Circumferences of 3-connected claw-free graphs, II

For a graph H , the circumference of H , denoted by c ( H ) , is the length of a longest cycle in H . It is proved in Chen (2016) that if H is a 3-connected claw-free graph of order n with δ ≥ 8 , then c ( H ) ≥ min { 9 δ − 3 , n } . In Li (2006), Li conjectured that every 3-connected k -regular claw-free graph H of order n has c ( H ) ≥ min { 10 k − 4 , n } . Later, Li posed an open problem in Li (2008): how long is the best possible circumference for a 3-connected regular claw-free graph? In this paper, we study the circumference of 3-connected claw-free graphs without the restriction on regularity and provide a solution to the conjecture and the open problem above. We determine five families F i ( 1 ≤ i ≤ 5 ) of 3-connected claw-free graphs which are characterized by graphs contractible to the Petersen graph and show that if H is a 3-connected claw-free graph of order n with δ ≥ 16 , then one of the following holds: (a) either c ( H ) ≥ min { 10 δ − 3 , n } or H ∈ F 1 . (b) either c ( H ) ≥ min { 11 δ − 7 , n } or H ∈ F 1 ∪ F 2 . (c) either c ( H ) ≥ min { 11 δ − 3 , n } or H ∈ F 1 ∪ F 2 ∪ F 3 . (d) either c ( H ) ≥ min { 12 δ − 10 , n } or H ∈ F 1 ∪ F 2 ∪ F 3 ∪ F 4 . (e) if δ ≥ 23 then either c ( H ) ≥ min { 12 δ − 7 , n } or H ∈ F 1 ∪ F 2 ∪ F 3 ∪ F 4 ∪ F 5 . This is also an improvement of the prior results in Chen (2016), Lai et al. (2016), Li et al. (2009) and Mathews and Sumner (1985)

A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

[EN] This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the vehicles are not required to return to the depot after completing a service. In this new problem, the demands of customers are fulfilled by a heterogeneous fixed fleet of vehicles having various capacities, fixed costs and variable costs. This problem is an important variant of the open vehicle routing problem (OVRP) and can cover more practical situations in transportation and logistics. Since this problem belongs to NP-hard Problems, An approach based on column generation (CG) is applied to solve the HFFOVRP. A tight integer programming model is presented and the linear programming relaxation of which is solved by the CG technique. Since there have been no existing benchmarks, this study generated 19 test problems and the results of the proposed CG algorithm is compared to the results of exact algorithm. Computational experience confirms that the proposed algorithm can provide better solutions within a comparatively shorter period of time.Yousefikhoshbakht, M.; Dolatnejad, A. (2017). A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem. International Journal of Production Management and Engineering. 5(2):55-71. doi:10.4995/ijpme.2017.5916SWORD557152Aleman, R. E., & Hill, R. R. (2010). A tabu search with vocabulary building approach for the vehicle routing problem with split demands. International Journal of Metaheuristics, 1(1), 55. doi:10.1504/ijmheur.2010.033123Anbuudayasankar, S. P., Ganesh, K., Lenny Koh, S. C., & Ducq, Y. (2012). Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications, 39(3), 2296-2305. doi:10.1016/j.eswa.2011.08.009Brandão, J. (2009). A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. European Journal of Operational Research, 195(3), 716-728. doi:10.1016/j.ejor.2007.05.059Çatay, B. (2010). A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Expert Systems with Applications, 37(10), 6809-6817. doi:10.1016/j.eswa.2010.03.045Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91. doi:10.1287/mnsc.6.1.80Gendreau, M., Guertin, F., Potvin, J.-Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157-174. doi:10.1016/j.trc.2006.03.002Gendreau, M., Laporte, G., Musaraganyi, C., & Taillard, É. D. (1999). A tabu search heuristic for the heterogeneous fleet vehicle routing problem. Computers & Operations Research, 26(12), 1153-1173. doi:10.1016/s0305-0548(98)00100-2Lei, H., Laporte, G., & Guo, B. (2011). The capacitated vehicle routing problem with stochastic demands and time windows. Computers & Operations Research, 38(12), 1775-1783. doi:10.1016/j.cor.2011.02.007Li, X., Leung, S. C. H., & Tian, P. (2012). A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365-374. doi:10.1016/j.eswa.2011.07.025Li, X., Tian, P., & Aneja, Y. P. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 46(6), 1111-1127. doi:10.1016/j.tre.2010.02.004Penna, P. H. V., Subramanian, A., & Ochi, L. S. (2011). An Iterated Local Search heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2), 201-232. doi:10.1007/s10732-011-9186-ySaadati Eskandari, Z., YousefiKhoshbakht, M. (2012). Solving the Vehicle Routing Problem by an Effective Reactive Bone Route Algorithm, Transportation Research Journal, 1(2), 51-69.Subramanian, A., Drummond, L. M. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Computers & Operations Research, 37(11), 1899-1911. doi:10.1016/j.cor.2009.10.011Syslo, M., Deo, N., Kowalik, J. (1983). Discrete Optimization Algorithms with Pascal Programs, Prentice Hall.Taillard, E. D. (1999). A heuristic column generation method for the heterogeneous fleet VRP, RAIRO Operations Research, 33, 1-14. https://doi.org/10.1051/ro:1999101Tarantilis, C. D., & Kiranoudis, C. T. (2007). A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research, 179(3), 806-822. doi:10.1016/j.ejor.2005.03.059Wang, H.-F., & Chen, Y.-Y. (2012). A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering, 62(1), 84-95. doi:10.1016/j.cie.2011.08.018Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9), 2565-2575. doi:10.1080/00207543.2013.855337Yousefikhoshbakht, M., & Khorram, E. (2012). Solving the vehicle routing problem by a hybrid meta-heuristic algorithm. Journal of Industrial Engineering International, 8(1). doi:10.1186/2251-712x-8-1

Computer simulations of VANETs using realistic city topologies

