Tarski Geometry Axioms – Part II

In our earlier article [12], the first part of axioms of geometry proposed by Alfred Tarski [14] was formally introduced by means of Mizar proof assistant [9]. We defined a structure TarskiPlane with the following predicates: of betweenness between (a ternary relation),of congruence of segments equiv (quarternary relation), which satisfy the following properties: congruence symmetry (A1),congruence equivalence relation (A2),congruence identity (A3),segment construction (A4),SAS (A5),betweenness identity (A6),Pasch (A7). Also a simple model, which satisfies these axioms, was previously constructed, and described in [6]. In this paper, we deal with four remaining axioms, namely: the lower dimension axiom (A8),the upper dimension axiom (A9),the Euclid axiom (A10),the continuity axiom (A11). They were introduced in the form of Mizar attributes. Additionally, the relation of congruence of triangles cong is introduced via congruence of sides (SSS).In order to show that the structure which satisfies all eleven Tarski’s axioms really exists, we provided a proof of the registration of a cluster that the Euclidean plane, or rather a natural [5] extension of ordinary metric structure Euclid 2 satisfies all these attributes.Although the tradition of the mechanization of Tarski’s geometry in Mizar is not as long as in Coq [11], first approaches to this topic were done in Mizar in 1990 [16] (even if this article started formal Hilbert axiomatization of geometry, and parallel development was rather unlikely at that time [8]). Connection with another proof assistant should be mentioned – we had some doubts about the proof of the Euclid’s axiom and inspection of the proof taken from Archive of Formal Proofs of Isabelle [10] clarified things a bit. Our development allows for the future faithful mechanization of [13] and opens the possibility of automatically generated Prover9 proofs which was useful in the case of lattice theory [7].Coghetto Roland - Rue de la Brasserie 5, 7100 La Louvière, BelgiumGrabowski Adam - Institute of Informatics, University of Białystok, Ciołkowskiego 1M, 15-245 Białystok, PolandCzesław Byliński. Introduction to real linear topological spaces. Formalized Mathematics, 13(1):99–107, 2005.Czesław Byliński. Some basic properties of sets. Formalized Mathematics, 1(1):47–53, 1990.Roland Coghetto. Circumcenter, circumcircle and centroid of a triangle. Formalized Mathematics, 24(1):17–26, 2016. doi:10.1515/forma-2016-0002.Agata Darmochwał. The Euclidean space. Formalized Mathematics, 2(4):599–603, 1991.Adam Grabowski. Efficient rough set theory merging. Fundamenta Informaticae, 135(4): 371–385, 2014. doi:10.3233/FI-2014-1129.Adam Grabowski. Tarski’s geometry modelled in Mizar computerized proof assistant. In Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11–14, 2016, pages 373–381, 2016. doi:10.15439/2016F290.Adam Grabowski. Mechanizing complemented lattices within Mizar system. Journal of Automated Reasoning, 55:211–221, 2015. doi:10.1007/s10817-015-9333-5.Adam Grabowski and Christoph Schwarzweller. On duplication in mathematical repositories. In Serge Autexier, Jacques Calmet, David Delahaye, Patrick D. F. Ion, Laurence Rideau, Renaud Rioboo, and Alan P. Sexton, editors, Intelligent Computer Mathematics, 10th International Conference, AISC 2010, 17th Symposium, Calculemus 2010, and 9th International Conference, MKM 2010, Paris, France, July 5–10, 2010. Proceedings, volume 6167 of Lecture Notes in Computer Science, pages 300–314. Springer, 2010. doi:10.1007/978-3-642-14128-7_26.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.Timothy James McKenzie Makarios. A mechanical verification of the independence of Tarski’s Euclidean Axiom. 2012. Master’s thesis.Julien Narboux. Mechanical theorem proving in Tarski’s geometry. In F. Botana and T. Recio, editors, Automated Deduction in Geometry, volume 4869 of Lecture Notes in Computer Science, pages 139–156. Springer, 2007.William Richter, Adam Grabowski, and Jesse Alama. Tarski geometry axioms. Formalized Mathematics, 22(2):167–176, 2014. doi:10.2478/forma-2014-0017.Wolfram Schwabhäuser, Wanda Szmielew, and Alfred Tarski. Metamathematische Methoden in der Geometrie. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1983.Alfred Tarski and Steven Givant. Tarski’s system of geometry. Bulletin of Symbolic Logic, 5(2):175–214, 1999.Andrzej Trybulec and Czesław Byliński. Some properties of real numbers. Formalized Mathematics, 1(3):445–449, 1990.Wojciech A. Trybulec. Axioms of incidence. Formalized Mathematics, 1(1):205–213, 1990.Wojciech A. Trybulec. Vectors in real linear space. Formalized Mathematics, 1(2):291–296, 1990

Automatic classification of human facial features based on their appearance

[EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.Fuentes-Hurtado, F.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2019). Automatic classification of human facial features based on their appearance. PLoS ONE. 14(1):1-20. https://doi.org/10.1371/journal.pone.0211314S120141Damasio, A. R. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132-135. doi:10.1016/0166-2236(85)90051-7Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xTodorov, A. (2011). Evaluating Faces on Social Dimensions. Social Neuroscience, 54-76. doi:10.1093/acprof:oso/9780195316872.003.0004Little, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Porter, J. P., & Olson, K. L. (2001). Anthropometric Facial Analysis of the African American Woman. Archives of Facial Plastic Surgery, 3(3), 191-197. doi:10.1001/archfaci.3.3.191Gündüz Arslan, S., Genç, C., Odabaş, B., & Devecioğlu Kama, J. (2007). Comparison of Facial Proportions and Anthropometric Norms Among Turkish Young Adults With Different Face Types. Aesthetic Plastic Surgery, 32(2), 234-242. doi:10.1007/s00266-007-9049-yFerring, V., & Pancherz, H. (2008). Divine proportions in the growing face. American Journal of Orthodontics and Dentofacial Orthopedics, 134(4), 472-479. doi:10.1016/j.ajodo.2007.03.027Mane, D. R., Kale, A. D., Bhai, M. B., & Hallikerimath, S. (2010). Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: A pilot study. Journal of Forensic and Legal Medicine, 17(8), 421-425. doi:10.1016/j.jflm.2010.09.001Ritz-Timme, S., Gabriel, P., Tutkuviene, J., Poppa, P., Obertová, Z., Gibelli, D., … Cattaneo, C. (2011). Metric and morphological assessment of facial features: A study on three European populations. Forensic Science International, 207(1-3), 239.e1-239.e8. doi:10.1016/j.forsciint.2011.01.035Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005). Recent advances in visual and infrared face recognition—a review. Computer Vision and Image Understanding, 97(1), 103-135. doi:10.1016/j.cviu.2004.04.001Tavares, G., Mourão, A., & Magalhães, J. (2016). Crowdsourcing facial expressions for affective-interaction. Computer Vision and Image Understanding, 147, 102-113. doi:10.1016/j.cviu.2016.02.001Buckingham, G., DeBruine, L. M., Little, A. C., Welling, L. L. M., Conway, C. A., Tiddeman, B. P., & Jones, B. C. (2006). Visual adaptation to masculine and feminine faces influences generalized preferences and perceptions of trustworthiness. Evolution and Human Behavior, 27(5), 381-389. doi:10.1016/j.evolhumbehav.2006.03.001Boberg M, Piippo P, Ollila E. Designing Avatars. DIMEA ‘08 Proc 3rd Int Conf Digit Interact Media Entertain Arts. ACM; 2008; 232–239. doi: https://doi.org/10.1145/1413634.1413679Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Laurentini, A., & Bottino, A. (2014). Computer analysis of face beauty: A survey. Computer Vision and Image Understanding, 125, 184-199. doi:10.1016/j.cviu.2014.04.006Alemany S, Gonzalez J, Nacher B, Soriano C, Arnaiz C, Heras H. Anthropometric survey of the Spanish female population aimed at the apparel industry. Proceedings of the 2010 Intl Conference on 3D Body scanning Technologies. 2010. pp. 307–315.Vinué, G., Epifanio, I., & Alemany, S. (2015). Archetypoids: A new approach to define representative archetypal data. Computational Statistics & Data Analysis, 87, 102-115. doi:10.1016/j.csda.2015.01.018Jee, S., & Yun, M. H. (2016). An anthropometric survey of Korean hand and hand shape types. International Journal of Industrial Ergonomics, 53, 10-18. doi:10.1016/j.ergon.2015.10.004Kim, N.-S., & Do, W.-H. (2014). Classification of Elderly Women’s Foot Type. Journal of the Korean Society of Clothing and Textiles, 38(3), 305-320. doi:10.5850/jksct.2014.38.3.305Sarakon P, Charoenpong T, Charoensiriwath S. Face shape classification from 3D human data by using SVM. The 7th 2014 Biomedical Engineering International Conference. IEEE; 2014. pp. 1–5. doi: https://doi.org/10.1109/BMEiCON.2014.7017382PRESTON, T. A., & SINGH, M. (1972). Redintegrated Somatotyping. Ergonomics, 15(6), 693-700. doi:10.1080/00140137208924469Lin, Y.-L., & Lee, K.-L. (1999). Investigation of anthropometry basis grouping technique for subject classification. Ergonomics, 42(10), 1311-1316. doi:10.1080/001401399184965Malousaris, G. G., Bergeles, N. K., Barzouka, K. G., Bayios, I. A., Nassis, G. P., & Koskolou, M. D. (2008). Somatotype, size and body composition of competitive female volleyball players. Journal of Science and Medicine in Sport, 11(3), 337-344. doi:10.1016/j.jsams.2006.11.008Carvalho, P. V. R., dos Santos, I. L., Gomes, J. O., Borges, M. R. S., & Guerlain, S. (2008). Human factors approach for evaluation and redesign of human–system interfaces of a nuclear power plant simulator. Displays, 29(3), 273-284. doi:10.1016/j.displa.2007.08.010Fabri M, Moore D. The use of emotionally expressive avatars in Collaborative Virtual Environments. AISB’05 Convention:Proceedings of the Joint Symposium on Virtual Social Agents: Social Presence Cues for Virtual Humanoids Empathic Interaction with Synthetic Characters Mind Minding Agents. 2005. pp. 88–94. doi:citeulike-article-id:790934Sukhija, P., Behal, S., & Singh, P. (2016). Face Recognition System Using Genetic Algorithm. Procedia Computer Science, 85, 410-417. doi:10.1016/j.procs.2016.05.183Trescak T, Bogdanovych A, Simoff S, Rodriguez I. Generating diverse ethnic groups with genetic algorithms. Proceedings of the 18th ACM symposium on Virtual reality software and technology—VRST ‘12. New York, New York, USA: ACM Press; 2012. p. 1. doi: https://doi.org/10.1145/2407336.2407338Vanezis, P., Lu, D., Cockburn, J., Gonzalez, A., McCombe, G., Trujillo, O., & Vanezis, M. (1996). Morphological Classification of Facial Features in Adult Caucasian Males Based on an Assessment of Photographs of 50 Subjects. Journal of Forensic Sciences, 41(5), 13998J. doi:10.1520/jfs13998jTamir, A. (2011). Numerical Survey of the Different Shapes of the Human Nose. Journal of Craniofacial Surgery, 22(3), 1104-1107. doi:10.1097/scs.0b013e3182108eb3Tamir, A. (2013). Numerical Survey of the Different Shapes of Human Chin. Journal of Craniofacial Surgery, 24(5), 1657-1659. doi:10.1097/scs.0b013e3182942b77Richler, J. J., Cheung, O. S., & Gauthier, I. (2011). Holistic Processing Predicts Face Recognition. Psychological Science, 22(4), 464-471. doi:10.1177/0956797611401753Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Donnelly, N., & Davidoff, J. (1999). The Mental Representations of Faces and Houses: Issues Concerning Parts and Wholes. Visual Cognition, 6(3-4), 319-343. doi:10.1080/135062899395000Davidoff, J., & Donnelly, N. (1990). Object superiority: A comparison of complete and part probes. Acta Psychologica, 73(3), 225-243. doi:10.1016/0001-6918(90)90024-aTanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Klare B, Jain AK. On a taxonomy of facial features. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. IEEE; 2010. pp. 1–8. doi: https://doi.org/10.1109/BTAS.2010.5634533Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Asthana A, Zafeiriou S, Cheng S, Pantic M. Incremental face alignment in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014. pp. 1859–1866. doi: https://doi.org/10.1109/CVPR.2014.240Bag S, Barik S, Sen P, Sanyal G. A statistical nonparametric approach of face recognition: combination of eigenface & modified k-means clustering. Proceedings Second International Conference on Information Processing. 2008. p. 198.Doukas, C., & Maglogiannis, I. (2010). A Fast Mobile Face Recognition System for Android OS Based on Eigenfaces Decomposition. Artificial Intelligence Applications and Innovations, 295-302. doi:10.1007/978-3-642-16239-8_39Huang P, Huang Y, Wang W, Wang L. Deep embedding network for clustering. Proceedings—International Conference on Pattern Recognition. 2014. pp. 1532–1537. doi: https://doi.org/10.1109/ICPR.2014.272Dizaji KG, Herandi A, Deng C, Cai W, Huang H. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. Proceedings of the IEEE International Conference on Computer Vision. 2017. doi: https://doi.org/10.1109/ICCV.2017.612Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis [Internet]. Proceedings of the 33rd International Conference on International Conference on Machine Learning—Volume 48. JMLR.org; 2016. pp. 478–487. Available: https://dl.acm.org/citation.cfm?id=3045442Nousi, P., & Tefas, A. (2017). Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition. Communications in Computer and Information Science, 205-215. doi:10.1007/978-3-319-65172-9_18Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.00051

