2020
18
1
95
0
https://jcse.ir/article/257
Multi-criteria reactive approach for joint dynamic VNF load balancing and service auto-scaling in NFV
0
The evolution of 5G key-enabler technologies, such as Software-defined Networking (SDN) and Network Function Virtualization (NFV), has brought network operators" attention to procure an efficient service delivery mechanism. Therefore, it is vital to leverage the service management and orchestration functionalities that operate in harmony. This can curb the undesirable negative impact of inconsistency on the quality of service. In this paper, we investigate the joint load balancing and auto-scaling of the elastic services that are being provisioned in the computing infrastructure at the edge or cloud. We address the necessity and challenges of designing the load balancing algorithm and scale decision making policy, aware of one another, through several practical scenarios on multimedia service delivery in NFV. It is demonstrated that changing the VNF load balancing method may deteriorate the scaling quality of decision-making. Consequently, we propose a novel multi-criteria reactive approach using a mutual score to dynamically adapt the VNF load balancing algorithm with the auto-scaling engine. We implement the proposed joint method on the management and orchestration layer of our previously developed NFV/SDN testbed, called XeniumNFV, to evaluate the effectiveness of our approach by conducting extensive experiments on the elastic web service.
1
12
Amir
Kusedghi
Iran University of Science and Technology
ایران
Ahmad
Akbari
Iran University of Science and Technology
ایران
VNF load balancing
Service auto-scaling
NFV
SDN
Edge computing
5G networks
https://jcse.ir/ow_userfiles/plugins/base/attachments/61e09cfa9beb2_61e09cfa8a4b5.pdf
[[1] A. Kusedghi, A. Ghorab, and A. Akbari, “XeniumNFV: A unified, dynamic, distributed and event-driven SDN/NFV testbed,” Proc. Of Int’l. Conf. on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, Dec., pp. 320–326, 2018.
##[2] S. H. K. S. L. Chen, Y. Chen, “CLB: A novel load balancing architecture and algorithm for cloud services,” Computers and Electrical Engineering, vol. 58, no. 1, pp. 154–160, Feb. 2017.
##[3] D. J. A. Singh and M. Malhotra, “Autonomous agent based load balancing algorithm in cloud computing,” Proc. Computer Science, vol. 45, Jan., pp. 832–841, 2015.
##[4] C. C. Li and K. Wang, “An SLA-aware load balancing scheme for cloud datacenters,” Proc. of Int’l. Conf. on Information Networking (ICOIN), Phuket, Thailand, Feb., pp. 58–63, 2014.
##[5] S. M. B. A. Akbar and A. Sattar, “A Comparative Study on Load Balancing Algorithms for SIP Servers,” New Delhi: Springer, Feb., pp. 79–88, 2016.
##[6] V. Nguyen, K. Grinnemo, J. Taheri, and A. Brunstrom, “On load balancing for a virtual and distributed MME in the 5G core,” Proc. of Int’l. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, Sept. 2018.
##[7] M. Thai, Y. Lin, and Y. Lai, “A joint network and server load balancing algorithm for chaining virtualized network functions,” Proc. of Int’l. Conf. on Communications (ICC), Kuala Lumpur, Malaysia, May 2016.
##[8] X. T. W. Chen, Z. Shang and H. Li, “Dynamic server cluster load balancing in virtualization environment with Openflow,” International Journal of Distributed Sensor Networks, vol. 11, no. 7, pp. 531–538, July 2015.
##[9] W. Zhang, T. Wood, and J. Hwang, “Netkv: Scalable, self-managing, load balancing as a network function,” Proc. of Int’l. Conf. on Autonomic Computing (ICAC), Wurzburg, Germany, July 2016.
##[10] S. J. Y. Go, R. Guinto, C. A. M. Festin, I. Austria, R. Ocampo, and W. M. Tan, “An SDN/NFV-enabled architecture for detecting personally identifiable information leaks on network traffic,” Proc. of Int’l. Conf. on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia, July 2019.
##[11] M. Abdullah, W. Igbal, and F. Bukhari, “Containers vs virtual machines for auto-scaling multi-tier applications under dynamically increasing workloads,” Proc. of Int’l. Conf. on Intelligent Technologies and Applications (INTAP), Bahawalpur, Pakistan, Oct. 2019.
##[12] A. Kwan, J. Wong, H. Jacobsen, and V. Muthusamy, “Hyscale: Hybrid and network scaling of dockerized microservices in cloud data centres,” Proc. of Int’l. Conf. on Distributed Computing Systems (ICDCS), Dallas, TX, July 2019.
##[13] F. Rossi, M. Nardelli, and V. Cardellini, “Horizontal and vertical scaling of container-based applications using reinforcement learning,” Proc. of Int’l. Conf. on Cloud Computing (CLOUD), Milan, Italy, July 2019.
##[14] C. Qu, R. N. Calheiros, and R. Buyya, “Auto-scaling web applications in clouds: A taxonomy and survey,” ACM Comput. Surv., vol. 51, no. 4, Sept. 2018.
##[15] R. Mijumbi, S. Hasija, S. Davy, A. Davy, B. Jennings, and R. Boutaba, “Topology-aware prediction of virtual network function resource requirements,” IEEE Trans. On Network and Service Management, vol. 14, no. 1, pp. 106–120, Mar. 2017.
##[16] V. Sciancalepore, F. Z. Yousaf, and X. Costa-Perez, “z-torch: An automated NFV orchestration and monitoring solution,” IEEE Trans. On Network and Service Management, vol. 15, no. 4, pp. 1292–1306, Dec. 2018.
##[17] J. Duan, C. Wu, F. Le, A. X. Liu, and Y. Peng, “Dynamic scaling of virtualized, distributed service chains: A case study of IMS,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2501– 2511, Nov. 2017.
##[18] A. M. Medhat, G. A. Carella, M. Pauls, and T. Magedanz, “Extensible framework for elastic orchestration of service function chains in 5g networks,” Proc. of Int’l. Conf. on Network Function Virtualization and Software Defined Networks (NFV-SDN), Berlin, Germany, pp. 327–333, Nov. 2017.
##[19] F. Lombardi, A. Muti, L. Aniello, R. Baldoni, S. Bonomi, and L. Querzoni, “Pascal: An architecture for proactive auto-scaling of distributed services,” Future Generation Computer Systems, vol. 98, no. 2, pp. 342 – 361, Sept. 2019.
##[20] W. Iqbal, A. Erradi, M. Abdullah, and A. Mahmood, “Predictive auto-scaling of multi-tier applications using performance varying cloud resources,” IEEE Transactions on Cloud Computing (Early Access, pp. 1–1, Sept. 2019.
##[21] I. Alawe, Y. Hadjadj-Aoul, A. Ksentinit, P. Bertin, C. Viho, and D. Darche, “An efficient and lightweight load forecasting for proactive scaling in 5G mobile networks,” Proc. of Int’l. Conf. on Standards for Communications and Networking (CSCN), Paris, France, Oct. 2018.
##[22] D. Cotroneo, R. Natella, and S. Rosiello, “NFV-throttle: An overload control framework for network function virtualization,” IEEE Trans. On Network and Service Management, vol. 14, no. 4, pp. 949–963, Dec. 2017.
##[23] R. N. D. Cotroneo and S. Rosiello, “Overload control for virtual network functions under CPU contention,” Future Generation Computer Systems, vol. 99, no. 1, pp. 164–176, Oct. 2019.
##[24] A. Bauer, N. Herbst, S. Spinner, A. Ali-Eldin, and S. Kounev, “Chameleon: A hybrid, proactive auto-scaling mechanism on a levelplaying field,” IEEE Trans. on Parallel and Distributed Systems, vol. 30, no. 4, pp. 800–813, Apr. 2019.
##[25] A. Bauer, V. Lesch, L. Versluis, A. Ilyushkin, N. Herbst, and S. Kounev, “Chamulteon: Coordinated auto-scaling of micro-services,” Proc. Of Int’l. Conf. on Distributed Computing Systems (ICDCS), Dallas, TX, Oct. 2019.
##[26] B. Benifa and D. Dharma, “HAS: Hybrid auto-scaler for resource scaling in cloud environment,” Journal of Parallel and Distributed Computing, vol. 120, no. 1, pp. 1–15, Oct. 2018.
