف مناسب‌ترین زبان پرس‌وجو برای استفاده از فرا‌پیوندها باید انتخاب ‌شود. در پژوهش جاری، مناسب‌ترین زبان پرس‌وجو زبانی است که کاربر نسبت به دو زبان دیگر جهت پیاده‌سازی پرس‌وجو به کلید کمتری از صفحه‌کلید نیاز داشته باشد. کاهش تعداد کلیدهایی که توسط کاربر فشار داده می‌شود، موجب کاهش زمان در پیاده‌سازی پرس‌وجو توسط کاربر و در نتیجه منجر به افزایش سرعت دسترسی کاربر به داده‌ها خواهد شد.   2 Deductive Database systems are designed based on a logical data model. Data (as opposed to Relational Databases Management System (RDBMS) in which data stored in tables) are saved as facts in a Deductive Database system. Datalog Educational System (DES) is a Deductive Database system that Datalog mode is the default mode in this system. It can extract data to use outer joins with three query languages (Datalog, SQL and RA) in default mode. In 2004, system DES was designed and implemented by Fernando S´aenz-P´erez from Department of Artificial Intelligence and Software Engineering, Complutense University, Madrid, Spain. In a paper, this researcher introduced outer joins of system DES  in 2012. The most important objective of present research is to complement and extend the paper authored by mentioned researcher. Therefore, in prior research, choosing the most appropriate query language has not been investigated to use outer joins for data extraction in Datalog mode in DES system. In this study, by considering two parameters (cost of writing a query and memory usage of a query) choosing the most appropriate query language has been investigated to use outer joins for data extraction in Datalog mode in Deductive system DES. Cost of writing a query parameter is considered in this study to decrease the query typing time, but other parameters are related to the query processing are not considered. If the processing time of the three query languages is assumed identical, after entering the query in the system DES, the idea of the present study (reduction of the typing time) can lead to the reduction of the response time. Also, there are two hypotheses in this study as follows: 1) it is assumed that the user is fluent in all three query languages and wants to access the given data quickly through the most appropriate query language. 2) In the present study, the simplicity or difficulty of a query language is not considered. The results of the research show that one language cannot be appropriate for all queries; therefore, for every different query the most appropriate query language must be chose to use outer joints. In the current research, the most appropriate query language is the one in which, in comparison with other two query languages, the user will need to use less buttons of the keyboard to press in order to fulfill the query. The decrease in the number of buttons pressed by the user will decrease the time consumed to fulfill the query and, therefore, it will lead to a faster access to data.   139 150 2018/03/22016/06/22016/10/112017/01/202017/02/122017/03/102016/09/202016/08/312017/03/12017/01/19 1395/10/30 2017/10/252017/03/52017/06/102017/10/252016/10/242018/03/62017/03/52017/03/52017/10/252017/07/25 1396/5/3 مهدی رنجبر حسنی محمودآبادی Mahdi Ranjbar Hassani Mahmood Abadi دانشگاه آزاد اسلامی واحد کرمان ایران mranjbar@iauk.ac.ir احمد فراهی Ahmad Faraahi دانشگاه پیام نور ایران afaraahi@pnu.ac.ir Deductive Database DES Datalog mode Outer Joins Data extraction پایگاه داده استنتاجی DES حالت دیتالوگ فرا‌‌پیوندها استخراج داده‌ها. [1] R. Ramakrishnan, and J. D. Ullman, "A Survey of Deductive Database Systems," The Journal of Logic Programming, vol. 23, pp. 125–149, 1995.##[2] F. S’aenz-P’erez, "DES: A Deductive Database System," Electronic Notes on Theoretical Computer Science, vol. 271, pp. 63–78, 2011.##[3] A. Faraahi, "A Query Optimistion for Deductive Database," Ph.D thesis, Department of Computing, University Bradford, U.K, 1996.##[4] N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, and F. Scarcello, "The DLV system for knowledge representation and reasoning," ACM Tran. on Computational Logic, vol. 7, pp. 499–562, 2006.##[5] K. Sagonas, T. Swift, and D. S. Warren, "XSB as an efficient Deductive Database engine," In SIGMOD'94: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pp. 442–453, 1994.##https://doi.org/10.1145/191839.191970##[6] M. S. Lam, J. Whaley, V. B. Livshits, M. C. Martin, D. Avots, M. Carbin, and C. Unkel, "Context-sensitive program analysis as database queries," In Chen Li, editor, Proceedings of the Twenty-fourth ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems (PODS), pp. 1–12, 2005.##[7] F. Arni, K. Ong, S. Tsur, H. Wang, and C. Zaniolo, "The Deductive Database System LDL++," Theory and Practice of Logic Programming, vol. 3, pp. 61–94, 2003.##[8] M. Jarke, M. A. Jeusfeld, and C. Quix, "ConceptBase V7.1 User Manual," Technical report, RWTH Aachen, 2008.##[9] G. Ramalingam, and E. Visser, editors, "Proceedings of the Workshop on Partial Evaluation and Semantics-based Program Manipulation," ACM, 2007.