In this paper, we discuss about the population balance equations which are described by the following differential equation model [4? ? ? ]

dy (x)

---------- + (1 + κxm) y (x) = 2m+1κxmy (2x) , 0 ≤ x ≤ b, (1)

dx

where κ, b and m are constant. If the binary equal breakage is assumed then the value of m is assumed to be 4 and the initial condition y (x) is y (0) = 1. (2)

As we know, there is no exact solution for this equation [4? ? ], so we have to solve it by approximation methods. Several numerical schemes developed to solve the population balance differential equation, such as Block-pulse method [? ], weighted residual method [? ], the method of orthogonal series expansion and wavelet Galerkin method [4] and rationalized Haar functions [? ]. In this paper, we discuss on the application of auto-correlation functions of Daubechies wavelets to solve the population balance differential equation (1), and compare with some of above methods.

Population balance differential equation, Daubechies wavelet, auto-correlation functions, Lagrange interpolation

[1] Alipanah, A. and Dehghan, M. [2008], ‘Solution of population balance equa- tions via rationalized haar functions’, Kybernetes 37, 1189–1196.

[2] Beylkin, G. [n.d.], ‘On the representation of operators in bases of compactly supported wavelets’, SIAM J. Numer. Anal. 121.

[3] Casazza, P. G. and Kutyniok, G. [2012], Finite Frame: Theory and Applica- tions, Brichauer, Buston.

[4] M.Q. Chen, C. H. and Shih, Y. [n.d.], ‘A wavelet-galerkin method for solving population balance equations’, Computers Chem. Engng. 20.

[5] R. Ansari, C. G. and Kaiser, J. [n.d.], ‘Wavelet construction using lagrange halfband filters’, IEEE Trans. Circuits Syst. 38.

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In IPTV systems, thousands of live TV channels and video contents are available to subscribers. To benefit from the rich set of contents, users need to be able to rapidly and easily find

what they are actually interested in. The integration of a recommender system into the IPTV infrastructure improves the user experience by providing an effective way of browsing for interesting

programs and movies. But to improve the quality of recommendation, a clear understanding of access pattern to the items is necessary. However, for security reasons, in IPTV systems this kind of information is not publicly available. In this paper, we try to generate a so-called artificial dataset based on a model which mimics the behavior of a typical IPTV user, and with the aid of MovieLens dataset. We then show a typical application of the dataset in recommender systems.

IPTV; user behavior; synthetic dataset; recommender systems

[1] Technical report, Cisco visual networking index: Forecast and methodology. 2013-2018

[2] R. Bambini, P. Cremonesi, and R. Turrin “A recommender system for an IPTV service provider: a real large-scale production environment,” in Recommender systems handbook, Springer, pp 299- 331, 2011.

[3] H.J. Kwon, K.S. Hong, “Personalized electronic program guide for IPTV based on collaborative filtering with novel similarity method,” In Consumer Electronics (ICCE), IEEE International Conference on: IEEE. pp 467-468, 2011.

[4] M. Krstic, M. Bjelica, “Context-aware personalized program guide based on neural network,” IEEE Transactions on Consumer Electronics. 58(4): pp 1301-1306, 2012.

[5] A.A. Beyragh, A.G. Rahbar, “IFCS: an intelligent fast channel switching in IPTV over PON based on human behavior prediction,” Multimedia tools and applications. 72(2): pp 1049-1071, 2014.

[6] S. Song, H. Moustafa, and H. Afifi, “Advanced IPTV services personalization through context- aware content recommendation,” IEEE Transactions on Multimedia. 14(6): pp 1528-1537,

2012.

[7] A.M. Elmisery, S. Rho, and D. Botvich, “Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender service,” Multimedia tools and applications: pp 1-31, 2014

[8] MovieLens dataset. http://www.grouplens.org/data.

[9] A. Abdollahpouri, “QoS-Aware Live IPTV Streaming Over Wireless Multi-hop Networks,” Shaker- Verlag, Aachen, 2012.

[10] J. Cong, B.E. Wolfinger, “A unified load generator based on formal load specification and load transformation,” In Proceedings of the 1st international conference on Performance evaluation

methodolgies and tools. ACM, 2006

[11] A. Abdollahpouri, B.E. Wolfinger, J. Lai, and of C. Vinti, “Elaboration and formal description of 43 IPTV user models and their application to IPTV system analysis,” MMBnet, 2011.