##[27] L. Velasco, R. Casellas, and S. Llana, “A control and management architecture supporting autonomic NFV services,” Photonic Network Communications, vol. 37, no. 1, pp. 24–37, Feb. 2019.
##[28] R. Moreira, F. de Oliveira Silva, P. Frosi Rosa, and R. Aguiar, “A flexible network and compute-aware orchestrator to enhance QoS in NFV-based multimedia services,” Proc. of Int’l. Conf. on Advanced Information Networking and Applications (AINA), Krakow, Poland, pp. 512–519, May 2018.
##[29] R. Poddar, A. Vishnoi, and V. Mann, “Haven: Holistic load balancing and auto scaling in the cloud,” Proc. of Int’l. Conf. on Communication Systems and Networks (COMSNETS), Bangalore, India, Jan. 2015.
##[30] W. Abderrahim and Z. Choukair, “Dependability integration in cloud-hosted telecommunication services,” IEEE Trans. on Dependable and Secure Computing, vol. 16, no. 6, pp. 957–968, Nov. 2019.
##[31] A. J. Gonzalez, G. Nencioni, A. Kamisinski, B. E. Helvik, and P. E. ´ Heegaard, “Dependability of the NFV orchestrator: State of the art and research challenges,” IEEE Communications Surveys Tutorials, vol. 20, no. 4, pp. 3307 3329, Fourthquarter 2018.
##[32] A. Kusedghi, Z. Bagherabadi, and A. Akbari, “An IMS-aware VM placement in cloud environment,” Proc. of Int’l. Conf. on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, pp. 327–334, Dec. 2018.
##[33] “Sipp,” https://github.com/SIPp/sipp, Sept. 2020, accessed on 2020-09- 01.
##[34] Sangoma Technologies, “Asterisk,” https://www.asterisk.org/, Sept. 2020, accessed on 2020-09 01.
##[35] OpenSIPS project, “Opensips,” https://opensips.org/, July 2020, accessed on 2020-09-01.
##[36] Kamailio SIP project, “Kamailio sip load balancer,” https://www.kamailio.org/w/, Sept. 2020, accessed on 2020-09-01.
##[37] Docker, “Docker hub,” https://www.docker.com/, Sept. 2020, accessed on 2020-09-01.
##[38] Apache Software Foundation, “Apache httpd,” https://httpd.apache.org/, Apr. 2020, accessed on 2020-09-01.
##[39] HAProxy community edition, “Haproxy,” http://www.haproxy.org/, Dec. 2019, accessed on 2020-09-01.
##[40] The Linux Foundation, “Opendaylight,” https://www.opendaylight.org/, Dec. 2018, accessed on 2020 09-01.
##[41] D. Mosberger and T. Jin, “Httperf—a tool for measuring web server performance,” SIGMETRICS Perform. Eval. Rev., vol. 26, no. 3, p. 31–37, Dec. 1998.
##[42] T. Benson, A. Anand, A. Akella, and M. Zhang, “Understanding data center traffic characteristics,” SIGCOMM Comput. Commun. Rev., vol. 40, no. 1, p. 92–99, Jan. 2010.
##[43] Z. Liu, N. Niclausse, and C. Jalpa-Villanueva, “Traffic model and performance evaluation of web servers,” Performance Evaluation, vol. 46, no. 2, pp. 77 – 100, Oct. 2001.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/258
Stochastic Spintronic Neuron for Hardware Implementation of Neural Networks
0
The hardware implementation of neural networks has always been of interest to researchers as it can significantly increase the efficiency and application of neural networks due to the distributed nature of Artificial Neural Networks (ANNs) in both memory and computation. Direct implementation of ANNs also offers large gains when scaling the network sizes. Stochastic neurons are among the most significant aspects of machine learning algorithms and are very important in different neural networks. In this paper, a hardware model for the stochastic neuron based on the two-in-one magnetic tunnel junction (TiO-MTJ) in a subcritical current switching regime is proposed. The use of TiO-MTJ has reduced the area of the proposed neuron and eliminated the risk of MTJ read disturbance. Functional evaluation of the proposed model demonstrates that the behavior of the proposed model is comparable to the mathematical description of the stochastic neuron, and it has a negligible error in comparison with the theoretical model. The simulation results of image binarization over 10,000 images indicate that the proposed hardware model has only 0.25% pack signal-to-noise ratio (PSNR) and 0.02% structural similarity (SSIM) variation compared to its software-based counterpart. The results of corners simulations also show the proper performance of the proposed neuron even in the presence of inevitable major process variations.
13
19
Abdolah
Amirany
Shahid Beheshti University
ایران
Mohammad
Hossein Moaiyeri
Shahid Beheshti University
ایران
Kian
Jafari
Shahid Beheshti University
ایران
Masoud
Meghdadi
Shahid Beheshti University
ایران
Stochastic Neuron
Spintronic
Magnetic Tunnel Junction (MTJ)
Neural Networks
Image Binarization
https://jcse.ir/ow_userfiles/plugins/base/attachments/61e14b63bb0c4_61e14b63bab4b.pdf
[[1] A. Amirany, M. H. Moaiyeri, and K. Jafari, "Nonvolatile Associative Memory Design Based on Spintronic Synapses and CNTFET Neurons," IEEE Transactions on Emerging Topics in Computing, pp. 1-1, 2020.
##[2] D. Shin and H.-J. Yoo, "The Heterogeneous Deep Neural Network Processor With a Non-von Neumann Architecture," Proceedings of the IEEE, pp. 1-16, 2019.
##[3] S. Basu, R. E. Bryant, G. De Micheli, T. Theis, and L. Whitman, "Nonsilicon, Non-von Neumann Computing—Part I [Scanning the Issue]," Proceedings of the IEEE, vol. 107, no. 1, pp. 11-18, 2019.
##[4] A. Amirany and R. Rajaei, "Low Power, and Highly Reliable Single Event Upset Immune Latch for Nanoscale CMOS Technologies," presented at the Electrical Engineering (ICEE), Iranian Conference on, 2018.
##[5] M. Krishna Gopi Krishna, A. Roohi, R. Zand, and R. F. DeMara, "Heterogeneous energy-sparing reconfigurable logic: spin-based storage and CNFET-based multiplexing," IET Circuits, Devices Systems, vol. 11, no. 3, pp. 274-279, 2017.
##[6] C. Pan and A. Naeemi, "Non-Boolean Computing Benchmarking for Beyond-CMOS Devices Based on Cellular Neural Network," IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 2, pp. 36-43, 2016.
##[7] A. Amirany, M. H. Moaiyeri, and K. Jafari, "Process-in-Memory Using a Magnetic-Tunnel-Junction Synapse and a Neuron Based on a Carbon Nanotube Field-Effect Transistor," IEEE Magnetics Letters, vol. 10, pp. 1-5, 2019.
##[8] A. Amirany, K. Jafari, and M. H. Moaiyeri, "True Random Number Generator for Reliable Hardware Security Modules Based on a Neuromorphic Variation-Tolerant Spintronic Structure," IEEE Transactions on Nanotechnology, pp. 1-1, 2020.
##[9] A. Sengupta, Y. Shim, and K. Roy, "Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets," IEEE Trans Biomed Circuits Syst, vol. 10, no. 6, pp. 1152-1160, Dec 2016.
##[10] Z. I. Mannan, S. P. Adhikari, C. Yang, R. K. Budhathoki, H. Kim, and L. Chua, "Memristive Imitation of Synaptic Transmission and Plasticity," IEEE Trans Neural Netw Learn Syst, Feb 11 2019.
##[11] Y. Yan et al., "Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype," IEEE Trans Biomed Circuits Syst, vol. 13, no. 3, pp. 579-591, Jun 2019.
##[12] A. Amirany, M. H. Moaiyeri, and K. Jafari, "Bio-Inspired Nonvolatile and Low-Cost Spin-Based 2-Bit per Cell Memory," presented at the 25th International Computer Conference, Computer Society Of Iran, Tehran, Iran, 2020.
##[13] A. Amirany, K. Jafari, and M. H. Moaiyeri, "BVA-NQSL: A Bio-inspired Variation Aware Nonvolatile Quaternary Spintronic Latch," IEEE Magnetics Letters, vol. 11, pp. 1-5, 2020.