##[10] F. S’aenz-P’erez, "Outer Joins in a Deductive Database System," Electronic Notes on Theoretical Computer Science, vol. 282, pp. 73–88, 2012.##[11] F. S´aenz-P´erez, "Improving the Deductive System DES with Persistence by Using SQL DBMS's," S. Escobar (Ed.): XIV Jornadas sobre Programaci´on y Lenguajes, pp. 100–114, 2015.##[12] J. D. Ullman, "Database and Knowledge-Base Systems," Vols. I (Classical Database Systems) and II (The New Technologies), Computer Science Press, 1988.##[13] C. Zaniolo, S. Ceri, C. Faloutsos, R. T. Snodgrass, V. S. Subrahmanian, R. Zicari, "Ad-vanced Database Systems," Morgan Kau-fmann, 1997.##[14] ISO/IEC. ISO/IEC 132111-2: Prolog Standard, 2000.##[15] ISO/IEC. SQL:2008 ISO/IEC 9075(1-4,9-11,13,14):2008 Standard, 2008.##[16] E. Codd, "Relational Completeness of Data Base Sublanguages," In Rustin (ed.), Database Systems. Courant Computer Science Symposia Series 6. Englewood Cliffs, N.J.Prentice-Hall, pp. 1-38, 1972.##[17] F. S’aenz-P’erez, "Towards Bridging the Expressiveness Gap Between Relational and Deductive Databases," Prometidos-CM (S2009TIC-1465) and GPD (UCM-BSCH-GR35/10-A-910502), pp. 1-15, 2013.##[18] F. S´aenz-P´erez, "Datalog Educational System 4.1," Available: http://des.sourceforge.net/, [Accessed: April. 2016].##[19] F. S´aenz-P´erez, "Datalog Educational System V4.1 User's Manual," Available: http://des.sourceforge.net/, pp. 1-274, [Accessed: April. 2016].##[20] S. W. Dietrich, "Understanding Relational Database Query Languages," Prentice Hall, 2001.##[21] S. Ludwiy, "Comparison of a Deductive Database with a Semantic Web reasoning engine," Knowledge-Based Systems, vol. 23, pp. 634-642, 2010.##[1] R. Ramakrishnan, and J. D. Ullman, "A Survey of Deductive Database Systems," The Journal of Logic Programming, vol. 23, pp. 125–149, 1995.##[2] F. S’aenz-P’erez, "DES: A Deductive Database System," Electronic Notes on Theoretical Computer Science, vol. 271, pp. 63–78, 2011.##[3] A. Faraahi, "A Query Optimistion for Deductive Database," Ph.D thesis, Department of Computing, University Bradford, U.K, 1996.##[4] N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, and F. Scarcello, "The DLV system for knowledge representation and reasoning," ACM Tran. on Computational Logic, vol. 7, pp. 499–562, 2006.##[5] K. Sagonas, T. Swift, and D. S. Warren, "XSB as an efficient Deductive Database engine," In SIGMOD'94: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pp. 442–453, 1994.##https://doi.org/10.1145/191839.191970##[6] M. S. Lam, J. Whaley, V. B. Livshits, M. C. Martin, D. Avots, M. Carbin, and C. Unkel, "Context-sensitive program analysis as database queries," In Chen Li, editor, Proceedings of the Twenty-fourth ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems (PODS), pp. 1–12, 2005.##[7] F. Arni, K. Ong, S. Tsur, H. Wang, and C. Zaniolo, "The Deductive Database System LDL++," Theory and Practice of Logic Programming, vol. 3, pp. 61–94, 2003.##[8] M. Jarke, M. A. Jeusfeld, and C. Quix, "ConceptBase V7.1 User Manual," Technical report, RWTH Aachen, 2008.##[9] G. Ramalingam, and E. Visser, editors, "Proceedings of the Workshop on Partial Evaluation and Semantics-based Program Manipulation," ACM, 2007.##[10] F. S’aenz-P’erez, "Outer Joins in a Deductive Database System," Electronic Notes on Theoretical Computer Science, vol. 282, pp. 73–88, 2012.##[11] F. S´aenz-P´erez, "Improving the Deductive System DES with Persistence by Using SQL DBMS's," S. Escobar (Ed.): XIV Jornadas sobre Programaci´on y Lenguajes, pp. 100–114, 2015.##[12] J. D. Ullman, "Database and Knowledge-Base Systems," Vols. I (Classical Database Systems) and II (The New Technologies), Computer Science Press, 1988.##[13] C. Zaniolo, S. Ceri, C. Faloutsos, R. T. Snodgrass, V. S. Subrahmanian, R. Zicari, "Ad-vanced Database Systems," Morgan Kau-fmann, 1997.##[14] ISO/IEC. ISO/IEC 132111-2: Prolog Standard, 2000.##[15] ISO/IEC. SQL:2008 ISO/IEC 9075(1-4,9-11,13,14):2008 Standard, 2008.##[16] E. Codd, "Relational Completeness of Data Base Sublanguages," In Rustin (ed.), Database Systems. Courant Computer Science Symposia Series 6. Englewood Cliffs, N.J.Prentice-Hall, pp. 1-38, 1972.##[17] F. S’aenz-P’erez, "Towards Bridging the Expressiveness Gap Between Relational and Deductive Databases," Prometidos-CM (S2009TIC-1465) and GPD (UCM-BSCH-GR35/10-A-910502), pp. 1-15, 2013.##[18] F. S´aenz-P´erez, "Datalog Educational System 4.1," Available: http://des.sourceforge.net/, [Accessed: April. 2016].##[19] F. S´aenz-P´erez, "Datalog Educational System V4.1 User's Manual," Available: http://des.sourceforge.net/, pp. 1-274, [Accessed: April. 2016].##[20] S. W. Dietrich, "Understanding Relational Database Query Languages," Prentice Hall, 2001.##[21] S. Ludwiy, "Comparison of a Deductive Database with a Semantic Web reasoning engine," Knowledge-Based Systems, vol. 23, pp. 634-642, 2010.## ##