Researchers in vehicular ad hoc networks (VANETs) commonly use simulation to test new algorithms and techniques. This is the case because of the high cost and labor involved in deploying and testing vehicles in real outdoor scenarios. However, when determining the factors that should be taken into account in these simulations, some factors such as realistic road topologies and presence of obstacles are rarely addressed. In this paper, we first evaluate the packet error rate (PER) through actual measurements in an outdoor road scenario, and deduce a close model of the PER for VANETs. Secondly, we introduce a topology-based visibility scheme such that road dimension and geometry can be accounted for, in addition to line-of-sight. We then combine these factors to determine when warning messages (i.e., messages that warn drivers of danger and hazards) are successfully received in a VANET. Through extensive simulations using different road topologies, city maps, and visibility schemes, we show these factors can impact warning message dissemination time and packet delivery rate.This work was partially supported by the Ministerio de Educacion y Ciencia, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador".Martínez, FJ.; Fogue, M.; Toh, C.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Computer simulations of VANETs using realistic city topologies. Wireless Personal Communications. 69(2):639-663. https://doi.org/10.1007/s11277-012-0594-6S639663692Martinez F. J., Toh C.-K., Cano J.-C., Calafate C. T., Manzoni P. (2011) A survey and comparative study of simulators for vehicular ad hoc networks (VANETs). Wireless Communications and Mobile Computing Journal 11(7): 813–828Toh C.-K. (2001) Ad hoc mobile wireless networks: Protocols and systems. Prentice Hall, Englewood Cliffs, NJIEEE 802.11 Working Group. (2010). IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 6: Wireless Access in Vehicular Environments.Sommer, C., Eckhoff, D., German, R., & Dressler F. (2011). A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments. In Eighth international conference on wireless on-demand network systems and services (WONS), pp. 84–90.Bohm, A., Lidstrom, K., Jonsson, M., & Larsson, T. (2010). Evaluating CALM M5-based vehicle-to-vehicle communication in various road settings through field trials. In Proceedings of the 35th IEEE conference on local computer networks (LCN’10), Denver, Colorado, USA, pp. 613–620.Martinez, F. J., Fogue, M., Coll, M., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2010). Assessing the impact of a realistic radio propagation model on VANET scenarios using real maps. In 9th IEEE international symposium on network computing and applications (NCA), Boston, USA, pp. 132–139.Fall, K., & Varadhan, K. (2000). “ns notes and documents,” The VINT project. UC Berkeley, LBL, USC/ISI, and Xerox PARC, February 2000. Available at http://www.isi.edu/nsnam/ns/ns-documentation.html .Marinoni, S., & Kari, H. H. (2006). Ad hoc routing protocol performance in a realistic environment. In Proceedings of the international conference on networking, international conference on systems and international conference on mobile communications and learning technologies (ICN/ICONS/MCL 2006), Washington, DC, USA.Mahajan, A., Potnis, N., Gopalan, K., & Wang, A. (2007). Modeling VANET deployment in urban settings. In International workshop on modeling analysis and simulation of wireless and mobile systems (MSWiM 2007), Crete Island, Greece.Suriyapaiboonwattana, K., Pornavalai, C., & Chakraborty, G. (2009). An adaptive alert message dissemination protocol for VANET to improve road safety. In IEEE intlernational conference on fuzzy systems, 2009. FUZZ-IEEE 2009, pp. 1639–1644.Bako, B., Schoch, E., Kargl, F., & Weber, M. (2008). Optimized position based gossiping in VANETs. In Vehicular technology conference, 2008. VTC 2008-Fall. IEEE 68th, pp. 1–5.Martinez, F. J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2008). Citymob: A mobility model pattern generator for VANETs. In IEEE vehicular networks and applications workshop (Vehi-Mobi, held with ICC), Beijing, China.Torrent-Moreno, M., Santi, P., & Hartenstein, H. (2007). Inter-vehicle communications: Assessing information dissemination under safety constraints. In Proceedings of the 4th annual conference on wireless on demand network systems and services (WONS), Oberguyrgl, Austria.Martinez, F. J., Toh, C.-K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2009). Realistic radio propagation models (RPMs) for VANET simulations. In IEEE wireless communications and networking conference (WCNC), Budapest, Hungary.Martinez, F. J., Toh, C.-K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2010). A street broadcast reduction scheme (SBR) to mitigate the broadcast storm problem in VANETs. Wireless personal communications, pp. 1–14. doi: 10.1007/s11277-010-9989-4Ni, S.-Y., Tseng, Y.-C., Chen, Y.-S., & Sheu, J.-P. (1999). The broadcast storm problem in a mobile ad hoc network. In ACM/IEEE international conference on mobile computing and networking (MobiCom 1999), Seattle Washington.Krajzewicz, D., & Rossel, C. (2007). “Simulation of urban mobility (SUMO),” Centre for Applied Informatics (ZAIK) and the Institute of Transport Research at the German Aerospace Centre. Available at http://sumo.sourceforge.net/index.shtml .OpenStreetMap Team. (2009). OpenStreetMap, collaborative project to create a free editable map of the world. Available at http://www.openstreetmap.org .U.S. Census Bureau. (2009). TIGER, topologically integrated geographic encoding and referencing. Available at http://www.census.gov/geo/www/tiger .Krauss S., Wagner P., Gawron C. (1997) Metastable states in a microscopic model of traffic flow. Physical Review E 55(5): 5597–5602Krajzewicz, D., Hertkorn, G., Rossel, C., & Wagner, P. (2002). SUMO (Simulation of Urban MObility)—An open-source traffic simulation. In Proceedings of the 4th middle east symposium on simulation and modelling (MESM2002), Sharjah, United Arab Emirates, pp. 183–187

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. Prague Bull Math Linguist 100:101–112Alabau V, Rodríguez-Ruiz L, Sanchis A, Martínez-Gómez P, Casacuberta F (2011) On multimodal interactive machine translation using speech recognition. In: Proceedings of the International Conference on Multimodal Interaction, pp 129–136Alabau V, Sanchis A, Casacuberta F (2014) Improving on-line handwritten recognition in interactive machine translation. Pattern Recognit 47(3):1217–1228Apostolico A, Guerra C (1987) The longest common subsequence problem revisited. Algorithmica 2:315–336Azadi F, Khadivi S (2015) Improved search strategy for interactive machine translation in computer assisted translation. In: Proceedings of Machine Translation Summit XV, pp 319–332Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations. arXiv:1409.0473Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E, Vilar J-M (2009) Statistical approaches to computer-assisted translation. Comput Linguist 35:3–28Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311Chen SF, Goodman J (1996) An empirical study of smoothing techniques for language modeling. In: Proceedings of the Annual Meeting on Association for Computational Linguistics, pp 310–318Cheng S, Huang S, Chen H, Dai X, Chen J (2016) PRIMT: a pick-revise framework for interactive machine translation. In: Proceedings of the North American Chapter of the Association for Computational Linguistics, pp 1240–1249Dale R (2016) How to make money in the translation business. Nat Lang Eng 22(2):321–325Domingo M, Peris, Á, Casacuberta F (2016) Interactive-predictive translation based on multiple word-segments. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp 282–291Federico M, Bentivogli L, Paul M, Stüker S (2011) Overview of the IWSLT 2011 evaluation campaign. In: International Workshop on Spoken Language Translation, pp 11–27Foster G, Isabelle P, Plamondon P (1997) Target-text mediated interactive machine translation. Mach Transl 12:175–194González-Rubio J, Benedí J-M, Casacuberta F (2016) Beyond prefix-based interactive translation prediction. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning, pp 198–207González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) On the use of confidence measures within an interactive-predictive machine translation system. 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In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp 48–54Koehn P, Tsoukala C, Saint-Amand H (2014) Refinements to interactive translation prediction based on search graphs. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 574–578Marie B, Max A (2015) Touch-based pre-post-editing of machine translation output. In: Proceedings of the conference on empirical methods in natural language processing, pp 1040–1045Nepveu L, Lapalme G, Langlais P, Foster G (2004) Adaptive language and translation models for interactive machine translation. In: Proceedings of the conference on empirical method in natural language processing, pp 190–197Nielsen J (1993) Usability engineering. Morgan Kaufmann Publishers Inc, BurlingtonOch F J (2003) Minimum error rate training in statistical machine translation. 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Open innovation: past, present and future trends

[EN] Purpose The purpose of this paper is to provide interested parties with the means of grasping how the literature on open innovation has evolved over the course of time. In this way, the authors furthermore contribute towards a better understanding, scaling and positioning of this field of research. Design/methodology/approach This study applies a combination of bibliometric techniques, such as citations, co-citations and social network analysis in order to map the scientific domain of open innovation. Currently, bibliometric analysis represents a methodology in effect on a global scale to evaluate the existing state of fields of research (Mutschke et al., 2011). This spans the application of quantitative and statistical analysis to publications such as articles and their respective citations and serving to evaluate the performance of research through returning data on all of the activities ongoing in a scientific field with summaries of these data generating a broad perspective on the research activities and impacts, especially as regards the researchers, journals, countries and universities (Hawkins, 1977; Osareh, 1996; Thomsom Reuters, 2008). Findings This research aims to map and analyse the intellectual knowledge held on open innovation. To this end, the authors carried out a bibliometric study with recourse to co-citations. Based on cluster and factorial analyses, it is possible identify and classify the several theoretical perspectives on open innovation across six areas: open innovation concept, open innovation and networks, open innovation and knowledge, open Innovation, and innovation spillovers, open innovation management and open innovation and technology.JoAo J. Ferreira and Cristina I. Fernandes acknowledge the financial support from NECE - Research Unit in Business Sciences funded by the Multiannual Funding Programme of R&D Centres of FCT - Fundacao para a Ciencia e a Tecnologia (Project UID/GES/04630/2013).Fernandes, C.; Ferreira, J.; Peris-Ortiz, M. (2019). Open innovation: past, present and future trends. Journal of Organizational Change Management. 32(5):578-602. https://doi.org/10.1108/JOCM-09-2018-0257S578602325Ahn, J. M., Minshall, T., & Mortara, L. (2017). Understanding the human side of openness: the fit between open innovation modes and CEO characteristics. R&D Management, 47(5), 727-740. doi:10.1111/radm.12264Alexy, O., George, G., & Salter, A. J. (2013). Cui Bono? The Selective Revealing of Knowledge and Its Implications for Innovative Activity. Academy of Management Review, 38(2), 270-291. doi:10.5465/amr.2011.0193Baldwin, C., & von Hippel, E. (2011). Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation. Organization Science, 22(6), 1399-1417. doi:10.1287/orsc.1100.0618Ballell, L., Bates, R. H., Young, R. J., Alvarez-Gomez, D., Alvarez-Ruiz, E., Barroso, V., … Cammack, N. (2013). Fueling Open-Source Drug Discovery: 177 Small-Molecule Leads against Tuberculosis. ChemMedChem, 8(2), 313-321. doi:10.1002/cmdc.201200428Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120. doi:10.1177/014920639101700108Belussi, F., Sammarra, A., & Sedita, S. R. (2010). Learning at the boundaries in an «Open Regional Innovation System»: A focus on firms’ innovation strategies in the Emilia Romagna life science industry. Research Policy, 39(6), 710-721. doi:10.1016/j.respol.2010.01.014Berchicci, L. (2013). Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative performance. Research Policy, 42(1), 117-127. doi:10.1016/j.respol.2012.04.017Berkhout, G., Hartmann, D., & Trott, P. (2010). Connecting technological capabilities with market needs using a cyclic innovation model. R&D Management, 40(5), 474-490. doi:10.1111/j.1467-9310.2010.00618.xBerthon, P., Ewing, M. T., & Napoli, J. (2008). Brand Management in Small to Medium-Sized Enterprises*. Journal of Small Business Management, 46(1), 27-45. doi:10.1111/j.1540-627x.2007.00229.xBianchi, M., Campodall’Orto, S., Frattini, F., & Vercesi, P. (2010). Enabling open innovation in small- and medium-sized enterprises: how to find alternative applications for your technologies. R&D Management, 40(4), 414-431. doi:10.1111/j.1467-9310.2010.00613.xChen, J., Chen, Y., & Vanhaverbeke, W. (2011). The influence of scope, depth, and orientation of external technology sources on the innovative performance of Chinese firms. Technovation, 31(8), 362-373. doi:10.1016/j.technovation.2011.03.002Cheng, C.-F., Lai, M.-K., & Wu, W.