[12] Y.J. Park, A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” In Proceedings of the ACM conference on Recommender systems. pp 11-18, 2008

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Mobile Ad-hoc Networks (MANETs) have no clear line of defense; and therefore, beside legitimate network nodes, they are also accessible by malicious nodes. Traditional ways of protecting the

network (such as firewalls) are not sufficient and effective. Therefore, intrusion detection systems (IDS) are required to monitor the network and detect the misbehavior and anomalies. Intrusion detection is the act of detecting actions that attempt to compromise the security goals. Intrusion detection systems encounter challenges such as misdetection, misjudgment, and slow response to the attack. In recent years, several data mining techniques as classification, clustering, and association rule discovery are being used for this purpose. In this paper, we propose a hybrid technique that combines data mining approaches like K-Means clustering algorithm and AFS theory as a feature selection module. The main purpose of the proposed technique is to decrease the number of attributes associated with each data point. The proposed technique performs better in terms of detection rate and accuracy when applied to KDD CUP 99 dataset in comparison with other intrusion detection systems in the detection of DoS and Probe attacks.

Intrusion Detection System (IDS), K-means clustering, AFS theory

[1] H.C. Jang, Y.N. Lien, T.C. Tsai, Rescue information system for earthquake disasters based on MANET emergency communication platform, in: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Comput32 ing: Connecting the World Wirelessly, ACM, 2009, pp. 623–627.

[2] A. Vasiliou, A.A. Economides, MANETs for environmental monitoring, in: IEEE International Telecommunications Symposium, 2006, pp. 813–818.

[3] B.C. Seet, G. Liu, B.S. Lee, C.H. Foh, K.J. Wong, K.K. Lee, A-STAR: a mobile ad hoc routing strategy for metropolis vehicular communications, in: NETWORKING 2004. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications, Springer, 2004, pp. 989–999.

[4] R. Sekar, A. Gupta, T. Shanbhag, J. Frullo, A. Tiwari, H. Yang, S. Zhou, Specification-based anomaly detection: A new approach for detecting network intrusions, in: Proceedings of ACM conference on computer and communication security, 2002, pp. 265–274.

[5] N. Wu, J. Zhang, Factor-analysis based anomaly detection and clustering, Decision Support Systems. 42 (2006) 375–389.

[6] N. Ye, Ehiabor. T, Y. Zhang, First-order versus high-order stochastic models for computer intrusion detection, Quality and Reliability Engineering International. 18 (2002) 243–250.

[7] X. Li, N. Ye, A supervised clustering and classification algorithm for mining data with mixed variables, IEEE Transactions On Systems, Man, and Cybernetics. 36 (2006) 396–406.

[8] Y. Liu, K. Chen, X. Liao, W. Zhang, A genetic clustering method for intrusion detection, Pattern Recognition. 37 (2004) 927–942.

[9] Z. Zhang, H. Shen, Application of online-training SVMs for real-time intrusion detection with different considerations, Computer Communications. 28 (2005) 1428–1442.

[10] N. Ye, Q. Chen, An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems, Quality and Reliability Engineering International. 17 (2001) 105–112.

[11] S.Y. Jiang, X. Song, H. Wang, J.J. Han, Q.H. Li, A clustering-based method for unsupervised intrusion detections, Pattern Recognition Letters. 27 (2006) 802–810.

[12] S.H. Oh, W.S. Lee, An anomaly intrusion detection method by clustering normal user behavior. Computers and Security. 22 (2003) 596–612.

[13] W.H. Chen, S.H. Hsu, H.P. Shen, Application of SVM and ANN for intrusion detection, Computers and Operations Research. 32 (2005) 2617–2634.

[14] S. Pastrana, A. Mitrokotsa, A. Orfila, P. Peris-Lopez, Evaluation of classification algorithms for intrusion detection in MANETs, Knowledge-Based Systems, 36 (2012) 217–225.

[15] R. Bace, P. Mell, NIST Special Publication on Intrusion Detection Systems, Technical Report, National Institute of Standards and Technologies, 2001.

[16] LI Yongzhong,YANG Ge,XU Jing Zhao Bo, A new intrusion detection method based on Fuzzy HMM, IEEE, (2008) Volume 2, Issue 8.

[17] Li Tian, Research on Network Intrusion Detection System Based on Improved K-means Clustering Algorithm, Computer Science-Technology and Applications, 2009. IFCSTA ‘09. International Forum

[18] P. Dabas, R. Chaudhary, Survey of Network Intrusion Detection Using K-Mean Algorithm, in: International Journal of Advanced Research in Computer Science and Software Engineering, 2013, pp. 507-511.