##[14] C. Pan and A. Naeemi, "A Proposal for Energy-Efficient Cellular Neural Network Based on Spintronic Devices," IEEE Transactions on Nanotechnology, vol. 15, no. 5, pp. 820-827, 2016.
##[15] K. Gurney, An Introduction to Neural Networks. Taylor \\amp; Francis, Inc., 1997, p. 288.
##[16] S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, 1994, p. 768.
##[17] C. Turchetti, Stochastic Models of Neural Networks. IOS Press, 2004.
##[18] A. Mondal and A. Srivastava, "Energy-efficient Design of MTJ-based Neural Networks with Stochastic Computing," ACM Journal on Emerging Technologies in Computing Systems, vol. 16, no. 1, pp. 1-27, 2020.
##[19] A. Amirany, K. Jafari, and M. H. Moaiyeri, "High-Performance and Soft Error Immune Spintronic Retention Latch for Highly Reliable Processors," presented at the Electrical Engineering (ICEE), Iranian Conference on, 2020.
##[20] Y. Wang, Y. Zhang, E. Y. Deng, J. O. Klein, L. A. B. Naviner, and W. S. Zhao, "Compact model of magnetic tunnel junction with stochastic spin transfer torque switching for reliability analyses," Microelectronics Reliability, vol. 54, no. 9-10, pp. 1774-1778, 2014.
##[21] R. Rajaei and A. Amirany, "Nonvolatile Low-Cost Approximate Spintronic Full Adders for Computing in Memory Architectures," IEEE Transactions on Magnetics, vol. 56, no. 4, pp. 1-8, 2020.
##[22] R. Rajaei and A. Amirany, "Reliable, High-Performance, and Nonvolatile Hybrid SRAM/MRAM-Based Structures for Reconfigurable Nanoscale Logic Devices," Journal of Nanoelectronics and Optoelectronics, vol. 13, no. 9, pp. 1271-1283, 2018.
##[23] S. Huda and A. Sheikholeslami, "A Novel STT-MRAM Cell With Disturbance-Free Read Operation," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 60, no. 6, pp. 1534-1547, 2013.
##[24] J. Grollier, D. Querlioz, K. Y. Camsari, K. Everschor-Sitte, S. Fukami, and M. D. Stiles, "Neuromorphic spintronics," Nature Electronics, 2020.
##[25] J. C. Slonczewski, "Conductance and exchange coupling of two ferromagnets separated by a tunneling barrier," Phys Rev B Condens Matter, vol. 39, no. 10, pp. 6995-7002, Apr 1 1989.
##[26] Y. Zhang et al., "Compact Modeling of Perpendicular-Anisotropy CoFeB/MgO Magnetic Tunnel Junctions," IEEE Transactions on Electron Devices, vol. 59, no. 3, pp. 819-826, 2012.
##[27] A. Amirany, F. Marvi, K. Jafari, and R. Rajaei, "Nonvolatile Spin-Based Radiation Hardened Retention Latch and Flip-Flop," IEEE Transactions on Nanotechnology, vol. 18, pp. 1089-1096, 2019.
##[28] Y. Qu, J. Han, B. F. Cockburn, W. Pedrycz, Y. Zhang, and W. Zhao, "A true random number generator based on parallel STT-MTJs," presented at the Design, Automation Test in Europe Conference Exhibition (DATE), 2017.
##[29] A. Amirany and R. Rajaei, "Fully Nonvolatile and Low Power Full Adder Based on Spin Transfer Torque Magnetic Tunnel Junction With Spin-Hall Effect Assistance," IEEE Transactions on Magnetics, vol. 54, no. 12, pp. 1-7, 2018.
##[30] "Predictive Technology Model (PTM) Low Power 45nm Metal Gate / High-K / Strained-Si." http://ptm.asu.edu/modelcard/LP/45nm_LP.pm (accessed 2019)
##[31] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB. Pearson Education India, 2004.
##[32] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans Image Process, vol. 13, no. 4, pp. 600-12, Apr 2004.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/259
A Novel Approach to Improve Rate-Distortion-Complexity in Versatile Video Coding Standard
0
Versatile Video Coding (VVC) achieves up to 30% bitrate reduction at the same quality level compared to its predecessor, High Efficiency Video Coding (HEVC). It could support resolutions from 4K to 16K as well as 360° videos. Some new coding tools, such as AFFINE, Integer Motion Vector (IMV), Decoder-side Motion Vector Refinement (DMVR), and Triangle are proposed for VVC to improve the encoder efficiency. But, these new coding tools usually impose high computational complexity on the encoder side. In this paper, we provide a new approach to reduce the computational complexity of the Rate-Distortion Optimization (RDO) process in the encoder side of VVC. In the proposed approach, first, the effectiveness of each coding tool at various parts of the scene is estimated. The results of the experiments show that some of the coding tools--,i.e., AFFINE and IMV, have much better performance in borderline CTUs. So, the proposed approach suggests considering these coding tools in the RDO process, just for the borderline CTUs. This way the computational complexity is decreased considerably without affecting the coding performance. Simulation results show that by disabling the AFFINE and IMV coding tools in the rate-distortion optimization process of non-borderline CTUs, the encoding gain is reduced by only 0.88% and 0.72% BD-rate, but the processing time is reduced by 11.70% and 63.91%, respectively. As the second approach, the correlation between the various coding tools is investigated. Our simulation results show that the AFFINE and Triangle coding tools are highly correlated to each other. So, in the rate-distortion process, if the encoder decided to disable the AFFINE coding tool, the Triangle coding tool is also can be considered disabled without examining the rate-distortion process for this coding tool. This way, the computational complexity is reduced, by 4.96%, on average, without affecting the encoding gain considerably.
20
27
Amir
Rezaeieh
K. N. Toosi University of Technology
ایران
Hoda
Roodaki
School of Computer Science
ایران
Versatile Video Coding standard (VVC)
AFFINE coding tool
DMVR coding tool
GBI coding tool
BIO coding tool
Triangle coding tool
IMV coding tool
https://jcse.ir/ow_userfiles/plugins/base/attachments/62193fc0ab13a_62193fc0a9c11.pdf
[[1] M. Manohara, R. Mudumbai, J. Gibson and U. Madhow, "Error correction scheme for uncompressed HD video over wireless," 2009 IEEE International Conference on Multimedia and Expo, pp. 802-805, 2009.
##[2] M-Z. Wang, S. Wan, H. Gong, and M-Y. Ma, “Attention-Based Dual-Scale CNN In-Loop Filter for Versatile Video Coding,” IEEE Access, vol. 7, pp. 145214 – 145226, 2019.
##[3] A. Rezaeieh and H. Roodaki, "A Method for Rate-Distortion-Complexity Optimization in Versatile Video Coding Standard," 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pp. 1-5, 2021.
##[4] A. Tissier, A. Mercat, T. Amestoy, W. Hamidouche, J. Vanne, and D. Menard, “Complexity Reduction Opportunities in the Future VVC Intra Encoder,” 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6, 2019.
##[5] L. Li, H. Li, D. Liu, H. Yang, S. Lin, H. Chen, and F. Wu, “An Efficient Four-Parameter Affine Motion Model for Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, pp. 1934 – 1948, 2017.
##[6] X. Ji, D. Zhao, and W. Gao, “Concealment of Whole-Picture Loss in Hierarchical B-Picture Scalable Video Coding,” IEEE Transactions on Multimedia, vol. 11, pp. 11-22, 2009.
##[7] X. Chen, J. An and J. Zheng, “Decoder-Side Motion Vector Refinement Based on Bilateral Template Matching,” Joint Video Exploration Team of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting: Chengdu, CN, 15–21, 2016.
##[8] C-C. Chen, X. Xiu, Y. He, and Y. Ye, “Generalized Bi-prediction Method for Future Video Coding,” Picture Coding Symposium (PCS), pp. 1-5, 2016.
##[9] S. Aramvith and M-T. Sun, The Essential Guide to Video Processing (Second Edition), Academic Press, 2009.
##[10] D. PARK, J. LEE, J-W. KANG, and J-G. KIM, “Simplified Triangular Partitioning Mode in Versatile Video Coding,” IEICE Transactions on Information and Systems, vol. E103–D, pp. 472-475, 2020.
