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Proving Opacity of Transactional Memory with Early Release

, P. Brief announcement: actions in the twilight-concurrent irrevocable transactions and inconsistency repair. In PODC ’10, 2010. [5] Dice, D., Shalev, O., and Shavit, N. Transactional Locking II. In Proc. DISC’06, 2006. [6] Doherty, S., Groves, L., Luchangco, V., and Moir, M. Towards formally specifying and verifying transactional memory. Formal Aspects of Computing, 25, Sept. 2013. [7] Dziuma, D., Fatourou, P., and Kanellou, E. Consistency for transactional memory computing. Bulletin of the EATCS, 113, 2014

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Parallel RANSAC for Point Cloud Registration

, 24(6):381–395, June 1981. [7] Hänsch R., Weber T., and Hellwich O. Comparison of 3D interest point detectors and descriptors for point cloud fusion. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences , pages 57–64, Aug. 2014. [8] Hartley R. and Zisserman A. Multiple view geometry in computer vision . Cambridge University Press, Cambridge, 2003. Choix de documents en appendice. [9] Heuros 3D object recognition system. . Accessed: 04-05-2015. [10] Izadi S., Kim D., Hilliges O

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Data Management for Fingerprint Recognition Algorithm Based on Characteristic Points' Group

., Govindaraju V., Cartwright E. N.(2006) K-plet and coupled bfs: A graph based fingerprint representation and matching algorithm. LNCS : 309 - 315 [5] Grzeszyk C. (1992) Forensic fingerprint examination marks . (in Polish), Wydawnictwo Centrum Szkolenia Policji, Legionowo [6] He Y., Ou Z.(2005) Fingerprint matching algorithm based on local minutiae adjacency graph, Journal of Harbin Institute of Technology 10/05, pp. 95-103 [7] Huk M., Szczepanik M. (2011) Multiple classifier error probability for multi-class problems

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Keypoint-Less, Heuristic Application of Local 3D Descriptors

References [1] Heuros 3D object recognition system . . Accessed: 04-05-2015. [2] Aldoma, A., et al. CAD-model recognition and 6DOF pose estimation using 3D cues . In IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011 , 585–592. 2011. [3] Alexandre, L. A. 3D descriptors for object and category recognition: a comparative evaluation . In Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on

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Domain Modeling in the Context of Ontology

References [1] Hesse W., Ontologies in the Software Engineering Process, in: R. Lenz at al. (ed.): EAI 2005 - Proceedings of the Workshop on Enterprise Application Integration, 2005. [2] Gašević D., Djurić D., Devedžić V., Model Driven Architecture and Ontology Development , Springer 2006. [3] Happel H-J., Seedorf S., Applications of ontologies in software engineering, Proc. of Workshop on Sematic Web Enabled Software Engineering (SWESE) on the ISWC , 2006. [4] Kleshchev A.S., How can ontologies contribute to software development?, Knowledge

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Secure energy efficient routing protocol for wireless sensor network

Aggregation Protocol for Cluster-Based Wireless Sensor Networks with no Requirements for Trusted Aggregator Nodes, Next Generation Mobile Applications, Services and Technologies, NGMAST7. 2007. [7] Chris Karlof and David Wagner, Secure routing in wireless sensor networks: attacks and countermeasures, Ad Hoc Networks, Elsevier Journal , doi:10.1016/S1570-8705(03)00008-8, 2003, 293–315. [8] D. Hwang, B. Lai, I. Verbauwhede, Energy-memory-security trade-offsin distributed sensor networks, ADHOC-NOW , Springer, Berlin/Heidelberg, 2004, pp. 7081. [9] Diffie

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Data Sensitive Recommendation Based On Community Detection

services, ACM Transactions on Intelligent Systems and Technology (TIST) , 4 , 1, 2013, 10. [4] Blondel V D., Guillaume J L., Lambiotte R., Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment , 10, 2008, 10008. [5] Breese J.S., Heckerman D., Kadie C., Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence Morgan Kaufmann Publishers Inc ., 1998: 43-52. [6] Balabanović M., Shoham Y., Fab: content

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Predicting Aggregated User Satisfaction in Software Projects

Francisco, CA, USA, 1998, 144-151. [8] Friedman J., Hastie T., Tibshirani R., Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting , The Annals of Statistics, 28 , 2, 2000, 337-374. [9] Garcés, L., Ampatzoglou, A., Avgeriou, P., Nakagawa, E.Y., Quality attributes and quality models for ambient assisted living software systems: A systematic mapping , Information and Software Technology, 82 , 2017, 121-138. [10] Hall M., Frank E., Combining Naive Bayes and Decision Tables, in: D.L. Wilson & H. Chad (Eds), Proceedings of

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Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

. [6] Clark D. M. Evolution of algebraic terms 1: term to term operation continuity. International Journal of Algebra and Computation, 23(05):1175-1205, 2013. [7] Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002. [8] Ficici S. G. and Pollack J. B. Pareto optimality in coevolutionary learning. In Kelemen J. and Sosík P., editors, Advances in Artificial Life, 6th European Conference, ECAL 2001, volume 2159

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Security Aspects in Resource Management Systems in Distributed Computing Environments

-Based Grid Resource Selection, Proc. Workshop Adaptive Grid Middleware (AGridM '03), 2003. [4] Bąk S., Krystek M., Kurowski K., Oleksiak A., Piątek W., Węglarz J., GSSIM - a tool for distributed computing experiments, Scientific Programming Journal, vol. 19, no. 4, p. 231-251, 2011 [5] Delft University of Technology, Grid workloads archive, 2007, [Online]. Available:, [Accessed 2016] [6] Feitelson D., Parallel workload archive, 2011, [Online]. Available: http

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