[19] M.M. Solanki, M.V. Dhamdhere, Intrusion Detection System by using K-Means clustering, C 4.5, FNN, SVM classifier, in: International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2014, pp. 19-23.

[20] M. Govindarajan, R.M. Chandrasekaran, Intrusion detection using neural based hybrid classification methods, Computer Networks. 55 (2011)

[21] H. Zhang, Y. Jiang, The Improved K-means Algorithm in Intrusion Detection System Research, Advanced Engineering Forum, 2011, pp. 204-208.

[22] KDD CUP 1999 dataset (1999). <http://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data> (accessed March 2009).

[23] X. Liu, W. Wang, T. Chai, The fuzzy clustering analysis based on AFS theory, IEEE Trans. Syst. Man Cybern B: Cybern. 35 (5) (2005) 1013–1027.

[24] X. Liu, The fuzzy theory based on AFS algebras and AFS structure, J. Math. Anal. Appl. 217 (2) (1998) 459–478.

[25] X. Liu, The fuzzy sets and systems based on AFS structure, EI algebra and EII algebra, Fuzzy Sets Syst. 95 (2) (1998) 179–188.

[26] X. Liu, T. Chai, W. Wang, W. Liu, Approaches to the representations and logic operations of fuzzy concepts in the framework of axiomatic fuzzy set theory I,Inf. Sci. 177 (4) (2007) 1007–1026.

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In this paper, the author has defined a new class of transitive sets and mixing sets called D-transitive sets and called D-mixing sets respectively and he has proved that every weakly D-mixing subset in a differential manifold M implies D-transitive subset of M but not conversely, and that these new definitions are all preserved under Dconjugation. Finally, we illustrate by theorems the relevant topologically D-transitive and D-chaotic behavior by some maps defined on a differential manifold and relationship between the new and basic definitions of transitive sets are given.

D-transitive sets, weakly D-mixing chaos, D-Conjugation and topological manifold.

[1] Antoni A. Kosinski, [1993], Differential manifolds, Volume 138 in Pure and Applied Mathematics.

[2] Mohammed N. Murad, [2014], New types of chaos and non-wandering points in topological spaces, Pure and Applied Mathematics Journal, SciencePG.

[3] A.W. Brown, [2010], Nonexpanding Attractors: Conjugacy to Algebraic Models and Classification in 3-Manifolds, J. Mod. Dyn. V. 4, No. 3, 517548.

[4] A. Flavio, & C. Sylvain, [2011], Transitivity and topological mixing for C1 diffeomorphisms. Essays in Mathematics and its Applications: In Honor of Stephen Smale’s 80th Birthday.

[5] https://en.wikipedia.org/wiki/Generic_property

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In the world of networking and data communication, a data center environment monitoring and cooling is critical and important point for reliably operating servers and networking devices. Data center monitoring and cooling also have roles in minimizing required cooling energy. In this paper, designing and implementing of data center real-time temperature monitoring and cooling system is presented. The system mainly depends on Arduino mi-crocontroller board that is directly connected to GSM shield and temperature sensor. It is used as a base for processing received temperature data from a temperature sensor as input in one hand, and on the other hand it is to sends signal to switch on attached cooling system and alert message to the network Administrators mobile phone as output. Normally, the system monitors data centers temperature and saves these data to the attached server. If the temperature value reached a predefined threshold, which is supposed to be 25c, the system switches on the attached cooling system and sends an alert message to notify the network Administrator at the same time. The system also allows authorized users to monitor data centers temperature through website through local network or Internet.

Arduino Microcontroller; Max6675 Sensor; GSM Module; Data Center Monitoring; Network Access

[1] Curtis, L. [2008], Environmentally Sustainable Infrastructure Design, The Architecture Journal Microsoft.

[2] T. Wellem and B. Setiawan,[2012],A Microcontroller-based Room Tempera-ture Monitoring System, International Journal of Computer Applications, vol. 53.

[3] Solorio, Rigoberto, [2008],”A Web-Based Temperature Monitoring System for the College of Arts and Letters”, Electronic Theses, Projects, and Dissertations. Paper 129.

[4] Emily G. and Patrick D. J.,[2012],Environmental Monitoring with Arduino.

[5] Santoso Budijono, Jeffri Andrianto, Muhammad Axis Novradin Noor,[2014],Design and implementation of modular home security sys-tem with short messaging system, EPJ Web of Conferences 68, 00025, published by EDP Sciences.