-Y. (2010). Exploring the impact of innovation strategy on R&D employees’ job satisfaction: A mathematical model and empirical research. Technovation, 30(7-8), 459-470. doi:10.1016/j.technovation.2010.03.006Chesbrough, H. and Bogers, M. (2014), “Explicating open innovation: clarifying an emerging paradigm for understanding innovation”, in Chesbrough, H., Vanhaverbeke, W. and West, J. (Eds), New Frontiers in Open Innovation, Oxford University Press, Oxford, pp. 3-28.Chesbrough, H. (2012). Open Innovation: Where We’ve Been and Where We’re Going. Research-Technology Management, 55(4), 20-27. doi:10.5437/08956308x5504085Chesbrough, H. W., & Appleyard, M. M. (2007). Open Innovation and Strategy. California Management Review, 50(1), 57-76. doi:10.2307/41166416Chiaroni, D., Chiesa, V., & Frattini, F. (2011). The Open Innovation Journey: How firms dynamically implement the emerging innovation management paradigm. Technovation, 31(1), 34-43. doi:10.1016/j.technovation.2009.08.007Christensen, J. F., Olesen, M. H., & Kjær, J. S. (2005). The industrial dynamics of Open Innovation—Evidence from the transformation of consumer electronics. Research Policy, 34(10), 1533-1549. doi:10.1016/j.respol.2005.07.002Cooke, P. (2005). Regionally asymmetric knowledge capabilities and open innovation. Research Policy, 34(8), 1128-1149. doi:10.1016/j.respol.2004.12.005Cooper, R. G. (2008). Perspective: The Stage-Gate®Idea-to-Launch Process—Update, What’s New, and NexGen Systems. Journal of Product Innovation Management, 25(3), 213-232. doi:10.1111/j.1540-5885.2008.00296.xDahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699-709. doi:10.1016/j.respol.2010.01.013Dahlander, L., O’Mahony, S., & Gann, D. M. (2014). One foot in, one foot out: how does individuals’ external search breadth affect innovation outcomes? Strategic Management Journal, 37(2), 280-302. doi:10.1002/smj.2342Di Gangi, P. M., & Wasko, M. (2009). Steal my idea! Organizational adoption of user innovations from a user innovation community: A case study of Dell IdeaStorm. Decision Support Systems, 48(1), 303-312. doi:10.1016/j.dss.2009.04.004Dodgson, M., Gann, D., & Salter, A. (2006). The role of technology in the shift towards open innovation: the case of Procter & Gamble. R and D Management, 36(3), 333-346. doi:10.1111/j.1467-9310.2006.00429.xDoloreux, D., & Melançon, Y. (2008). On the dynamics of innovation in Quebec’s coastal maritime industry. 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R&D Management, 40(3), 246-255. doi:10.1111/j.1467-9310.2010.00595.xJacobides, M. G., & Billinger, S. (2006). Designing the Boundaries of the Firm: From «Make, Buy, or Ally» to the Dynamic Benefits of Vertical Architecture. Organization Science, 17(2), 249-261. doi:10.1287/orsc.1050.0167Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and Problem-Solving Effectiveness in Broadcast Search. Organization Science, 21(5), 1016-1033. doi:10.1287/orsc.1090.0491Kaminski, P. C., de Oliveira, A. C., & Lopes, T. M. (2008). Knowledge transfer in product development processes: A case study in small and medium enterprises (SMEs) of the metal-mechanic sector from São Paulo, Brazil. Technovation, 28(1-2), 29-36. doi:10.1016/j.technovation.2007.07.001Keupp, M. M., & Gassmann, O. (2009). Determinants and archetype users of open innovation. R&D Management, 39(4), 331-341. doi:10.1111/j.1467-9310.2009.00563.xKirschbaum, R. (2005). Open Innovation In Practice. 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Journal of Management Information Systems, 26(1), 197-224. doi:10.2753/mis0742-1222260108Lemon, M., & Sahota, P. S. (2004). Organizational culture as a knowledge repository for increased innovative capacity. Technovation, 24(6), 483-498. doi:10.1016/s0166-4972(02)00102-5Li, Y.-R. (2009). The technological roadmap of Cisco’s business ecosystem. Technovation, 29(5), 379-386. doi:10.1016/j.technovation.2009.01.007Lichtenthaler, U. (2007). The Drivers of Technology Licensing: An Industry Comparison. California Management Review, 49(4), 67-89. doi:10.2307/41166406Lichtenthaler, U. (2008). Open Innovation in Practice: An Analysis of Strategic Approaches to Technology Transactions. IEEE Transactions on Engineering Management, 55(1), 148-157. doi:10.1109/tem.2007.912932Lichtenthaler, U. (2009). Outbound open innovation and its effect on firm performance: examining environmental influences. R&D Management, 39(4), 317-330. doi:10.1111/j.1467-9310.2009.00561.xLichtenthaler, U., & Ernst, H. (2006). Attitudes to externally organising knowledge management tasks: a review, reconsideration and extension of the NIH syndrome. R and D Management, 36(4), 367-386. doi:10.1111/j.1467-9310.2006.00443.xLichtenthaler, U., & Ernst, H. (2007). External technology commercialization in large firms: results of a quantitative benchmarking study. R&D Management, 37(5), 383-397. doi:10.1111/j.1467-9310.2007.00487.xLichtenthaler, U., & Ernst, H. (2009). Opening up the innovation process: the role of technology aggressiveness. R&D Management, 39(1), 38-54. doi:10.1111/j.1467-9310.2008.00522.xLichtenthaler, U., & Lichtenthaler, E. (2009). A Capability-Based Framework for Open Innovation: Complementing Absorptive Capacity. Journal of Management Studies, 46(8), 1315-1338. doi:10.1111/j.1467-6486.2009.00854.xLove, J. H., Roper, S., & Bryson, J. R. (2011). Openness, knowledge, innovation and growth in UK business services. 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Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment

[EN] The logical separation of the data plane and the control plane of the network device conceptually defined by software-defined networking (SDN) creates many opportunities to create smart networking with better efficiency for network management and operation. SDN implementation over telecommunications (Telcos) and Internet service provider (ISP) networks is a challenging issue due to the lack of a high maturity level of SDN-based standards and several other critical factors that are considered during the real-time migration of existing legacy IPv4 networks. Different migration approaches have been studied; however, none of them seem to be close to realizing implementation. This paper implements the SDN-IP and Open Network Operating System (ONOS) SDN controller to migrate legacy IPv4 networks to multi-domain software-defined IPv6 (SoDIP6) networks and experimentally evaluate the viability of joint network migration in the ISP networks. We present results using extensive simulations for the suitable placement of the master ONOS controller during network migration by considering minimum control path latency using optimal path routing and the breadth first router replacement (BFR) technique. Our empirical analysis and evaluations show that the identification of the median router to attach the master controller and router migration planning using BFR give better results for carrier-grade legacy networks' migration to SoDIP6 networks.This research was partially funded by the Norwegian University of Science and Technology, Trondhiem, Norway (NTNU) under Sustainable Engineering Education Project (SEEP) financed by EnPE, University Grant Commission (grant-ID: FRG7475Engg01), Bhaktapur, Nepal, Nepal academy of Science and Technology (NAST), Kathmandu, Nepal, and U.S. National Science Foundation (NSF). The work of Danda B. Rawat was partly supported by the U.S. National Science Foundation (NSF) under grants CNS 1650831 and HRD 1828811. Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the NSF. We are thankful to the ERASMUS+ KA107 project and the GRC lab team members at Universitat Politècnica De València for the research support and facilitation.Dawadi, BR.; Rawat, DB.; Joshi, SR.; Manzoni, P. (2020). Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment. Electronics. 9(9):1-22. https://doi.org/10.3390/electronics9091454S12299Dawadi, B. R., Rawat, D. B., & Joshi, S. R. (2019). Software Defined IPv6 Network: A New Paradigm for Future Networking. Journal of the Institute of Engineering, 15(2), 1-13. doi:10.3126/jie.v15i2.27636Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Manzoni, P. (2020). Evolutionary gaming approach for decision making of Tier‐3 Internet service provider networks migration to SoDIP6 networks. International Journal of Communication Systems, 33(11). doi:10.1002/dac.4399Gu, D., Su, J., Xue, Y., Wang, D., Li, J., Luo, Z., & Yan, B. (2020). Modeling IPv6 adoption from biological evolution. Computer Communications, 158, 166-177. doi:10.1016/j.comcom.2020.02.081IPv6 Capability Measurement https://stats.labs.apnic.net/ipv6Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Keitsch, M. M. (2018). Joint Cost Estimation Approach for Service Provider Legacy Network Migration to Unified Software Defined IPv6 Network. 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). doi:10.1109/cic.2018.00056Csikor, L., Szalay, M., Retvari, G., Pongracz, G., Pezaros, D. P., & Toka, L. (2020). Transition to SDN is HARMLESS: Hybrid Architecture for Migrating Legacy Ethernet Switches to SDN. IEEE/ACM Transactions on Networking, 28(1), 275-288. doi:10.1109/tnet.2019.2958762Sandhya, Sinha, Y., & Haribabu, K. (2017). A survey: Hybrid SDN. Journal of Network and Computer Applications, 100, 35-55. doi:10.1016/j.jnca.2017.10.003Mostafaei, H., Lospoto, G., Di Lallo, R., Rimondini, M., & Di Battista, G. (2020). A framework for multi‐provider virtual private networks in software‐defined federated networks. International Journal of Network Management, 30(6). doi:10.1002/nem.2116Dawadi, B. R., Rawat, D. B., & Joshi, S. R. (2019). Evolutionary Dynamics of Service Provider Legacy Network Migration to Software Defined IPv6 Network. Advances in Intelligent Systems and Computing, 245-257. doi:10.1007/978-3-030-19861-9_24Salsano, S., Ventre, P. L., Lombardo, F., Siracusano, G., Gerola, M., Salvadori, E., … Prete, L. (2016). Hybrid IP/SDN Networking: Open Implementation and Experiment Management Tools. IEEE Transactions on Network and Service Management, 13(1), 138-153. doi:10.1109/tnsm.2015.2507622Vissicchio, S., Tilmans, O., Vanbever, L., & Rexford, J. (2015). Central Control Over Distributed Routing. Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. doi:10.1145/2785956.2787497Rizvi, S. N., Raumer, D., Wohlfart, F., & Carle, G. (2015). Towards carrier grade SDNs. Computer Networks, 92, 218-226. doi:10.1016/j.comnet.2015.09.029Risdianto, A. C., Tsai, P.-W., Ling, T. C., Yang, C.-S., & Kim, J. (2017). Enhanced Onos Sdn Controllers Deployment For Federated Multi-Domain Sdn-Cloud With Sd-Routing-Exchange. Malaysian Journal of Computer Science, 30(2), 134-153. doi:10.22452/mjcs.vol30no2.5Ventre, P. L., Salsano, S., Gerola, M., Salvadori, E., Usman, M., Buscaglione, S., … Snow, W. (2017). SDN-Based IP and Layer 2 Services with an Open Networking Operating System in the GÉANT Service Provider Network. IEEE Communications Magazine, 55(4), 71-79. doi:10.1109/mcom.2017.1600194SDN-IP Arhitecture https://wiki.onosproject.org/display/ONOS/SDN-IP+ArchitectureLee, H.-L., Liu, T.-L., & Chen, M. (2019). Deploying SDN-IP over Transnational Network Testbed. 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). doi:10.1109/icce-tw46550.2019.8991776Das, T., Sridharan, V., & Gurusamy, M. (2020). A Survey on Controller Placement in SDN. IEEE Communications Surveys & Tutorials, 22(1), 472-503. doi:10.1109/comst.2019.2935453Chen, W., Chen, C., Jiang, X., & Liu, L. (2018). Multi-Controller Placement Towards SDN Based on Louvain Heuristic Algorithm. IEEE Access, 6, 49486-49497. doi:10.1109/access.2018.2867931Qi, Y., Wang, D., Yao, W., Li, H., & Cao, Y. (2019). Towards Multi-Controller Placement for SDN Based on Density Peaks Clustering. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2019.8761814Lu, J., Zhang, Z., Hu, T., Yi, P., & Lan, J. (2019). A Survey of Controller Placement Problem in Software-Defined Networking. IEEE Access, 7, 24290-24307. doi:10.1109/access.2019.2893283Singh, A. K., Maurya, S., Kumar, N., & Srivastava, S. (2019). Heuristic approaches for the reliable SDN controller placement problem. Transactions on Emerging Telecommunications Technologies, 31(2). doi:10.1002/ett.3761Das, T., & Gurusamy, M. (2018). Resilient Controller Placement in Hybrid SDN/Legacy Networks. 2018 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2018.8647566Heller, B., Sherwood, R., & McKeown, N. (2012). The controller placement problem. ACM SIGCOMM Computer Communication Review, 42(4), 473-478. doi:10.1145/2377677.2377767SDN Control Plane Performance: Raising the Bar on SDN Performance, Scalability, and High Availability https://wiki.onosproject.org/download/attachments/13994369/Whitepaper-%20ONOS%20Kingfisher%20release%20performance.pdf?version=

Synthesis of the Inverse Kinematic Model of Non-Redundant Open-Chain Robotic Systems Using Groebner Basis Theory

[EN] One of the most important elements of a robot's control system is its Inverse Kinematic Model (IKM), which calculates the position and velocity references required by the robot's actuators to follow a trajectory. The methods that are commonly used to synthesize the IKM of open-chain robotic systems strongly depend on the geometry of the analyzed robot. Those methods are not systematic procedures that could be applied equally in all possible cases. This project presents the development of a systematic procedure to synthesize the IKM of non-redundant open-chain robotic systems using Groebner Basis theory, which does not depend on the geometry of the robot's structure. The inputs to the developed procedure are the robot's Denavit-Hartenberg parameters, while the output is the IKM, ready to be used in the robot's control system or in a simulation of its behavior. The Groebner Basis calculation is done in a two-step process, first computing a basis with Faugere's F4 algorithm and a grevlex monomial order, and later changing the basis