The role of sex differences in detecting deception in computer-mediated communication in English

[EN] While deception seems to be a common approach in interpersonal communication, most examination on interpersonal deception sees the sex of the interlocutor as unconnected with the capability to notice deceptive messages. This research studies the truth and deception detection capability of both male and female receivers when replying to both true and deceptive messages from both male and female speakers. The outcomes indicate that sex may be a significant variable in comprehending the interpersonal detection probabilities of truth and of lies. An interaction of variables including the speakers’ sex, receivers’ sex, and whether the message appears to be truthful or deceptive is created to relate to detection capability.Kuzio, A. (2018). The role of sex differences in detecting deception in computer-mediated communication in English. Journal of Computer-Assisted Linguistic Research. 2(1):39-53. doi:10.4995/jclr.2018.10521SWORD395321Aamodt, M. G., & Custer, H. (2006). Who can best catch a liar? A meta-analysis of individual differences in detecting deception. The Forensic Examiner, 15(1), 6-11.Blalock, H. M. (1972). Social Statistics. New York: McGraw Hill.Bond, C. F., & DePaulo, B. M. (2006). Accuracy of deception judgments. Personality and Social Psychology Review, 10(3), 214-234. https://doi.org/10.1207/s15327957pspr1003_2Boush, D. M., Friestad, M., & Wright, P. (2009). Deception in the marketplace : The psychology of deceptive persuasion and consumer self-protection. New York: Routledge.Camden, C., Motley, M. T., & Wilson, A. (1984). White lies in interpersonal communication: A taxonomy and preliminary investigation of social motivations. Western Journal of Speech Communication, 48(4), 309-325. https://doi.org/10.1080/10570318409374167Carlson, J., George, J., Burgoon, J., Adkins, M., & White, C. (2004). Deception in computer mediated communication. Group Decision and Negotiation, 13, 5-28. https://doi.org/10.1023/B:GRUP.0000011942.31158.d8Daft, R.L. & Lengel, R.H. (1986). Information richness: A new approach to managerial behavior and organizational design. In Cummings, L. L. & Staw, B.M. (Eds.), Research in organizational behavior 6 (pp. 191-233). Homewood, IL: JAI Press.DePaulo, B. M., Epstein, J. A., & Wyer, M. M. (1993). Sex differences in lying: How women and men deal with the dilemma of deceit. In M. Lewis, & C. Saarni (Eds.), Lying and deception in everyday life (pp. 126-147). New York: Guilford Press.DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70(5), 979- 995. https://doi.org/10.1037/0022-3514.70.5.979DePaulo, B. M., Kirkendol, S. E., Tang, J., & O'Brien, T. P. (1988). The motivational impairment effect in the communication of deception: Replications and extensions. Journal of Nonverbal Behavior, 12(3), 177-202. https://doi.org/10.1007/BF00987487DePaulo, B. M., Lassiter, G. D., & Stone, J. L. (1982). Attention all determinants of success at detecting deception and truth. Personality and Social Psychology Bulletin, 8(2), 273-279. https://doi.org/10.1177/0146167282082014DePaulo, B. M., & Rosenthal, R. (1981). Telling lies. Journal of Personality and Social Psychology, 37(10), 1713-1722. https://doi.org/10.1037/0022-3514.37.10.1713Dreber, A., & Johannesson, M. (2008). Gender differences in deception. Economics Letters, 99(1), 197-199. https://doi.org/10.1016/j.econlet.2007.06.027Ekman, P., & O'Sullivan, M. (1991). Who can catch a liar? American Psychologist, 46(9), 913-920. https://doi.org/10.1037/0003-066X.46.9.913Ekman, P., O'Sullivan, M., & Frank, M. G. (1999). A few can catch a liar. Psychological Science, 10(3), 263-266. https://doi.org/10.1111/1467-9280.00147Feldman, R. S., Forrest, J. A., & Happ, B. R. (2002). Self-presentation and verbal deception: Do self-presenters lie more? Basic and Applied Social Psychology, 24(2), 163-170. https://doi.org/10.1207/153248302753674848George, J. F., & Robb, A. (2008). Deception and computer-mediated communication in daily life. Communication Reports, 21(2), 92-103. https://doi.org/10.1080/08934210802298108Hample, D. (1980). Purposes and effects of lying. Southern Speech Communication Journal, 46(1), 33-47. https://doi.org/10.1080/10417948009372474Hancock, J., Thom-Santelli, J., & Ritchie, T. (2004). Deception and design: The impact of communication technology on lying behavior. In E. Dykstra-Erickson, & M. Tscheligi (Eds.), Proceedings of the 2004 conference on human factors in computing systems (pp. 129-134). New York: Association for Computing Machinery.https://doi.org/10.1145/985692.985709Haselton, M. G., Buss, D. M., Oubaid, V., & Angleitner, A. (2005). Sex, lies, and strategic interference: The psychology of deception between the sexes. Personality and Social Psychology Bulletin, 31(1), 3-23. https://doi.org/10.1177/0146167204271303Inglehart, R., Basa-ez, M., & Moreno, A. (1998). Human values and beliefs: A crosscultural sourcebook. Ann Arbor, MI: University of Michigan Press. https://doi.org/10.3998/mpub.14858Knapp, L. M., Hart, R. P., & Dennis, H. S. (1974). An exploration of deception as a communication construct. Human Communication Research, 1(1), 15-29. https://doi.org/10.1111/j.1468-2958.1974.tb00250.xKraut, R. E. (1980). Behavioral roots of person perception: The deception judgments of customs inspectors and laymen. Journal of Personality and Social Psychology, 39(5), 784-798. https://doi.org/10.1037/0022-3514.39.5.784Kuzio, A. (2018). Cross-cultural Deception in Polish and American English in Computer-Mediated Communication. New Castle upon Tyne: Cambridge Scholars Publishing.Levine, T. R., & Kim, R. K. (2010). Some considerations for a new theory of deceptive communication. In M. S. McGlone, & M. L. Knapp (Eds.), The