##[11] W. Chien and J. Boyce, “JVET AHG report: Tool reporting procedure (AHG13),” 13th JVET Meeting, Doc. JVET-M0013, 2019.
##[12] I. E. Richardson, The H.264 Advanced Video Compression Standard 2nd Edition, John Wiley and Sons, 2003.
##[13] X. Li, P. Amon, A. Hutter, and A. Kaup, “Lagrange Multiplier Selection for Rate-Distortion Optimization in SVC,” Picture Coding Symposium, pp. 1-4, 2009.
##[14] A. Tissier, A. Mercat, T. Amestoy, W. Hamidouche, J. Vanne, and D. Menard, "Complexity Reduction Opportunities in the Future VVC Intra Encoder," IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6, 2019.
##[15] M. Aklouf, M. Leny, F. Dufaux, and M. Kieffer, "Low Complexity Versatile Video Coding (VVC) for Low Bitrate Applications," 8th European Workshop on Visual Information Processing (EUVIP), pp. 22-27, 2019.
##[16] H. Gao, S. Esenlik, Z. Zhao, E. Steinbach, and J. Chen, "Low Complexity Decoder Side Motion Vector Refinement for VVC," Picture Coding Symposium (PCS), pp. 1-5, 2019.
##[17] S-H. Park and J-W. Kang, “Fast Affine Motion Estimation for Versatile Video Coding (VVC) Encoding,” IEEE Access, vol. 7, pp. 1-1, 2019.
##[18] Z. Zhang, X. Zhao, X. Li, Z. Li and S. Liu, “Fast Adaptive Multiple Transform for Versatile Video Coding,” Data Compression Conference (DCC), Snowbird, pp. 63-72, 2019.
##[19] Q. H. Van, L. D. T. Hue, V. D. Du, V. N. Hong, and X. HoangVan, "Complexity Controlled Side Information Creation for Distributed Scalable Video Coding,” 3rd International Conference on Recent Advances in Signal Processing, Telecommunications Computing (SigTelCom), pp. 104-108, 2019.
##[20] K-H.Tai, M-J. Chen, J-R. Lin, R-Y. Huang, C.-H. Yeh, C-Y. Chen, S. D. Lin, R-M. Weng, C.-Y. Chang, "Acceleration for HEVC Encoder by Bimodal Segmentation of Rate-Distortion Cost and Accurate Determination of Early Termination and Early Split," IEEE Access, vol. 7, pp. 45259-45273, 2019.
##[21] J. Boyce, K. Suehring, X. Li and V. Seregin, “JVET common test conditions and software reference configurations”, Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11 10th Meeting: San Diego, US, 10–20 Apr. 2018.
##[22] https://jvet.hhi.fraunhofer.de/trac/vvc/browser/vtm, last access on November 2021.
##[23] G. Bjøntegaard, “Calculation of average PSNR differences between RD curves,” ITU T SG16/Q6, Doc. VCEG-M33, April 2001.
##[24] W. Ren, W. He, and Y. Cui, “An Improved Fast Affine Motion Estimation Based on Edge Detection Algorithm for VVC,” Symmetry 2020, vol. 12, PP. 1143, 2020.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/260
A Semantic-based Feature Extraction Method Using Categorical Clustering for Persian Document Classification
0
Natural Language Processing (NLP) is one of the promising ﬁelds of artiﬁcial intelligence. Recently, a high volume of text data has been generated through the Internet. This kind of data is a valuable source of information that can be used in various ﬁelds such as information retrieval, recommender systems, etc. One practical task of text mining is document classiﬁcation. In this paper, we mainly focus on Persian document classiﬁcation. We introduce a new feature extraction approach derived from the combination of K-means clustering and Word2Vec to acquire semantically relevant and discriminant word representations. We call our proposed approach CC-Word2Vec (Categorical Clustering-Word2Vec) and use different classification models to compare the performance of our approach with other techniques like Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and Latent Dirichlet Allocation (LDA) methods. Our proposed method resulted in an improvement in the obtained accuracy of all classifiers in comparison with other techniques.
28
35
Saeedeh
Davoudi
School of Engineering Science
ایران
Sayeh
Mirzaei
College of Engineering
ایران
Persian document classiﬁcation
TF-IDF
Word2Vec
CC-Word2Vec
MLP
GB
LDA
K-Means
https://jcse.ir/ow_userfiles/plugins/base/attachments/61e70c28d6c1a_61e70c28d341f.pdf
[[1] M. Farhoodi and A. Yari, "Applying machine learning algorithms for automatic Persian text classification," 2010 6th International Conference on Advanced Information Management and Service (IMS), Seoul, 2010, pp. 318-323.
##[2] S. Zobeidi, M. Naderan, and S. E. Alavi, ”Effective text classiﬁcation using multi-level fuzzy neural network,” 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Qazvin, 2017, pp. 91-96.
##[3] Hu, Xia, and Huan Liu. "Text analytics in social media." In Mining text data, pp. 385-414. Springer, Boston, MA, 2012.
##[4] Ayoub Bagheri, Hamed Farzanehfar, Mohammad Hossein Saraee, Mohammad Reza Ahmadzadeh, The Farsi text classiﬁcation using Bayesian Algorithm. Second Iranian Conference on Data Mining of Iran, 2008.
##[5] Bina, B., M. H. Ahmadi, M. Rahgozar, “Farsi Text Classiﬁcation Using N-Grams and Knn Algorithm A Comparative Study.” DMIN (2008).
##[6] Fabrizio Sebastiani. 2002. Machine learning in automated text categorization. ACM Comput. Surv. 34, 1 (March 2002), 1-47.
##[7] S. Z. Mishu and S. M. Raﬁuddin, ”Performance analysis of supervised machine learning algorithms for text classiﬁcation,” 2016 19th International Conference on Computer and Information Technology (ICCIT), Dhaka, 2016, pp. 409-413.
##[8] F. Alzamzami, M. Hoda and A. E. Saddik, ”Light Gradient Boosting Machine for General Sentiment Classiﬁcation on Short Texts: A Comparative Evaluation,” in IEEE Access, vol. 8, pp. 101840-101858, 2020.
##[9] Resham N. Waykole, Anuradha D. Thakare. A review of feature extraction methods for text classification. International Journal of Advance Engineering and Research Development Volume 5, Issue 04, April -2018
##[10] Jahantigh, Morteza, Negin Daneshpour, Mohammad Erfani, and Nargess Orojlou. "Presenting an improved combination for classification of Persian texts." In 2016 Eighth International Conference on Information and Knowledge Technology (IKT), pp. 234-240. IEEE, 2016.
##[11] S. Ghasemi and A. H. Jadidinejad, ”Persian text classiﬁcation via character-level convolutional neural networks,” 2018 8th Conference of AI and Robotics and 10th RoboCup Iran Open International Symposium (IRANOPEN), Qazvin, 2018, pp. 1-6.
##[12] N Rezaeian, G Novikova, Persian Text Classiﬁcation using naive Bayes algorithms and Support Vector Machine algorithm, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), Vol. 8, No. 1, March 2020, pp. 178-188.
##[13] S. E. Rad, and A. R. Behjat, ”Document Classiﬁcation base on Ensemble Classiﬁers Support Vector Machine, Multi-layer Perceptron and k-Nearest Neighbors.” J. Biochem. Tech., vol. 2, pp. 174-182, Sep. 2019.
##[14] Ashkan, Jafari, Ezadi Hamed, Hossennejad Mihan, and Noohi Taher. "Improvement in automatic classification of Persian documents by means of support vector machine and representative vector." In International Conference on Innovative Computing Technology, pp. 282-292. Springer, Berlin, Heidelberg, 2011.
##[15] P. Ahmadi, M. Tabandeh and I. Gholampour, ”Persian text classiﬁcation based on topic models,” 2016 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, 2016, pp. 86-91.
##[16] Mikolov, Tomas, Kai Chen, G. S. Corrado and J. Dean. “Efﬁcient Estimation of Word Representations in Vector Space”. ICLR (2013).
##[17] Wang, Zhibo, Long Ma, and Yanqing Zhang. "A hybrid document feature extraction method using latent Dirichlet allocation and word2vec." In 2016 IEEE first international conference on data science in cyberspace (DSC), pp. 98-103. IEEE, 2016.