with the FGLM algorithm to the desired lexicographic order. This procedure's performance was proved calculating the IKM of a PUMA manipulator and a walking hexapod robot. The errors in the computed references of both IKMs were absolutely negligible in their corresponding workspaces, and their computation times were comparable to those required by the kinematic models calculated by traditional methods. The developed procedure can be applied to all Cartesian robotic systems, SCARA robots, all the non-redundant robotic manipulators that satisfy the in-line wrist condition, and any non-redundant open-chain robot whose IKM should only solve the positioning problem, such as multi-legged walking robots.This research was partially funded by Plan Nacional de I+D+i, Agencia Estatal de Investigacion del Ministerio de Economia, Industria y Competitividad del Gobierno de Espana, in the project FEDER-CICYT DPI2017-84201-R.Guzmán-Giménez, J.; Valera Fernández, Á.; Mata Amela, V.; Díaz-Rodríguez, MÁ. (2020). Synthesis of the Inverse Kinematic Model of Non-Redundant Open-Chain Robotic Systems Using Groebner Basis Theory. Applied Sciences. 10(8):1-22. https://doi.org/10.3390/app10082781S122108Atique, M. M. U., Sarker, M. R. I., & Ahad, M. A. R. (2018). Development of an 8DOF quadruped robot and implementation of Inverse Kinematics using Denavit-Hartenberg convention. Heliyon, 4(12), e01053. doi:10.1016/j.heliyon.2018.e01053Flanders, M., & Kavanagh, R. C. (2015). Build-A-Robot: Using virtual reality to visualize the Denavit-Hartenberg parameters. Computer Applications in Engineering Education, 23(6), 846-853. doi:10.1002/cae.21656Özgür, E., & Mezouar, Y. (2016). Kinematic modeling and control of a robot arm using unit dual quaternions. Robotics and Autonomous Systems, 77, 66-73. doi:10.1016/j.robot.2015.12.005Wang, X., Han, D., Yu, C., & Zheng, Z. (2012). The geometric structure of unit dual quaternion with application in kinematic control. Journal of Mathematical Analysis and Applications, 389(2), 1352-1364. doi:10.1016/j.jmaa.2012.01.016Barrientos, A., Álvarez, M., Hernández, J. D., del Cerro, J., & Rossi, C. (2012). Modelado de Caden as Cinemáticas mediante Matrices de Desplazamiento. Una alternativa al método de Denavit-Hartenberg. 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A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Information Sciences, 222, 528-543. doi:10.1016/j.ins.2012.07.051Rokbani, N., Casals, A., & Alimi, A. M. (2014). IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm. Computational Intelligence Applications in Modeling and Control, 369-395. doi:10.1007/978-3-319-11017-2_15Buchberger, B. (2001). Multidimensional Systems and Signal Processing, 12(3/4), 223-251. doi:10.1023/a:1011949421611Kendricks, K. D. (2013). A kinematic analysis of the gmf a-510 robot: An introduction and application of groebner basis theory. Journal of Interdisciplinary Mathematics, 16(2-03), 147-169. doi:10.1080/09720502.2013.800304Wang, Y., Hang, L., & Yang, T. (2006). Inverse Kinematics Analysis of General 6R Serial Robot Mechanism Based on Groebner Base. 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Prediction of Labor Induction Success from the Uterine Electrohysterogram

[EN] Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was to design an early success-predictor system by extracting temporal, spectral, and complexity parameters from the uterine electromyogram (electrohysterogram (EHG)). Different types of feature sets were used to design and train artificial neural networks: Set_1: obstetrical features, Set_2: EHG features, and Set_3: EHG+obstetrical features. Predictor systems were built to classify three scenarios: (1) induced women who reached active phase of labor (APL) vs. women who did not achieve APL (non-APL), (2) APL and vaginal delivery vs. APL and cesarean section delivery, and (3) vaginal vs. cesarean delivery. For Scenario 3, we also proposed 2-step predictor systems consisting of the cascading predictor systems from Scenarios 1 and 2. EHG features outperformed traditional obstetrical features in all the scenarios. Little improvement was obtained by combining them (Set_3). The results show that the EHG can potentially be used to predict successful labor induction and outperforms the traditional obstetric features. Clinical use of this prediction system would help to improve maternal-fetal well-being and optimize hospital resources.This work received financial support from the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R and RTI2018-094449-A-I00), Universitat Politècnica de València VLC/Campus (UPV-FE-2018-B02), Generalitat Valenciana (GV/2018/104), and Bial S.A.Benalcazar-Parra, C.; Ye Lin, Y.; Garcia-Casado, J.; Monfort-Ortiz, R.; Alberola Rubio, J.; Perales Marin, AJ.; Prats-Boluda, G. (2019). Prediction of Labor Induction Success from the Uterine Electrohysterogram. Journal of Sensors. 2019:1-12. https://doi.org/10.1155/2019/6916251S1122019Filho, O. B. M., Albuquerque, R. M., & Cecatti, J. G. (2010). A randomized controlled trial comparing vaginal misoprostol versus Foley catheter plus oxytocin for labor induction. Acta Obstetricia et Gynecologica Scandinavica, 89(8), 1045-1052. doi:10.3109/00016349.2010.499447Seyb, S. (1999). Risk of cesarean delivery with elective induction of labor at term in nulliparous women. Obstetrics & Gynecology, 94(4), 600-607. doi:10.1016/s0029-7844(99)00377-4Hou, L., Zhu, Y., Ma, X., Li, J., & Zhang, W. (2012). Clinical parameters for prediction of successful labor induction after application of intravaginal dinoprostone in nulliparous Chinese women. Medical Science Monitor, 18(8), CR518-CR522. doi:10.12659/msm.883273Pitarello, P. da R. P., Tadashi Yoshizaki, C., Ruano, R., & Zugaib, M. (2012). Prediction of successful labor induction using transvaginal sonographic cervical measurements. Journal of Clinical Ultrasound, 41(2), 76-83. doi:10.1002/jcu.21929Prado, C. A. de C., Araujo Júnior, E., Duarte, G., Quintana, S. M., Tonni, G., Cavalli, R. de C., & Marcolin, A. C. (2016). Predicting success of labor induction in singleton term pregnancies by combining maternal and ultrasound variables. The Journal of Maternal-Fetal & Neonatal Medicine, 1-35. doi:10.3109/14767058.2015.1135124Sievert, R. A., Kuper, S. G., Jauk, V. C., Parrish, M., Biggio, J. R., & Harper, L. M. (2017). Predictors of vaginal delivery in medically indicated early preterm induction of labor. American Journal of Obstetrics and Gynecology, 217(3), 375.e1-375.e7. doi:10.1016/j.ajog.2017.05.025Garfield, R. E., Maner, W. L., Maul, H., & Saade, G. R. (2005). Use of uterine EMG and cervical LIF in monitoring pregnant patients. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 103-108. doi:10.1111/j.1471-0528.2005.00596.xFergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE, 8(10), e77154. doi:10.1371/journal.pone.0077154Aviram, A., Melamed, N., Hadar, E., Raban, O., Hiersch, L., & Yogev, Y. (2013). Effect of Prostaglandin E2 on Myometrial Electrical Activity in Women Undergoing Induction of Labor. American Journal of Perinatology, 31(05), 413-418. doi:10.1055/s-0033-1352486Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2018). Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Medical Engineering & Physics, 56, 27-35. doi:10.1016/j.medengphy.2018.04.002Benalcazar-Parra, C., Monfort-Orti, R., Ye-Lin, Y., Prats-Boluda, G., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2017). Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram. The Journal of Maternal-Fetal & Neonatal Medicine, 32(10), 1586-1594. doi:10.1080/14767058.2017.1410791Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8Diab, M. O., Marque, C., & Khalil, M. (2009). An unsupervised classification method of uterine electromyography signals: Classification for detection of preterm deliveries. Journal of Obstetrics and Gynaecology Research, 35(1), 9-19. doi:10.1111/j.1447-0756.2008.00981.xShi, S.-Q., Maner, W. L., Mackay, L. B., & Garfield, R. E. (2008). Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals. American Journal of Obstetrics and Gynecology, 198(2), 235.e1-235.e4. doi:10.1016/j.ajog.2007.08.039Østborg, T. B., Romundstad, P. R., & Eggebø, T. M. (2016). Duration of the active phase of labor in spontaneous and induced labors. Acta Obstetricia et Gynecologica Scandinavica, 96(1), 120-127. doi:10.1111/aogs.13039Baños, N., Migliorelli, F., Posadas, E., Ferreri, J., & Palacio, M. (2015). Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation. Fetal Diagnosis and Therapy, 38(3), 161-169. doi:10.1159/000433429Bueno, B., San-Frutos, L., Salazar, F., Pérez-Medina, T., Engels, V., Archilla, B., … Bajo, J. (2005). Variables that predict the success of labor induction. Acta Obstetricia et Gynecologica Scandinavica, 84(11), 1093-1097. doi:10.1111/j.0001-6349.2005.00881.xWare, V., & Raynor, B. D. (2000). Transvaginal ultrasonographic cervical measurement as a predictor of successful labor induction. American Journal of Obstetrics and Gynecology, 182(5), 1030-1032. doi:10.1067/mob.2000.105399Rooijakkers, M. J., Song, S., Rabotti, C., Oei, S. G., Bergmans, J. W. M., Cantatore, E., & Mischi, M. (2014). Influence of Electrode Placement on Signal Quality for Ambulatory Pregnancy Monitoring. Computational and Mathematical Methods in Medicine, 2014, 1-12. doi:10.1155/2014/960980Garfield, R. E., & Maner, W. L. (2007). Physiology and electrical activity of uterine contractions. Seminars in Cell & Developmental Biology, 18(3), 289-295. doi:10.1016/j.semcdb.2007.05.004Leman, H., Marque, C., & Gondry, J. (1999). Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Transactions on Biomedical Engineering, 46(10), 1222-1229. doi:10.1109/10.790499BUHIMSCHI, C., BOYLE, M., & GARFIELD, R. (1997). Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface. Obstetrics & Gynecology, 90(1), 102-111. doi:10.1016/s0029-7844(97)83837-9Schlembach, D., Maner, W. L., Garfield, R. E., & Maul, H. (2009). Monitoring the progress of pregnancy and labor using electromyography. European Journal of Obstetrics & Gynecology and Reproductive Biology, 144, S33-S39. doi:10.1016/j.ejogrb.2009.02.016Alamedine, D., Diab, A., Muszynski, C., Karlsson, B., Khalil, M., & Marque, C. (2014). Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. Signal, Image and Video Processing, 8(6), 1169-1178. doi:10.1007/s11760-014-0655-2Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yTerrien, J., Marque, C., Gondry, J., Steingrimsdottir, T., & Karlsson, B. (2010). Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals. Computers in Biology and Medicine, 40(2), 223-230. doi:10.1016/j.compbiomed.2009.11.019Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Lemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Blanco, S., Garay, A., & Coulombie, D. (2013). Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction. ISRN Neurology, 2013, 1-5. doi:10.1155/2013/287327Brennan, M., Palaniswami, M., & Kamen, P. (2001). Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48(11), 1342-1347. doi:10.1109/10.959330Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Makond, B., Wang, K.-J., & Wang, K.-M. (2015). Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer. Computer Methods and Programs in Biomedicine, 119(3), 142-162. doi:10.1016/j.cmpb.2015.02.005Gori, M., & Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(1), 76-86. doi:10.1109/34.107014Zong, W., Huang, G.-B., & Chen, Y. (2013). Weighted extreme learning machine for imbalance learning. Neurocomputing, 101, 229-242. doi:10.1016/j.neucom.2012.08.010Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). 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Denial of service mitigation approach for IPv6-enabled smart object networks

Denial of service (DoS) attacks can be defined as any third-party action aiming to reduce or eliminate a network's capability to perform its expected functions. Although there are several standard techniques in traditional computing that mitigate the impact of some of the most common DoS attacks, this still remains a very important open problem to the network security community. DoS attacks are even more troublesome in smart object networks because of two main reasons. First, these devices cannot support the computational overhead required to implement many of the typical counterattack strategies. Second, low traffic rates are enough to drain sensors' battery energy making the network inoperable in short times. To realize the Internet of Things vision, it is necessary to integrate the smart objects into the Internet. This integration is considered an exceptional opportunity for Internet growth but, also, a security threat, because more attacks, including DoS, can be conducted. For these reasons, the prevention of DoS attacks is considered a hot topic in the wireless sensor networks scientific community. In this paper, an approach based on 6LowPAN neighbor discovery protocol is proposed to mitigate DoS attacks initiated from the Internet, without adding additional overhead on the 6LoWPAN sensor devices.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e Tecnologia through the Pest-OE/EEI/LA0008/2011.Oliveira, LML.; Rodrigues, JJPC.; De Sousa, AF.; Lloret, J. (2013). Denial of service mitigation approach for IPv6-enabled smart object networks. Concurrency and Computation: Practice and Experience. 