[6] Michael G. Rodriguez; Luis E. Ortiz Uriarte; Yi Jia; Kazutomo Yoshii; Robert Ross; Peter H.,[2011], Wireless sensor network for data-center environmental monitoring, Proceedings of 2011 Fifth International Conference on the Sensing Technology (ICST).

[7] Sharmila Borah,[2013],Temperature Monitoring of Server Room Using Matlab and Arduino, International Journal of Engineering Research and Technology (IJERT), ISSN: 2278-0181, Vol. 2

Issue 9.

[8] Vaibhav M. Davande, Pradeep C. Dhanawade, Vinayak B. Sutar,[2016],Real Time Temperature Monitoring Using LABVIEW and Arduino, International Journal of Innovative Research in Computer and Communication Engineer-ing, Vol. 4, Issue 3.

[9] Rahul Antony, Reema Mathew A, Divya K, Teenu Jose,[2015],Multi Security System Using GSM and PIC 16F877A, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 5, Issue 3.

[10] Sanchit Patil, Vivek Shah, Saptarshi Patnaik,[2015],Implementation of Wire-less Sensor Networks Using ZigBee, International Journal for Research in Applied Science and Engineering Technology (IJRASET), ISSN: 2321-9653, Volume 3 Issue XI.

[11] Ahmed Chalak Shakir and Najmadin Wahid Boskany,[2015],Design and Implementation of Counter System using Arduino Wireless Motion Sensor, International Journal of Advanced Scientific and Technical Research Issue 5 volume 3, Available online on http://www.rspublication.com/ijst/index.html ISSN 2249-9954.

[12] H. Muhammad Asraf, K.A. Nur Dalila, A.W. Muhammad Hakim and R.H. Muhammad Faizzuan Hon,[2017],Development of Experimental Simulator via Arduino-based PID Temperature Control System using LabVIEW, E-ISSN: 2289-8131 Vol. 9 No. 1-5.

[13] Pooja and Prince,[2016] GSM based Smart Water Purifier, International Jour-nal of Innovations in Engineering and Technology (IJIET).

[14] Najmadin Wahid Boskany and Ranjdar M. Abdullah,[2016],Intelligent Anti-Theft Car Security System based on Arduino and GSM Network, International Journal of Multidisciplinary and Current

Research, ISSN: 2321-3124, Avail-able online 23 June 2016, Vol.4 (May/June 2016 issue), Research Article Available at: http://ijmcr.com.

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Superiorization is an iterative method for constrained optimiza- tion. It is used for improving the efficacy of an iterative method whose con- vergence is resilient to certain kinds of perturbations. Such perturbations are designed to force the perturbed algorithm to produce more useful results for the intended application than the ones that are produced by the original itera- tive algorithm. The perturbed algorithm is called the superiorized version of the original unperturbed algorithm. Superiorization is a great way in iterative algorithms to improve the convergence rate and control the input noise of the algorithm process.

Nonexpansive mapping; Minimization over fixed point; Bounded perturbation resilience;

Superiorization.

[1] Censor Y, Davidi R and Herman G T 2010 Perturbation resilience and superiorization of iterative algorithms Inverse Problems 26 (2010), 065008.

[2] G.T. Herman, Fundamentals of Computerized Tomography: Image Reconstruction from Projections, Springer-Verlag, London, UK, 2nd Edition, (2009).

[3] E.S. Helou, M.V.W. Zibetti and E.X. Miqueles, Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction, Inverse Problems, Vol. 33 (2017), 044010.

[4] Q. Yang, W. Cong and G. Wang, Superiorization-based multi-energy CT image reconstruction, Inverse Problems, Vol. 33 (2017), 044014.

[5] S. Luo and T. Zhou, Superiorization of EM algorithm and its application in single-photon emission computed tomography (SPECT), Inverse Problems and Imaging, Vol. 8, pp. 223-246, (2014).

[6] R. Davidi, Y. Censor, R.W. Schulte, S. Geneser and L. Xing, Feasibilityseeking and superiorization algorithms applied to inverse treatment planning inradiationtherapy,ContemporaryMathematics, Vol.636,pp.83-92,(2015).

[7] E. Bonacker, A. Gibali, K-H. Kfer and P. Sss, Speedup of lexicographic optimization by superiorization and its applications to cancer radiotherapy treatment, Inverse Problems, Vol. 33 (2017).

[8] Superiorization: Theory and Applications, Special Issue of the journal Inverse Problems, Volume 33, Number 4, April 2017.

[9] H. He, H. Xu, Perturbation resilience and superiorization methodology of averaged mappings, Inverse Problems 33 (2017), 044007.

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