interplay of truth and deception: New agendas in theory and research (pp. 16-34). New York: Routledge.Levine, T. R., Park, H. S., & McCornack, S. A. (2006). Accuracy in detecting truths and lies: Documenting the "Veracity Effect". Communication Monographs, 66(2), 125- 144. https://doi.org/10.1080/03637759909376468Manstead, A., Wagner, H. L., & McDonald, C. J. (1986). Deceptive and non-deceptive communications: Sending experience, modality, and individual abilities. Journal of Nonverbal Behavior, 10(3), 147-167. https://doi.org/10.1007/BF00987612McCornack, S. A., & Parks, M. R. (1990). What women know that men don't: Sex differences in determining the truth behind deceptive messages. Journal of Social and Personal Relationships, 7(1), 107-118. https://doi.org/10.1177/0265407590071006Park, H. S., Levine, T. R., McCornack, S. A., Morrison, K., & Ferrara, M. (2002). How people really detect lies. Communication Monographs, 69(2), 144-157. https://doi.org/10.1080/714041710Prater, T., & Kiser, S. B. (2002). Lies, lies, and more lies. SAM Advanced Management Journal,67(2), 9-36.Sanchez-Pages, S., & Vorsatz, M. (2008). Enjoy the silence: An experiment on truthtelling. Experimental Economics, 12(2), 220-241. https://doi.org/10.1007/s10683-008-9211-7Seiter, J. S., Bruschke, J., & Bai, C. (2002). The acceptability of deception as a function of perceivers' culture, deceiver's intention, and deceiver-deceived relationship. Western Journal of Communication, 66(2), 158-180. https://doi.org/10.1080/10570310209374731Serota, K. B., Levine, T. R., & Boster, F. J. (2010). The prevalence of lying in America: Three studies of self-reported lies. Human Communication Research, 36(1), 2-25. https://doi.org/10.1111/j.1468-2958.2009.01366.xTurner, R. E., Edgley, C., & Olmstead, G. (1975). Information control in conversations: Honesty is not always the best policy. Kansas Journal of Sociology, 11(1), 69-89.https://doi.org/10.17161/STR.1808.6098Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and nonverbal communication of deception. In L. Berkowitz (Ed.), Advances in experimental social psychology (volume 11, pp. 1-59). New York: Academic Press.https://doi.org/10.1016/S0065-2601(08)60369-

On the use of stabilization techniques in the Cartesian grid finite element method framework for iterative solvers

"This is the peer reviewed version of the following article: Navarro-Jiménez, José Manuel, Enrique Nadal, Manuel Tur, José Martínez-Casas, and Juan José Ródenas. 2020. "On the Use of Stabilization Techniques in the Cartesian Grid Finite Element Method Framework for Iterative Solvers." International Journal for Numerical Methods in Engineering 121 (13). Wiley: 3004-20. doi:10.1002/nme.6344, which has been published in final form at https://doi.org/10.1002/nme.6344. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Fictitious domain methods, like the Cartesian grid finite element method (cgFEM), are based on the use of unfitted meshes that must be intersected. This may yield to ill-conditioned systems of equations since the stiffness associated with a node could be small, thus poorly contributing to the energy of the problem. This issue complicates the use of iterative solvers for large problems. In this work, we present a new stabilization technique that, in the case of cgFEM, preserves the Cartesian structure of the mesh. The formulation consists in penalizing the free movement of those nodes by a smooth extension of the solution from the interior of the domain, through a postprocess of the solution via a displacement recovery technique. The numerical results show an improvement of the condition number and a decrease in the number of iterations of the iterative solver while preserving the problem accuracy.The authors wish to thank the Spanish "Ministerio de Economía y Competitividad," the "Generalitat Valenciana," and the "Universitat Politècnica de València" for their financial support received through the projects DPI2017-89816-R, Prometeo 2016/007 and the FPI2015 program, respectively.Navarro-Jiménez, J.; Nadal, E.; Tur Valiente, M.; Martínez Casas, J.; Ródenas, JJ. (2020). On the use of stabilization techniques in the Cartesian grid finite element method framework for iterative solvers. International Journal for Numerical Methods in Engineering. 121(13):3004-3020. https://doi.org/10.1002/nme.6344S3004302012113Burman, E., & Hansbo, P. (2010). Fictitious domain finite element methods using cut elements: I. A stabilized Lagrange multiplier method. Computer Methods in Applied Mechanics and Engineering, 199(41-44), 2680-2686. doi:10.1016/j.cma.2010.05.011Ruiz-Gironés, E., & Sarrate, J. (2010). Generation of structured hexahedral meshes in volumes with holes. Finite Elements in Analysis and Design, 46(10), 792-804. doi:10.1016/j.finel.2010.04.005Geuzaine, C., & Remacle, J.-F. (2009). Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities. International Journal for Numerical Methods in Engineering, 79(11), 1309-1331. doi:10.1002/nme.2579Parvizian, J., Düster, A., & Rank, E. (2007). Finite cell method. Computational Mechanics, 41(1), 121-133. doi:10.1007/s00466-007-0173-yDüster, A., Parvizian, J., Yang, Z., & Rank, E. (2008). The finite cell method for three-dimensional problems of solid mechanics. Computer Methods in Applied Mechanics and Engineering, 197(45-48), 3768-3782. doi:10.1016/j.cma.2008.02.036Nadal, E., Ródenas, J. J., Albelda, J., Tur, M., Tarancón, J. E., & Fuenmayor, F. J. (2013). Efficient Finite Element Methodology Based on Cartesian Grids: Application to Structural Shape Optimization. Abstract and Applied Analysis, 2013, 1-19. doi:10.1155/2013/953786Nadal, E., Ródenas, J. J., Sánchez-Orgaz, E. M., López-Real, S., & Martí-Pellicer, J. (2014). Sobre la utilización de códigos de elementos finitos basados en mallados cartesianos en optimización estructural. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 30(3), 155-165. doi:10.1016/j.rimni.2013.04.009Giovannelli, L., Ródenas, J. J., Navarro-Jiménez, J. M., & Tur, M. (2017). Direct medical image-based Finite Element modelling for patient-specific simulation of future implants. Finite Elements in Analysis and Design, 136, 37-57. doi:10.1016/j.finel.2017.07.010Schillinger, D., & Ruess, M. (2014). The Finite Cell Method: A Review in the Context of Higher-Order Structural Analysis of CAD and Image-Based Geometric Models. Archives of Computational Methods in Engineering, 22(3), 391-455. doi:10.1007/s11831-014-9115-yBurman, E., Claus, S., Hansbo, P., Larson, M. G., & Massing, A. (2014). CutFEM: Discretizing geometry and partial differential equations. International Journal for Numerical Methods in Engineering, 104(7), 472-501. doi:10.1002/nme.4823Tur, M., Albelda, J., Marco, O., & Ródenas, J. J. (2015). Stabilized method of imposing Dirichlet boundary conditions using a recovered stress field. Computer Methods in Applied Mechanics and Engineering, 296, 352-375. doi:10.1016/j.cma.2015.08.001Tur, M., Albelda, J., Nadal, E., & Ródenas, J. J. (2014). Imposing Dirichlet boundary conditions in hierarchical Cartesian meshes by means of stabilized Lagrange multipliers. International Journal for Numerical Methods in Engineering, 98(6), 399-417. doi:10.1002/nme.4629De Prenter, F., Verhoosel, C. V., van Zwieten, G. J., & van Brummelen, E. H. (2017). Condition number analysis and preconditioning of the finite cell method. Computer Methods in Applied Mechanics and Engineering, 316, 297-327. doi:10.1016/j.cma.2016.07.006Berger-Vergiat, L., Waisman, H., Hiriyur, B., Tuminaro, R., & Keyes, D. (2011). Inexact Schwarz-algebraic multigrid preconditioners for crack problems modeled by extended finite element methods. International Journal for Numerical Methods in Engineering, 90(3), 311-328. doi:10.1002/nme.3318Menk, A., & Bordas, S. P. A. (2010). A robust preconditioning technique for the extended finite element method. International Journal for Numerical Methods in Engineering, 85(13), 1609-1632. doi:10.1002/nme.3032Dauge, M., Düster, A., & Rank, E. (2015). Theoretical and Numerical Investigation of the Finite Cell Method. Journal of Scientific Computing, 65(3), 1039-1064. doi:10.1007/s10915-015-9997-3Elfverson, D., Larson, M. G., & Larsson, K. (2018). CutIGA with basis function removal. Advanced Modeling and Simulation in Engineering Sciences, 5(1). doi:10.1186/s40323-018-0099-2Verhoosel, C. V., van Zwieten, G. J., van Rietbergen, B., & de Borst, R. (2015). Image-based goal-oriented adaptive isogeometric analysis with application to the micro-mechanical modeling of trabecular bone. Computer Methods in Applied Mechanics and Engineering, 284, 138-164. doi:10.1016/j.cma.2014.07.009Burman, E. (2010). Ghost penalty. Comptes Rendus Mathematique, 348(21-22), 1217-1220. doi:10.1016/j.crma.2010.10.006BadiaS VerdugoF MartínAF. The aggregated unfitted finite element method for elliptic problems;2017.Jomo, J. N., de Prenter, F., Elhaddad, M., D’Angella, D., Verhoosel, C. V., Kollmannsberger, S., … Rank, E. (2019). Robust and parallel scalable iterative solutions for large-scale finite cell analyses. Finite Elements in Analysis and Design, 163, 14-30. doi:10.1016/j.finel.2019.01.009Béchet, É., Moës, N., & Wohlmuth, B. (2008). A stable Lagrange multiplier space for stiff interface conditions within the extended finite element method. International Journal for Numerical Methods in Engineering, 78(8), 931-954. doi:10.1002/nme.2515Hautefeuille, M., Annavarapu, C., & Dolbow, J. E. (2011). Robust imposition of Dirichlet boundary conditions on embedded surfaces. International Journal for Numerical Methods in Engineering, 90(1), 40-64. doi:10.1002/nme.3306Hansbo, P., Lovadina, C., Perugia, I., & Sangalli, G. (2005). A Lagrange multiplier method for the finite element solution of elliptic interface problems using non-matching meshes. Numerische Mathematik, 100(1), 91-115. doi:10.1007/s00211-005-0587-4Burman, E., & Hansbo, P. (2012). Fictitious domain finite element methods using cut elements: II. A stabilized Nitsche method. Applied Numerical Mathematics, 62(4), 328-341. doi:10.1016/j.apnum.2011.01.008Gerstenberger, A., & Wall, W. A. (2008). An eXtended Finite Element Method/Lagrange multiplier based approach for fluid–structure interaction. Computer Methods in Applied Mechanics and Engineering, 197(19-20), 1699-1714. doi:10.1016/j.cma.2007.07.002AxelssonO. Iterative solution methods;1994.Stenberg, R. (1995). On some techniques for approximating boundary conditions in the finite element method. Journal of Computational and Applied Mathematics, 63(1-3), 139-148. doi:10.1016/0377-0427(95)00057-7Zienkiewicz, O. C., & Zhu, J. Z. (1987). A simple error estimator and adaptive procedure for practical engineerng analysis. International Journal for Numerical Methods in Engineering, 24(2), 337-357. doi:10.1002/nme.1620240206Zienkiewicz, O. C., & Zhu, J. Z. (1992). The superconvergent patch recovery anda posteriori error estimates. Part 1: The recovery technique. International Journal for Numerical Methods in Engineering, 33(7), 1331-1364. doi:10.1002/nme.1620330702Blacker, T., & Belytschko, T. (1994). Superconvergent patch recovery with equilibrium and conjoint interpolant enhancements. International Journal for Numerical Methods in Engineering, 37(3), 517-536. doi:10.1002/nme.1620370309Díez, P., José Ródenas, J., & Zienkiewicz, O. C. (2007). Equilibrated patch recovery error estimates: simple and accurate upper bounds of the error. International Journal for Numerical Methods in Engineering, 69(10), 2075-2098. doi:10.1002/nme.1837Xiao, Q. Z., & Karihaloo, B. L. (s. f.). Statically Admissible Stress Recovery using the Moving Least Squares Technique. Progress in Computational Structures Technology, 111-138. doi:10.4203/csets.11.5Ródenas, J. J., Tur, M., Fuenmayor, F. J., & Vercher, A. (2007). Improvement of the superconvergent patch recovery technique by the use of constraint equations: the SPR-C technique. International Journal for Numerical Methods in Engineering, 70(6), 705-727. doi:10.1002/nme.1903Zhang, Z. (2001). Advances in Computational Mathematics, 15(1/4), 363-374. doi:10.1023/a:1014221409940González-Estrada, O. A., Nadal, E., Ródenas, J. J., Kerfriden, P., Bordas, S. P. A., & Fuenmayor, F. J. (2013). Mesh adaptivity driven by goal-oriented locally equilibrated superconvergent patch recovery. Computational Mechanics, 53(5), 957-976. doi:10.1007/s00466-013-0942-8Nadal, E., Díez, P., Ródenas, J. J., Tur, M., & Fuenmayor, F. J. (2015). A recovery-explicit error estimator in energy norm for linear elasticity. Computer Methods in Applied Mechanics and Engineering, 287, 172-190. doi:10.1016/j.cma.2015.01.013ZienkiewiczOC TaylorRL. The finite element method fifth edition volume 1: the basis.MA:Butterworth‐Heinemann;2000.Brenner, S. C., & Scott, L. R. (1994). The Mathematical Theory of Finite Element Methods. Texts in Applied Mathematics. doi:10.1007/978-1-4757-4338-