25(1):129-142. doi:10.1002/cpe.2850S129142251Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Karl, H., & Willig, A. (2005). Protocols and Architectures for Wireless Sensor Networks. doi:10.1002/0470095121IEEE Std 802.15.4-2006 Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specificationsfor low-rate wireless personal area networks (LR-WPANs) 2006ZigBee Alliance ZigBee Specification 2007WirelessHARThomepage 2012 http://www.hartcomm.org/Hui, J. W., & Culler, D. E. (2008). Extending IP to Low-Power, Wireless Personal Area Networks. IEEE Internet Computing, 12(4), 37-45. doi:10.1109/mic.2008.79Kushalnagar N Montenegro G Schumacher C IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): Overview, Assumptions, Problem Statement, and Goals 2007Montenegro G Kushalnagar N Hui J Culler D Transmission of IPv6 Packets over IEEE 802.15.4 Networks 2007Shelby Z Thubert P Hui J Chakrabarti S Bormann C Nordmark E 6LoWPAN Neighbor Discovery 2011Zhou, L., Chao, H.-C., & Vasilakos, A. V. (2011). Joint Forensics-Scheduling Strategy for Delay-Sensitive Multimedia Applications over Heterogeneous Networks. IEEE Journal on Selected Areas in Communications, 29(7), 1358-1367. doi:10.1109/jsac.2011.110803Roman, R., & Lopez, J. (2009). Integrating wireless sensor networks and the internet: a security analysis. Internet Research, 19(2), 246-259. doi:10.1108/10662240910952373Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks. IEEE Communications Surveys & Tutorials, 8(2), 2-23. doi:10.1109/comst.2006.315852Xiaojiang Du, & Hsiao-Hwa Chen. (2008). Security in wireless sensor networks. IEEE Wireless Communications, 15(4), 60-66. doi:10.1109/mwc.2008.4599222Pelechrinis, K., Iliofotou, M., & Krishnamurthy, S. V. (2011). Denial of Service Attacks in Wireless Networks: The Case of Jammers. IEEE Communications Surveys & Tutorials, 13(2), 245-257. doi:10.1109/surv.2011.041110.00022Zhou, L., Wang, X., Tu, W., Muntean, G., & Geller, B. (2010). Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks. IEEE Journal on Selected Areas in Communications, 28(3), 409-419. doi:10.1109/jsac.2010.100412Lin, K., Lai, C.-F., Liu, X., & Guan, X. (2010). Energy Efficiency Routing with Node Compromised Resistance in Wireless Sensor Networks. Mobile Networks and Applications, 17(1), 75-89. doi:10.1007/s11036-010-0287-xLi, H., Lin, K., & Li, K. (2011). Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks. Computer Communications, 34(4), 591-597. doi:10.1016/j.comcom.2010.02.026Oliveira, L. M. L., de Sousa, A. F., & Rodrigues, J. J. P. C. (2011). Routing and mobility approaches in IPv6 over LoWPAN mesh networks. International Journal of Communication Systems, 24(11), 1445-1466. doi:10.1002/dac.1228Narten T Nordmark E Simpson W Soliman H Neighbor Discovery for IP version 6 (IPv6) 2007Singh H Beebee W Nordmark E IPv6 Subnet Model: The Relationship between Links and Subnet Prefixes 2010Roman, R., Lopez, J., & Gritzalis, S. (2008). Situation awareness mechanisms for wireless sensor networks. IEEE Communications Magazine, 46(4), 102-107. doi:10.1109/mcom.2008.4481348Sakarindr, P., & Ansari, N. (2007). Security services in group communications over wireless infrastructure, mobile ad hoc, and wireless sensor networks. IEEE Wireless Communications, 14(5), 8-20. doi:10.1109/mwc.2007.4396938Tsao T Alexander R Dohler M Daza V Lozano A A Security Framework for Routing over Low Power and Lossy Networks 2009Karlof C Wagner D Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures First IEEE International Workshop on Sensor Network Protocols and Applications 2003 113 127 10.1109/SNPA.2003.1203362Hui J Thubert P Compression Format for IPv6 Datagrams in 6LoWPAN Networks 2009Elaine Shi, & Perrig, A. (2004). Designing Secure Sensor Networks. IEEE Wireless Communications, 11(6), 38-43. doi:10.1109/mwc.2004.1368895Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325-349. doi:10.1016/j.adhoc.2003.09.01

Setting decision thresholds when operating conditions are uncertain

[EN] The quality of the decisions made by a machine learning model depends on the data and the operating conditions during deployment. Often, operating conditions such as class distribution and misclassification costs have changed during the time since the model was trained and evaluated. When deploying a binary classifier that outputs scores, once we know the new class distribution and the new cost ratio between false positives and false negatives, there are several methods in the literature to help us choose an appropriate threshold for the classifier's scores. However, on many occasions, the information that we have about this operating condition is uncertain. Previous work has considered ranges or distributions of operating conditions during deployment, with expected costs being calculated for ranges or intervals, but still the decision for each point is made as if the operating condition were certain. The implications of this assumption have received limited attention: a threshold choice that is best suited without uncertainty may be suboptimal under uncertainty. In this paper we analyse the effect of operating condition uncertainty on the expected loss for different threshold choice methods, both theoretically and experimentally. We model uncertainty as a second conditional distribution over the actual operation condition and study it theoretically in such a way that minimum and maximum uncertainty are both seen as special cases of this general formulation. This is complemented by a thorough experimental analysis investigating how different learning algorithms behave for a range of datasets according to the threshold choice method and the uncertainty level.We thank the anonymous reviewers for their comments, which have helped to improve this paper significantly. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grant TIN 2015-69175-C4-1-R and by Generalitat Valenciana under Grant PROMETEOII/2015/013. Jose Hernandez-Orallo was supported by a Salvador de Madariaga Grant (PRX17/00467) from the Spanish MECD for a research stay at the Leverhulme Centre for the Future of Intelligence (CFI), Cambridge, a BEST Grant (BEST/2017/045) from Generalitat Valenciana for another research stay also at the CFI and an FLI Grant RFP2-152.Ferri Ramírez, C.; Hernández-Orallo, J.; Flach, P. (2019). Setting decision thresholds when operating conditions are uncertain. Data Mining and Knowledge Discovery. 33(4):805-847. https://doi.org/10.1007/s10618-019-00613-7S805847334Adams N, Hand D (1999) Comparing classifiers when the misallocation costs are uncertain. Pattern Recognit 32(7):1139–1147Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2013) On the effect of calibration in classifier combination. 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