##[18] Rehurek, Radim, and Petr Sojka, “Software Framework for Topic Modelling with Large Corpora.” In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. 2010.
##[19] Qaiser, Shahzad, and Ramsha Ali. “Text mining: Use of TF-IDF to Examine the Relevance of Words to Documents.” International Journal of Computer Applications 181 (2018): 25-29.
##[20] Pedregosa, Fabian, et al. “Scikit-learn: Machine learning in Python.” the Journal of Machine Learning Research 12 (2011): 2825-2830.
##[21] Peng, Min, Chongyang Wang, Tong Chen, Guangyuan Liu, and Xiaolan Fu. "Dual temporal scale convolutional neural network for micro-expression recognition." Frontiers in psychology 8 (2017): 1745.
##[22] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, null (3/1/2003), 993-1022.
##[23] https://engineering.linkedin.com/blog/2020/open-sourcing-detext.
##[24] https://www.sobhe.ir/hazm/
##[25] AleAhmad, Abolfazl, et al. “Hamshahri: A standard Persian text collection.” Knowledge-Based Systems 22.5 (2009): 382-387.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/264
Reliability Analysis of Sequential Logic Circuits Using Signal Flow Graphs
0
As the transistor sizes shrunk in advanced VLSI circuits recently, their susceptibility to the transient faults significantly has been increased which makes the reliability analysis of logic circuits more important. However, the reliability analysis of logic circuits is high computational complexity, because of multiple simultaneous errors, propagating the errors, masking mechanisms, re-convergent paths, etc. This figure becomes even more complicated in sequential circuits due to the feedback loops, where errors may store in flip-flops and re-enter the circuit. Moreover, it is essential to consider the waveform of errors in sequential circuits into account, which makes it more complex. This paper proposes a fast and scalable approach using probabilistic signal flow graphs to analyze the reliability of sequential logic circuits in the presence of multiple event transients. The proposed approach is based on nonlinear probabilistic graphs to find the probability of error for each gate after passing the circuit for an infinite number of clock cycles. Also, the proposed approach introduces a probability distribution function model to propagate the waveform of errors in the circuit to consider all masking mechanisms. Also, the proposed approach benefits from scalable runtime and memory requirements. Based on the simulation results, the proposed approach exhibits the computational complexity of.
36
43
Vahid
Hamiyati Vaghef
Information and Communication Technology Research Department
ایران
Ali
Peiravi
Niroo Research Institute
ایران
Sequential logic circuits
Transient faults
Probabilistic signal flow graph
Matrix sparsity
Mason’s rule
https://jcse.ir/ow_userfiles/plugins/base/attachments/622c848beb0ef_622c848bde289.pdf
[[1] H. T. Nguyen, Y. Yagil, N. Seifert and M. Reitsma, “Chip-level soft error estimation method,” IEEE Device and Materials Reliability, vol. 5, no. 3, pp. 365-381, NSP. 2005.
##[2] S. Borkar, “Designing reliable systems from unreliable components: the challenge of transistor variability and degradation,” IEEE Micro., vol. 25, no. 6, pp. 10–16, 2005.
##[3] K. Parker and E. McCluskey, “Probabilistic treatment of general combinational networks,” IEEE Trans. on Electronic Computers, vol. C-24, no. 6, pp. 668–670, 1975.
##[4] R. I. Bahar, J. Chen and J. Mundy, “A probabilistic-based design methodology for nanoscale computation,” in Proc. IEEE/ACM Int. Conf. on Computer Aided Design (ICCAD’03), San Jose, CA, pp. 480–486, Nov. 2003.
##[5] K. N. Patel, I .L. Markov and J. P. Hayes, “Evaluating circuit reliability under probabilistic gate-level fault models,” in Int. Workshop on Logic and Synthesis (IWLS), pp. 59-64, 2003.
##[6] S. Krishnaswamy, G. F. Viamonte, I. L. Markov and J. P. Hayes, “Accurate reliability evaluation and enhancement via probabilistic transfer matrices,” in Proc. of Design Automation and Test in Europe (DATE 2005), Munich, Germany, pp. 282–287, March 2005.
##[7] S. Krishnaswamy, I. L. Markov and J. P. Hayes, “Tracking uncertainty with probabilistic logic circuit testing,” IEEE Design and Test of Computers, vol. 24, no. 4, pp. 312-321, 2007.
##[8] S. Krishnaswamy, G. F. Viamontes, I.L. Markov, and J.P. Hayes, “Probabilistic transfer matrices in symbolic reliability analysis of logic circuits,” ACM Trans. Design Automation of Electronic Systems, vol. 13, no. 1, Article 8, 2008.
##[9] G. Norman, D. Parker, M. Kwiatkowska and S. Shukla, “Evaluating the reliability of NAND multiplexing with PRISM,” IEEE Trans. CAD of Integrated Circuits and Systems, vol. 24, no. 10, pp. 1629-1637, 2005.
##[10] B. S. Gill, C. Papachristou, F. G. Wolff and N. Seifert, “Node sensitivity analysis for soft errors in CMOS logic,” Proc. Test Conf, 2005 (ITS 2005), Austin, TX, pp. 984–972, Nov. 2005.
##[11] N. M. Zivanov and D. Marculescu, “Circuit reliability analysis using symbolic techniques,” IEEE Trans. CAD Integrated Circuits Syst., vol. 25, no. 12, pp. 2638–2649, 2006.
##[12] N. M. Zivanov and D. Marculescu, “Multiple transient faults in combinational and sequential circuits: a systematic approach,” IEEE Trans. CAD, vol. 29, no. 10, pp. 1614-27, 2010.
##[13] T. Rejimon and S. Bhanja, “Scalable probabilistic computing models using Bayesian networks,” in Proc. 48th Int. Midwest Symp. Circuits Syst., vol. 1, Covington KY, pp. 712–715, Aug. 2005.
##[14] T. Rejimon and S. Bhanja, “Probabilistic error model for unreliable nano-logic gates,” in Proc. 6th IEEE Nano., vol. 1, Cincinnati, Ohio, pp. 47-50, July. 2006.
##[15] T. Rejimon, K. Lingasubramanian and S. Bhanja, “Probabilistic error modeling for nano-domain logic circuits,” IEEE Trans. VLSI, vol. 17, no. 1, pp. 55–65, 2009.
##[16] G. Asadi and M. B. Tahoori, “An analytical approach for soft error rate estimation in digital circuits,” in Proc. Int. Symp. Circuits and Systems (ISCAS 2005), vol. 3, Kobe, Japan, pp. 2991-2994, 2005.
##[17] N. Mohyuddin, E. Pakbaznia and M. Pedram, “Probabilistic error propagation in logic circuits using the Boolean difference calculus,” in Proc. 26th Int. Conf. Computer Design (ICCD 2008), Lake Tahoe, CA, pp. 7-13, Oct. 2008.
##[18] S. Gupta, A. J. C. Gemund and R. Abreu, “Probabilistic error propagation modeling in logic circuits,” in Proc. 4th Software Testing, Verification and Validation Workshops (ICSTW 2011), Berlin, pp. 617-623, March 2011.
##[19] M. Fazeli, S. N. Ahmadian, S. G. Miremadi, H. Asadi, M. B. Tahoori, “Soft error rate estimation of digital circuits in the presence of multiple event transients (METs),” Proc. IEEE/ACM Int. Conf. Design Automation and Test in Europe (DATE 2011), Grenoble, France, pp. 70-75, March 2011.
##[20] H. Asadi, M. B. Tahoori, “Soft error modeling and remediation techniques in ASIC designs,” Microelectronic, vol. 41, no. 8, pp. 506-522, 2010.
##[21] H. Asadi, M. B. Tahoori, M. Fazeli and S. G. Miremadi “Efficient algorithms to accurately compute derating factors of digital circuits,” Microelectronics Reliability, vol. 52, no. 6, pp. 1215-1226, 2012.
##[22] J. Han, E. Taylor, J. Gao and J. Fortes, “Towards accurate and efficient reliability modeling of nanoelectronic circuits,” in Proc. 6th IEEE Conf. Nanotechnology, Cincinnati, Ohio, pp. 395-398, 2006.