Revisión de los métodos computerizados para la reconstrucción de fragmentos arqueológicos de cerámica

[ES] Las cerámicas son los hallazgos más numerosos encontrados en las excavaciones arqueológicas; a menudo se usan para obtener información sobre la historia, la economía y el arte de un sitio. Los arqueólogos rara vez encuentran jarrones completos; en general, están dañados y en fragmentos, a menudo mezclados con otros grupos de cerámica.El análisis y la reconstrucción de fragmentos se realiza por un operador experto mediante el uso del método manual tradicional. Los artículos revisados proporcionaron evidencias de que el método tradicional no es reproducible, no es repetible, consume mucho tiempo y sus resultados generan grandes incertidumbres. Con el objetivo de superar los límites anteriores, en los últimos años, los investigadores han realizado esfuerzos para desarrollar métodos informáticos que permitan el análisis de fragmentos arqueológicos de cerámica, todo ello destinado a su reconstrucción. Para contribuir a este campo de estudio, en este artículo, se presenta un análisis exhaustivo de las publicaciones disponibles más importantes hasta finales de 2019. Este estudio, centrado únicamente en fragmentos de cerámica, se realiza mediante la recopilación de artículos en inglés de la base de datos Scopus, utilizando las siguientes palabras clave: "métodos informáticos en arqueología", "arqueología 3D", "reconstrucción 3D", "reconocimiento y reconstrucción automática de características", "restauración de reliquias en forma de cerámica ". La lista se completa con referencias adicionales que se encuentran a través de la lectura de documentos seleccionados. Los 53 trabajos seleccionados se dividen en tres períodos de tiempo. Según una revisión detallada de los estudios realizados, los elementos clave de cada método analizado se enumeran en función de las herramientas de adquisición de datos, las características extraídas, los procesos de clasificación y las técnicas de correspondencia. Finalmente, para superar las brechas reales, se proponen algunas recomendaciones para futuras investigaciones.[EN] Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.Highlights:The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.Eslami, D.; Di Angelo, L.; Di Stefano, P.; Pane, C. (2020). Review of computer-based methods for archaeological ceramic sherds reconstruction. Virtual Archaeology Review. 11(23):34-49. https://doi.org/10.4995/var.2020.13134OJS34491123Andrews, S., & Laidlaw, D. H. (2002). Toward a framework for assembling broken pottery vessels. In Proceedings of the National Conference on Artificial Intelligence, (August 2003), (pp. 945-946).Banterle, F., Itkin, B., Dellepiane, M., Wolf, L., Callieri, M., Dershowitz, N., & Scopigno, R. (2017). VASESKETCH: Automatic 3D Representation of Pottery from Paper Catalog Drawings. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1(693548), (pp. 683-690). https://doi.org/10.1109/ICDAR.2017.117Belenguer, C. S., & Vidal, E. V. (2012). Archaeological fragment characterization and 3D reconstruction based on projective GPU depth maps. In Proceedings of the 2012 18th International Conference on Virtual Systems & Multimedia, VSMM 2012: Virtual Systems in the Information Society, (pp. 275-282). https://doi.org/10.1109/VSMM.2012.6365935Blender. (2018). An open-source 3D graphics and animation software. Retrieved from https://www.blender.orgBrown, B. J., Toler-Franklin, C., Nehab, D., Burns, M., Dobkin, D., Vlachopoulos, A., Weyrich, T. (2008). A system for high-volume acquisition and matching of fresco fragments: Reassembling Theran wall paintings. ACM Transactions on Graphics, 27(3). https://doi.org/10.1145/1360612.1360683Cao, Y., & Mumford, D. (2002). Geometric Structure Estimation of Axially Symmetric Pots from Small Fragments. In Proceedings of the signal processing, pattern recognition and applications, IASTED, Crete, Greece, June 25-28, 2002, (pp. 92-97).Cohen, F., Zhang, Z., & Jeppson, P. (2010). Virtual reconstruction of archaeological vessels using convex hulls of surface markings. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, (pp. 55-61). http://dx.doi.org/10.1109/CVPRW.2010.5543528Cohen, F., Zhang, Z., & Liu, Z. (2016). Mending broken vessels a fusion between color markings and anchor points on surface breaks. Multimedia Tools and Applications, 75(7), 3709-3732. https://doi.org/10.1007/s11042-014-2190-0Cooper, D. B., Willis, A., Andrews, S., Baker, J., Cao, Y., Han, D., … others. (2001). Assembling virtual pots from 3D measurements of their fragments. In Proceedings of the 2001 Conference on Virtual Reality, Archeology, and Cultural Heritage, (pp. 241-254). https://doi.org/10.1145/584993.585032Di Angelo, L., Di Stefano, P., Morabito, A. E., & Pane, C. (2018). Measurement of constant radius geometric features in archaeological pottery. Measurement: Journal of the International Measurement Confederation, 124 (March), 138-146. https://doi.org/10.1016/j.measurement.2018.04.016Di Angelo, L., Di Stefano, P., & Pane, C. (2018). An automatic method for pottery fragments analysis. Measurement: Journal of the International Measurement Confederation, 128, 138-148. https://doi.org/10.1016/j.measurement.2018.06.008Di Angelo, Luca, Di Stefano, P., & Pane, C. (2017). Automatic dimensional characterization of pottery. Journal of Cultural Heritage, 26, 118-128. https://doi.org/10.1016/j.culher.2017.02.003Fragkos, S., Tzimtzimis, E., Tzetzis, D., Dodun, O., & Kyratsis, P. (2018). 