##[23] J. Han, H. Chen, E. Boykin and J. Fortes, “Reliability evaluation of logic circuits using probabilistic gate models,” Microelectronics Reliability, vol. 51, no. 2, pp. 468-476, 2011.
##[24] D. T. Franco, M. C. Vasconcelos, L. Naviner and J-F. Naviner, “Signal probability for reliability evaluation of logic circuits,” Microelectronics Reliability, vol. 48, no. 8-9, pp. 1586–1591, 2008.
##[25] J. T. Flaquer, J. M. Daveau, L. Naviner and P. Roche, “Fast reliability analysis of combinatorial logic circuits using conditional probabilities,” Microelectronics Reliability, vol. 50, no. 9-11, pp. 1215-1218, 2010.
##[26] J. T. Flaquer, J. M. Daveau, L. Naviner and P. Roche, “An approach to reduce computational cost in combinatorial logic netlist reliability analysis using circuit clustering and conditional probabilities,” in Proc. 17th IEEE Int. On-line Testing Symposium (IOLTS 2011), Athens, pp. 98-103, July 2011.
##[27] M. R. Choudhury and K. Mohanram, “Reliability analysis of logic circuits,” IEEE Trans. CAD. Integrated Circuits Syst., vol. 28, no. 3, pp. 392-405, 2009.
##[28] S. J. Seyyed Mahdavi and K. Mohammadi, “Improved single-pass approach for reliability analysis of digital combinational circuits,” Microelectronics Reliability, vol. 51, no. 2, pp. 477-484, 2011.
##[29] S. Krishnaswamy, S. M. Plaza, I. L. Markov and J. P. Hayes, “Signature-based SER analysis and design of logic circuits,” IEEE Trans. CAD Integrated Circuits Syst., vol. 28, no. 1, pp. 74–86, 2009.
##[30] H. Chen and J. Han, “Stochastic computational models for accurate reliability evaluation of logic circuits,” in Proc. 20th Great Lakes Symp. VLSI (GLSVLSI 2010), vol. 10, Providence, Rhode Island, pp. 61–68, 2010.
##[31] J. Han, H. Chen, J. Liang, P. Zhu, Z. Yang and F. Lombardi, “A stochastic computational approach for accurate and efficient reliability evaluation,” IEEE Trans. Computers, vol. 63, no. 6, pp. 1336–1350, 2014.
##[32] C. C. Yu and J. P. Hayes, “Trigonometric method to handle realistic error probabilities in logic circuits,” in Proc. IEEE Design, Automation and Test in Europe (DATE 2011), Grenoble, France, pp. 64-69, March 2011.
##[33] L. Chen, M. Ebrahimi, M.B. Tahoori, “CEP: Correlated error propagation for hierarchical soft error analysis,” J Electron Testing, Springer, vol. 29, pp. 143–158, 2013.
##[34] S. Gangadhar and S. Tragoudas, “A probabilistic approach to diagnose SETs in sequential circuits,” Electron Testing, Springer, vol. 29, no. 3, pp 317-330, 2013.
##[35] A. Evans, D. Alexandrescu, E. Costenaro, L. Chen, “Hierarchical RTL-based combinatorial SER estimation,” in Proc. 19th Int. On-Line Testing Symp. (IOLTS 2013), Crete, Greece, pp. 139-144, July 2013.
##[36] S.N. Pagliarini, A. B. Dhia, L. A. B. Naviner and J.-F. Naviner, “Snap: A novel hybrid method for circuit reliability assessment under multiple faults,” Microelectronics Reliability, vol. 53, no. 911, pp. 1230–1234, 2013.
##[37] S. Rezaei, S. G. Miremadi, H. Asadi and M. Fazeli, “Soft error estimation and mitigation of digital circuits by characterizing input patterns of logic gates,” Microelectronics Reliability, vol. 54, no. 6-7, pp 1412-1420, 2014.
##[38] H. Pahlevanzadeh, Q. Yu, “A new analytical model of SET latching probability for circuits experiencing single or multiple-cycle single-event transients,” Electron Testing, Springer, vol. 30, no. 5, pp. 595-609, 2014.
##[39] M. S. Ansari, A. Mahani, J. Han and B. F. Cockburn, “A novel gate grading approach for soft error tolerance in combinational circuits,” IEEE Conf. Electrical and Computer Engineering, Vancouver, Canada, pp. 1-4, May 2016.
##[40] M. Ebrahimi, H. Asadi, R. Bishnoi and M. B. Tahoori, “Layout-based modeling and mitigation of multiple event transients,” IEEE Trans. on CAD. Integrated Circuits Syst., vol. 35, no. 3, pp. 367-379, 2016.
##[41] Y. Du and S. Chen, “A novel layout-based single event transient injection approach to evaluate the soft error rate of large combinational circuits in complimentary metal-oxide-semiconductor bulk technology,” IEEE Trans. on Reliability, vol. 65, no. 1, pp. 248-255, 2016.
##[42] B. Ghavami, M. Raji, K. Saremi, H. Pedram, “An Incremental Algorithm for Soft Error Rate Estimation of Combinational Circuits,” IEEE Trans. on Device and Materials Reliability, vol. 18, no. 3, pp. 463-473, 2018.
##[43] W. Ibrahim, H. Ibrahim, “Multithreaded and Reconvergent Aware Algorithms for Accurate Digital Circuits Reliability Estimation,” IEEE Trans. Reliability, vol. 68, no. 2, pp. 514-525, 2019.
##[44] V. H. Vaghef, A. Peiravi, “A graph based approach for reliability analysis of nano-scale VLSI logic circuits,” Microelectronics Reliability, vol. 54, no. 6-7, pp. 1299-1306, 2014.
##[45] V. H. Vaghef, A. Peiravi, “Node-to-node error sensitivity analysis using a graph based approach for VLSI logic circuits,” Microelectronics Reliability, vol. 55, no. 1, pp. 264-271, 2015.
##[46] N. M. Zivanov and D. Marculescu, “Modeling and optimization for soft-error reliability of sequential circuits,” IEEE Trans. CAD Integrated Circuits Syst. vol. 27, no. 5, pp. 803-816, 2008.
##[47] C. C. Yu and J. P. Hayes, “Scalable and accurate estimation of probabilistic behavior in sequential circuits,” in Proc. 28th VLSI Test Symposium (VTS 2010), Santa Cruz, CA, pp. 165-170, Apr. 2010.
##[48] K. Lingasubramanian and S. Bhanja,"Probabilistic error modeling for sequential logic," in Proc. 7th IEEE Conf. Nanotechnology, Hong Kong, China, pp. 616-620, Aug. 2007.
##[49] J. Monteiro, S. Devadas, B. Lin, “A methodology for efficient estimation of switching activity in sequential logic circuits,” in Proc. 31st Conf. Design Automation (DAC), San Diego, CA, pp. 12-17, June1994.
##[50] J. Monteiro and S. Devadas “Power Estimation for Sequential Circuits,” Computer-aided design techniques for low power sequential logic circuits, Springer, pp. 35-80, 1997.
##[51] C-Y Tsui, M. Pedram and A. M. Despain, “Exact and approximate methods for calculating signal and transition probabilities in FSMs,” in Proc. 31st Conf. Design Automation (DAC), San Diego, CA, USA, pp. 18-23, June1994.
##[52] S. J. S. Mahdavi and K. Mohammadi, “SCRAP: sequential circuits reliability analysis program,” Microelectronics Reliability, vol. 49, no. 8, pp. 924–933, 2009.
##[53] T.A. Davis, “Direct methods for sparse linear systems,” Philadelphia, SIAM, 2006.
##[54] P. Shivakumar, M. Kistler, S. W. Keckler, D. Burger, L. Alvisi, “Modeling the effect of technology trends on the soft error rate of combinational logic,” Int. conf. on Dependable Systems and Networks (DSN 2002), Bethesda, MD, USA, pp. 389-398, June 2002.
##[55] M. E. Van Valkenburg, “Network analysis,” Englewood Cliffs, NJ:Prentice-Hall, 1974.
##[56] S. J. Mason, “Feedback theory – some properties of signal flow graphs,” in Proc. of IRE, vol. 41, no.9, New York, pp. 1144-1156, 1953.