3D laser scanning and digital restoration of an archaeological find. MATEC Web of Conferences, 178. https://doi.org/10.1051/matecconf/201817803013Funkhouser, T., Shin, H., Toler-Franklin, C., Castañeda, A. G., Brown, B., Dobkin, D., Weyrich, T. (2011). Learning how to match fresco fragments. Journal on Computing and Cultural Heritage, 4(2). https://doi.org/10.1145/2037820.2037824Halir, R., & Menard, C. (1996). Diameter estimation for archaeological pottery using active vision. In Proceedings of the 20th Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) on Pattern Recognition 1996, (pp. 251-261).Halir, R., & Flusser, J. (1997). Estimation of profiles of sherds of archaeological pottery. In Proceedings of the of the Czech Pattern Recognition Workshop (CPRW'97), Czech Republic, February 1997, 1-5, (pp. 126-130).Halir, R. (1999). An Automatic Estimation Of The Axis Of Rotation Of Fragments Of Archaeological Pottery: A Multi-Step Model-Based Approach. In Proceedings of the 7th International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media (WSCG '99) https://semanticscholar.org/0248/ae5a8dca3d2c6bfff282ce481a5625d32362Hall, N. S., & Laflin, S. (1984). A computer aided design technique for pottery profiles. In Computer applications in Archaeology, (pp. 178-188). Computer Center, University of Birmingham Birmingham. Retrieved from https://www.bcin.ca/bcin/detail.app?id=40524Han, D., & Hahn, H. S. (2014). Axis estimation and grouping of rotationally symmetric object segments. Pattern Recognition, 47(1), 296-312. https://doi.org/10.1016/j.patcog.2013.06.022Hlavackova-Schindler, K., Kampel, M., & Sablatnig, R. (2001). Fitting of a Closed Planar Curve Representing a Profile of an Archaeological Fragment. In Proceedings VAST 2001 Virtual Reality, Archeology, and Cultural Heritage, (pp. 263-269). https://doi.org/10.1145/585031.585034Huang, Q. X., Flöry, S., Gelfand, N., Hofer, M., & Pottmann, H. (2006). Reassembling fractured objects by geometric matching. ACM SIGGRAPH 2006 Papers, SIGGRAPH '06, (May), (pp. 569-578). https://doi.org/10.1145/1179352.1141925Igwe, P. C., & Knopf, G. K. (2006). 3D object reconstruction using geometric computing. Geometric Modeling and Imaging New Trends, 9-14. https://doi.org/10.1109/GMAI.2006.1Kalasarinis, I., & Koutsoudis, A. (2019). Assisting pottery restoration procedures with digital technologies. International Journal of Computational Methods in Heritage Science, 3(1), 20-32. https://doi.org/10.4018/ijcmhs.2019010102Kampel, M., & Sablatnig, R. (2003). Profile-based Pottery Reconstruction. In IEEE Proceeding of Conference on Computer Vision and Pattern Recognition Workshops, Wisconsin, June, (pp. 1-6). https://doi.org/10.1109/CVPRW.2003.10007Kampel, M, & Mara, H. (2005). Robust 3D reconstruction of archaeological pottery based on concentric circular rills. In Proceedings of the Sixth International. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'05), Montreux, Switzerland, (pp. 14-20). Retrieved from https://semanticscholar.org/43df/9b3c6fef5aa54964bdc4825a86cc4e9f4531Kampel, M., & Sablatnig, R. (2003). An automated pottery archival and reconstruction system. Journal of Visualization and Computer Animation, 14(3), 111-120. https://doi.org/10.1002/vis.310Kampel, M., & Sablatnig, R. (2004). 3D Puzzling of Archeological Fragments. In Proceedings of 9th Computer Vision Winter Workshop, (February), (pp. 31-40). Retrieved from https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/cvww041Karasik, A., & Smilansky, U. (2011). Computerized morphological classification of ceramics. Journal of Archaeological Science, 38(10), 2644-2657. https://doi.org/10.1016/j.jas.2011.05.023Kashihara, K. (2012). Three-dimensional reconstruction of artifacts based on a hybrid genetic algorithm. In IEEE International Conference on Systems, Man and Cybernetics, (pp. 900-905). https://doi.org/10.1109/ICSMC.2012.6377842Kashihara, K. (2017). An intelligent computer assistance system for artifact restoration based on genetic algorithms with plane image features. International Journal of Computational Intelligence and Applications, 16(3), 1-15. https://doi.org/10.1142/S1469026817500213Kleber, F., & Sablatnig, R. (2009). A survey of techniques for document and archaeology artifact reconstruction. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, (March 2014), (pp. 1061-1065). https://doi.org/10.1109/ICDAR.2009.154Kotoula, E. (2016). Semiautomatic fragments matching and virtual reconstruction: a case study on ceramics. International Journal of Conservation Science, 7(1), 71-86. Retrieved from http://eprints.lincoln.ac.uk/id/eprint/31035/Lucena, M., Martínez-Carrillo, A. L., Fuertes, J. M., Javier Carrascosa Malagón, F., & Ruiz Rodríguez, A. (2016). Decision support system for classifying archaeological pottery profiles based on mathematical morphology. Multimedia Tools and Applications, 75(7), 3677-3691. https://doi.org/10.1007/s11042-014-2063-6Maiza, C., & Gaildrat, V. (2005). Automatic classification of archaeological potsherds. In Proceedings of the 8th International Conference on Computer Graphics and Artificial Intelligence, Limoges, France, May 11-12, 2005, (pp. 135-147). https://semanticscholar.org/3c95/82c3e562b44e7d61dc0fd3487ea3dc977ff3Mara, H., Kampel, M., & Sablatnig, R. (2002). Preprocessing of 3D-Data for Classification of Archaeological Fragments in an Automated System. In Proceedings of the 26th Workshop of the Austrian Association for Pattern Recognition, Vision with Non-Traditional Sensors, (ÖAGM/AAPR), Graz, Austria, 10-11 September 2002, (pp. 257-264). https://doi.org/10.1.1.15.748Mara, H., & Sablatnig, R. (2006). The orientation of fragments of rotationally symmetrical 3D-shapes for archaeological documentation. In Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006, (June), (pp. 1064-1071). https://doi.org/10.1109/3DPVT.2006.105Melero, F. J., Torres, J. C., & Leon, A. (2003). On the interactive 3d reconstruction of Iberian vessels. In 4th International Symposium on Virtual Reality, Archaeology, and Intelligent Cultural Heritage, VAST, 3, (pp. 71-78). http://dx.doi.org/10.2312/VAST/VAST03/071-078Papaioannou, G., Karabassi, E. a., & Theoharis, T. (2000). Automatic Reconstruction of Archaeological Finds-A Graphics Approach. In International Conference on Computer Graphics and Artificial Intelligence, (March), (pp. 117-125). Retrieved from https://semanticscholar.org/6a3c/7ec8f544bbfb83174d868cd406eaaf40f438Papaioannou, G., Karabassi, E. A., & Theoharis, T. (2002). Reconstruction of three-dimensional objects through the matching of their parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 114-124. https://doi.org/10.1109/34.982888Pulli, K. (1999). Multiview registration for large data sets. In Proceedings of Second International Conference on 3D Digital Imaging and Modeling, Ottawa, ON, Canada, 4-8 December 1999, (pp. 160-168). http://doi.org/10.1109/IM.1999.805346Rasheed, N. A., & Nordin, J. (2015a). A Survey of Computer Methods in Reconstruction of 3D Archaeological Pottery Objects. International Journal of Advanced Research, 3(3), 712-714. Retrieved from https://academia.edu.documents/45540231Rasheed, N. A., & Nordin, M. J. (2014). A polynomial function in the automatic reconstruction of fragmented objects. Journal of Computer Science, 10(11), 2339-2348. https://doi.org/10.3844/jcssp.2014.2339.2348Rasheed, N. A., & Nordin, M. J. (2015b). Archaeological fragments classification based on RGB color and texture features. Journal of Theoretical and Applied Information Technology, 76(3), 358-365. Retrieved from http://repository.uobabylon.edu.iq/papers/publication.aspx?pubid=6746Rasheed, N. A., & Nordin, M. J. (2018). Classification and reconstruction algorithms for the archaeological fragments. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.019Rasheed, N. A., Nordin, M. J., Dakheel, A. H., Nados, W. L., & Maaroof, M. K. A. (2017). Classification archaeological fragments into groups. Research Journal of Applied Sciences, Engineering, and Technology, 14(9), 324-333. https://doi.org/10.19026/rjaset.14.5072Sablatnig, R., & Menard, C. (1997). 3D Reconstruction of Archaeological Pottery using Profile Primitives. In Proceedings of I International Workshop on Synthetic-Natural Hybrid Coding and Three-Dimensional Imaging, (pp. 93-96).Sablatnig, R., Menard, C., & Kropatseh, W. (1998). Classification of archaeological fragments using a description language. In Proceedings of European Signal Processing Conference, (Eusipco '98), (pp. 1097-1100), 1998.Sakpere, W. (2019). 3D Reconstruction of Archaeological Pottery from Its Point Cloud. In Proceedings of Iberian Conference on Pattern Recognition and Image Analysis, (pp. 125-136). https://doi.org/10.1007/978-3-030-31332-6_11Shin, H., Doumas, C., Funkhouser, T., Rusinkiewicz, S., Steiglitz, K.,Vlachopoulos, & Weyrich, T. (2010). Analyzing Fracture Patterns in Theran Wall Paintings. In Proceedings of the 11th International Symposium on Virtual Reality, Archaeology - VAST, (pp. 71-78). https://doi.org/10.2312/VAST/VAST10/071-078Son, K., Almeida, E. B., & Cooper, D. B. (2013). Axially symmetric 3D pots configuration system using the axis of symmetry and break curve. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 257-264). https://doi.org/10.1109/CVPR.2013.40Stamatopoulos, M. I., & Anagnostopoulos, C.-N. (2016). 3D digital reassembling of archaeological ceramic pottery fragments based on their thickness profile. The Computing Research Repository (CoRR). Retrieved from https://arxiv.org/abs/1601.05824Toler-Franklin, C., Funkhouser, T., Rusinkiewicz, S., Brown, B., & Weyrich, T. (2010). Multi-Feature Matching of Fresco Fragments. ACM Transactions on Graphics, 29(6), 1-12. https://doi.org/10.1145/1882261.1866207Üçoluk, G., & Hakki Toroslu, I. (1999). Automatic reconstruction of broken 3-D surface objects. Computers and Graphics, 23(4), 573-582. https://doi.org/10.1016/S0097-8493(99)00075-8Vendrell-Vidal, E., & Sánchez-Belenguer, C. (2014). A Discrete Approach for Pairwise Matching of Archaeological Fragments. Journal on Computing and Cultural Heritage, 7(3), 1-19. https://doi.org/10.1145/2597178Willis, A., Orriols, X., & Cooper, D. B. (2003). Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (16-22 June 2003), Madison, Wisconsin, USA (pp. 1-7). https://doi.org/10.1109/CVPRW.2003.10014Willis, A. R., & Cooper, D. B. (2004). Bayesian assembly of 3D axially symmetric shapes from fragments. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, (pp. 82-89). https://doi.org/10.1109/cvpr.2004.1315017Zhou, Mingquam, Geng, G., Wu, Z., Zheng, X., Shui, W., Lu, K., & Gao, Y. (2007). A system for re-assembly of fragment objects and computer-aided restoration of cultural relics. Virtual Retrospect 2007, 3, 21-27. Retrieved from http://hal.univ-savoie.fr/ENIB/hal-01765241v1Zhou, Mingquan, Geng, G., Wu, Z., & Shui, W. (2010). A Virtual Restoration System for Broken Pottery. In Proceedings of the CAA Conference 37th Computer applications and quantitative methods in archaeology, Williamsburg, VA, USA, 22-26 March 2009; (pp. 391-396). Retrieved from https://semanticscholar.org/87b5/aa5c7710806677abbedb4e43f6134e05304

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

Key Steps in Developing a Cognitive Vaccine against Traumatic Flashbacks: Visuospatial Tetris versus Verbal Pub Quiz

Background: Flashbacks (intrusive memories of a traumatic event) are the hallmark feature of Post Traumatic Stress Disorder, however preventative interventions are lacking. Tetris may offer a 'cognitive vaccine' [1] against flashback development after trauma exposure. We previously reported that playing the computer game Tetris soon after viewing traumatic material reduced flashbacks compared to no-task [1]. However, two criticisms need to be addressed for clinical translation: (1) Would all games have this effect via distraction/enjoyment, or might some games even be harmful? (2) Would effects be found if administered several hours post-trauma? Accordingly, we tested Tetris versus an alternative computer game - Pub Quiz - which we hypothesized not to be helpful (Experiments 1 and 2), and extended the intervention interval to 4 hours (Experiment 2).Methodology/Principal Findings: The trauma film paradigm was used as an experimental analog for flashback development in healthy volunteers. In both experiments, participants viewed traumatic film footage of death and injury before completing one of the following: (1) no-task control condition (2) Tetris or (3) Pub Quiz. Flashbacks were monitored for 1 week. Experiment 1: 30 min after the traumatic film, playing Tetris led to a significant reduction in flashbacks compared to no-task control, whereas Pub Quiz led to a significant increase in flashbacks. Experiment 2: 4 hours post-film, playing Tetris led to a significant reduction in flashbacks compared to no-task control, whereas Pub Quiz did not.Conclusions/Significance: First, computer games can have differential effects post-trauma, as predicted by a cognitive science formulation of trauma memory. In both Experiments, playing Tetris post-trauma film reduced flashbacks. Pub Quiz did not have this effect, even increasing flashbacks in Experiment 1. Thus not all computer games are beneficial or merely distracting post-trauma - some may be harmful. Second, the beneficial effects of Tetris are retained at 4 hours post-trauma. Clinically, this delivers a feasible time-window to administer a post-trauma "cognitive vaccine''