##[57] S. J. Mason, “Feedback theory: further properties of signal flow graphs,” in Proc. IRE, vol. 44, no.7, New York, pp. 920-926, 1956.
##[58] F. Firouzi, M. E. Salehi, F. Wang, S. M. Fakhraie, “An accurate model for soft error rate estimation considering dynamic voltage,” Microelectronics Reliability, vol. 51, no. 2, pp. 460–467, 2011.
##[59] M. Omana, G. Papasso, D. Rossi and C. Metra, “A model for transient fault propagation in combinatorial logic,” in Proc. 9th Int. On-Line Testing Symp. (IOLTS 2003), Bologna, Italy, pp. 111-115, July 2003.
##[60] F. Wang and V. D. Agrawal, “Soft error rate determination for nanoscale sequential logic,” in Proc. 11th Int. Symp. Quality Electronic Design (ISQED 2010), San Jose, CA, pp. 225–230, March 2010.
##[61] http://www.si2.org/openeda.si2.org/projects/nangatelib, 2019.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/265
Guided Ridge Regression-Based Polarimetric-Spatial Feature Extraction for Classification of Polarimetric SAR Images
0
Synthetic aperture radar (SAR) acquires high resolution images containing rich spatial information. The Polarimetric SAR (PolSAR) images are a good source of polarimetric and spatial information appropriate for land cover classification. Two PolSAR image classification methods are introduced in this work: ridge regression-based polarimetric-spatial (RRPS) and the guided RRPS. The RRPS feature extraction method generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of the PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The guided RRPS method uses the weights of the guided filter to revise the probability maps corresponding to the initial classification map. The proposed RRPS and guided RRPS methods show high performance for PolSAR image classification in small sample size situations.
44
53
Maryam
Imani
Faculty of Electrical and Computer Engineering
ایران
Ridge regression
Polarimetric SAR
feature space projection
classification
guided filter
https://jcse.ir/ow_userfiles/plugins/base/attachments/622cc339cdea2_622cc339c45de.pdf
[[1] A. Liu, F. Wang, H. Xu and L. Li, "N-SAR: A New Multichannel Multimode Polarimetric Airborne SAR," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 9, pp. 3155-3166, Sept. 2018.
##[2] M. Ahishali, S. Kiranyaz, T. Ince and M. Gabbouj, "Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks," 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia, pp. 73-76, 2020.
##[3] P. A. A. Penna and N. D. A. Mascarenhas, "SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 7194-7208, Sept. 2019.
##[4] S. Wang, J. Pei, K. Liu, S. Zhang and B. Chen, "Unsupervised classification of POLSAR data based on the polarimetric decomposition and the co-polarization ratio," 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, pp. 424-427, 2011.
##[5] H. Maurya and R. K. Panigrahi, "PolSAR image classification using generalized scattering models," 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), Singapore, pp. 408-412, 2017.
##[6] S. T. Tu, J. Y. Chen, W. Yang and H. Sun, "Laplacian Eigenmaps-Based Polarimetric Dimensionality Reduction for SAR Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 1, pp. 170-179, Jan. 2012.
##[7] L. Zhang, L. Sun and W. M. Moon, "Polarimetric SAR image classification based on contextual sparse representation," 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 1837-1840, 2015.
##[8] H. Bi, L. Xu, X. Cao, Y. Xue and Z. Xu, "Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field," in IEEE Transactions on Image Processing, vol. 29, pp. 6601-6614, 2020.
##[9] A. Masjedi, M. J. Valadan Zoej and Y. Maghsoudi, "Classification of Polarimetric SAR Images Based on Modeling Contextual Information and Using Texture Features," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 932-943, Feb. 2016.
##[10] B. Zou, X. Xu and L. Zhang, "Object-Based Classification of PolSAR Images Based on Spatial and Semantic Features," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 609-619, 2020.
##[11] A. Tombak, İ. Türkmenli, E. Aptoula and K. Kayabol, "Pixel-Based Classification of SAR Images Using Feature Attribute Profiles," in IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 4, pp. 564-567, April 2019.
##[12] P. Zhang, B. Li, X. Tan, Y. Jiang, M. Li and Y. Wu, "Hybrid Conditional Random Fields Based on Complex-valued 3D CNN for PolSAR Image Classification," 2020 IEEE Radar Conference (RadarConf20), 2020, pp. 1-6.
##[13] X. Nie, R. Gao, R. Wang and D. Xiang, "Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 8, pp. 1456-1460, Aug. 2021.
##[14] X. Mao, X. Xiao and Y. Lu, "PolSAR Data-Based Land Cover Classification Using Dual-Channel Watershed Region-Merging Segmentation and Bagging-ELM," in IEEE Geoscience and Remote Sensing Letters, In Press, 2021.
##[15] R. Sharma and R. K. Panigrahi, "Texture Classification-Based NLM PolSAR Filter," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 8, pp. 1396-1400, Aug. 2021.
##[16] M. Imani and H. Ghassemian, "Morphology-based structure-preserving projection for spectral–spatial feature extraction and classification of hyperspectral data," IET Image Processing, vol. 13, no. 2, pp. 270-279, Feb. 2019.
##[17] K. Cho, S. Park, J. Cho, H. Moon and S. Han, "Automatic Urban Area Extraction From SAR Image Based on Morphological Operator," in IEEE Geoscience and Remote Sensing Letters, 2020.
##[18] X. Kang, S. Li and J. A. Benediktsson, "Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2666-2677, May 2014.
##[19] X. Kang, S. Li and J. A. Benediktsson, "Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 6, pp. 3742-3752, June 2014.
##[20] M. Imani, "A Random Patches Based Edge Preserving Network for Land Cover Classification Using Polarimetric Synthetic Aperture Radar Images," International Journal of Remote Sensing, 2021.
##[21] M. Imani and H. Ghassemian, "Ridge regression-based feature extraction for hyperspectral data," International Journal of Remote Sensing, vol. 36, no. 6, pp. 1728–1742, 2015.
##[22] Qingfu Zhang and Yiu Wing Leung, "A class of learning algorithms for principal component analysis and minor component analysis," in IEEE Transactions on Neural Networks, vol. 11, no. 1, pp. 200-204, Jan. 2000.
##[23] M. Imani and H. Ghassemian, "The Investigation of Sensitivity of SVM Classifier Respect to The Number of Features and The Number of Training Samples," 2nd International Conference on Sensors and Models in Photogrammetry and Remote Sensing, Tehran, Iran, pp. 209-214, 5 – 8 October 2013.
##[24] L. Gomez, L. Alvarez, L. Mazorra and A. C. Frery, "Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems," Neurocomputing, vol. 255, pp. 52-60, 2017.
##[25] X. Nie, S. Ding, X. Huang, H. Qiao, B. Zhang and Z. -P. Jiang, "An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 1, pp. 302-320, Jan. 2019.
##[26] Z. Zhang, H. Wang, F. Xu and Y. Jin, "Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7177-7188, Dec. 2017.
##[27] J. Cohen, “A coefficient of agreement from nominal scales,” Edu. Psychol. Meas., vol. 20, no. 1, pp. 37–46, 1960.
##[28] G. M. Foody, “Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy,” Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627–633, 2004.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/266
Nature-inspired and teaching-learning-based methods for improving convergence speed in multi-agent systems
0
This paper suggests a novel method for inverse optimal control of the multi-agent systems (MAS) via a linear quadratic regulator (LQR) based on meta-heuristic algorithms. In this regard, first, the consensus protocol is designed and then the cost function is optimized via Jaya algorithm (JA), teaching-learning algorithm (TLBO), a novel meta-heuristic algorithm called advanced teaching-learning (ATLBO) and water cycle algorithm (WCA). ATLBO consists of two phases with two random values in both phases which affect the convergence rate. The optimal value of the controller’s parameter is obtained via these algorithms. Simulation outputs show the usefulness of nature-inspired and learning-based methods to calculate the cost with a better convergence rate. This research consists of an inverse optimal control approach and meta-heuristic algorithms for solving the consensus problem with the least cost.
54
59
Ramin
Fotouhi
Department of Control Engineering
ایران
Mahdi
Pourgholi
Shahid Beheshti University
ایران
Control systems
optimal control
multi-agent systems
algorithms
distributed systems
https://jcse.ir/ow_userfiles/plugins/base/attachments/622c8ca9122c7_622c8ca910dd9.pdf
[[1] Y. Cao and W. Ren, "LQR-based optimal linear consensus algorithms," American Control Conference, pp. 5204-5209, 2009
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##[2] Y. Cao and W. Ren, "Optimal Linear-Consensus Algorithms: An LQR Perspective," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 3, pp. 819-830, June 2010.
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##[3] B. Mu and Y. Shi, "Distributed LQR Consensus Control for Heterogeneous Multiagent Systems: Theory and Experiments," in IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 434443, 2018.
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##[4] Y. Li, H. Yang, Y. Yang, Y. Liu, and Y. Sun, “LQR-Based Optimal Leader-Following Consensus of Heterogeneous Multi-agent Systems,” Lecture Notes in Electrical Engineering Proceedings of 2019 Chinese Intelligent Systems Conference, pp. 122–130, 2019.
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##[5] R. Fotouhi and M. Pourgholi, "Discrete-time Inverse Optimal Control for Consensus of Multi-Agent Systems via a Novel Meta-Heuristic Algorithm," 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA), pp. 1-5, 2021.
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##[6] R. Fotouhi and M. Pourgholi, "Water Cycle Algorithm-Based Control for Optimal Consensus Problem," 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pp. 1-5, 2021.
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##[7] F. Chen and J. Chen, "Minimum-Energy Distributed Consensus Control of Multiagent Systems: A Network Approximation Approach," in IEEE Transactions on Automatic Control, vol. 65, no. 3, pp. 1144-1159, 2020.
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##[8] W. Ren and R. W. Beard, “Consensus seeking in multi-agent systems under dynamically changing interaction topologies,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 655–661, 2005.
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##[9] R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and Cooperation in Networked Multi-Agent Systems,” Proceedings of the IEEE, vol. 98, no. 7, pp. 1354–1355, 2010.
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##[10] R. V. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” International Journal of Industrial Engineering Computations, pp. 19–34, 2016.
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##[11] R.V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-Learning- based optimization: A novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
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##[12] H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi. "Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems." Computers Structures, Vol. 110, pp. 151-166, 2012.]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1
0
https://jcse.ir/article/267
Topology-Aware Graph Signal Sampling for Pooling in Graph Neural Networks
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As a generalization of convolutional neural networks to graph-structured data, graph convolutional networks learn feature embeddings based on the information of each node’s local neighborhood. However, due to the inherent irregularity of such data, extracting hierarchical representations of a graph becomes a challenging task. Several pooling approaches have been introduced to address this issue. In this paper, we propose a novel topology-aware graph signal sampling method to specify the nodes that represent the communities of a graph. Our method selects the sampling set based on the local variation of the signal of each node while considering vertex-domain distances of the nodes in the sampling set. In addition to the interpretability of the sampled nodes provided by our method, the experimental results both on stochastic block models and real-world dataset benchmarks show that our method achieves competitive results compared to the state-of-the-art in the graph classification task.
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62
Amirhossein
Nouranizadeh
Department of Computer Engineering
ایران
Mohammadjavad
Matinkia
Amirkabir University of Technology
ایران
Mohammad
Rahmati
Department of Computer Engineering
ایران
graph neural networks
pooling layer
graph signal sampling
graph classification
https://jcse.ir/ow_userfiles/plugins/base/attachments/623250a420447_623250a41e46c.pdf
[[1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779– 788.
##[2] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal processing magazine, vol. 29, no. 6, pp. 82–97, 2012.
##[3] M.-T. Luong, H. Pham, and C. D. Manning, “Effective ap- proaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025, 2015.
##[4] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al., “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144, 2016.
##[5] M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18–42, 2017.
##[6] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61–80, 2008.
##[7] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
##[8] W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in neural information processing systems, 2017, pp. 1024–1034.
##[9] P.Velicˇkovic ́, G. Cucurull, A.Casanova, A.Romero, P.Lio, and Y.Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017. [10] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph
##neural networks?” arXiv preprint arXiv:1810.00826, 2018.
##[11] Z.Wu, S.Pan, F.Chen, G.Long, C.Zhang, and P.S. Yu, “A comprehensive survey on graph neural networks,” arXiv preprint arXiv:1901.00596, 2019.
##[12] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains,” IEEE signal processing magazine, vol. 30, no. 3, pp. 83–98, 2013.
##[13] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017, pp. 1263–1272.
##[14] A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855– 864.
##[15] B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701–710
##[16] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th international conference on world wide web, 2015, pp. 1067–1077.
##[17] L. Backstrom and J. Leskovec, “Supervised random walks: predicting and recommending links in social networks,” in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 635–644.
##[18] R. v. d. Berg, T. N. Kipf, and M. Welling, “Graph convolutional matrix completion,” arXiv preprint arXiv:1706.02263, 2017.
##[19] Q. Lu and L. Getoor, “Link-based classification,” in Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003, pp. 496–503.
##[20] M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proceedings of the IEEE, vol. 104, no. 1, pp. 11–33, 2015.
##[21] P. D. Dobson and A. J. Doig, “Distinguishing enzyme structures from non-enzymes without alignments,” Journal of molecular biology, vol. 330, no. 4, pp. 771–783, 2003.
##[22] S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, “Graph kernels,” Journal of Machine Learning Research, vol. 11, no. Apr, pp. 1201–1242, 2010.
##[23] N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” Journal of Ma- chine Learning Research, vol. 12, no. Sep, pp. 2539–2561, 2011.
##[24] D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems, 2015, pp. 2224–2232.
##[25] M.Niepert, M.Ahmed, and K.Kutzkov, “Learning convolutional neural networks for graphs,” in International conference on machine learning, 2016, pp. 2014–2023.
##[26] S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” Journal of computer-aided molecular design, vol. 30, no. 8, pp. 595–608, 2016.
##[27] Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in Advances in neural information processing systems, 2018, pp. 4800– 4810.
##[28] J.Lee, I.Lee, and J.Kang, “Self-attention graph pooling,”arXivpreprint arXiv:1904.08082, 2019.
##[29] M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
##[30] O. Vinyals, S. Bengio, and M. Kudlur, “Order matters: Sequence to sequence for sets,” arXiv preprint arXiv:1511.06391, 2015.
##[31] H. Gao and S. Ji, “Graph u-nets,” arXiv preprint arXiv:1905.05178, 2019.
##[32] J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” arXiv preprint arXiv:1312.6203, 2013.
##[33] M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems, 2016, pp. 3844–3852.
##[34] R. Li, S. Wang, F. Zhu, and J. Huang, “Adaptive graph convolutional neural networks,” in Thirty-second AAAI conference on artificial intelligence, 2018.
##[35] M.Niepert, M.Ahmed, and K.Kutzkov, “Learning convolutionalneural networks for graphs,” in International conference on machine learning, 2016, pp. 2014–2023.
##[36] Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” arXiv preprint arXiv:1511.05493, 2015.
##[37] I. S. Dhillon, Y. Guan, and B. Kulis, “Weighted graph cuts without eigenvectors a multilevel approach,” IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 11, pp. 1944–1957, 2007.
##[38] H. Gao, Y. Liu, and S. Ji, “Topology-aware graph pooling networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
##[39] A. Anis, A. Gadde, and A. Ortega, “Efficient sampling set selection for bandlimited graph signals using graph spectral proxies,” IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3775–3789, 2016.
##[40] K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels” Bioinformatics, vol. 21, no. suppl_1, pp. i47–i56, 2005.
##[41] A.K.Debnath, R.L.Lopezde Compadre, G.Debnath, A.J.Shusterman, and C. Hansch, “Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,” Journal of medicinal chemistry, vol. 34,
##no. 2, pp. 786–797, 1991.
##[42] F. Orsini, P. Frasconi, and L. De Raedt, “Graph invariant kernels,” in Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
##[43] A.Paszke, S.Gross, S.Chintala, G.Chanan, E.Yang, Z.DeVito,Z.Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017.
##[44] M. Fey and J. E. Lenssen, “Fast graph representation learning with pytorch geometric,” arXiv preprint arXiv:1903.02428, 2019.
##]
Computer Society of Iran
Journal on Computer Science and Engineering (JCSE)
18
1