117,871 research outputs found
A. ROSSO, E. DI MARTINO & V. GEROVASILEIOU (2020) Revision of the genus Setosella (Bryozoa Cheilostomata) with description of new species from deep-waters and submarine caves of the Mediterranean Sea. Zootaxa, 4728: 401-442.
No full textRosso, A., Martino, E. Di, Gerovasileiou, V. (2020): A. ROSSO, E. DI MARTINO & V. GEROVASILEIOU (2020) Revision of the genus Setosella (Bryozoa Cheilostomata) with description of new species from deep-waters and submarine caves of the Mediterranean Sea. Zootaxa, 4728: 401-442. Zootaxa 4803 (3): 600-600, DOI: https://doi.org/10.11646/zootaxa.4803.3.1
Author Profiling and Plagiarism Detection
No full textThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25485-2_6In this chapter we introduce the topics that we will cover in the RuSSIR 2014 course on Author Profiling and Plagiarism Detection (APPD). Author profiling distinguishes between classes of authors studying how language is shared by classes of people. This task helps in identifying profiling aspects such as gender, age, native language, or even personality type. In case of the plagiarism detection task we are not interested in studying how language is shared. On the contrary, given a document we are interested in investigating if the writing style changes in order to unveil text inconsistencies, i.e., unexpected irregularities through the document such as changes in vocabulary, style and text complexity. In fact, when it is not possible to retrieve the source document(s) where plagiarism has been committed from, the intrinsic analysis of the suspicious document is the only way to find evidence of plagiarism. The difficulty in retrieving the source of plagiarism could be due to the fact that the documents are not available on the web or the plagiarised text fragments were obfuscated via paraphrasing or translation (in case the source document was in another language). In this overview, we also discuss the results of the shared tasks on author profiling (gender and age identification) and plagiarism detection that we help to organise at the PAN Lab on Uncovering Plagiarism, Authorship, and Social Software Misuse.The PAN shared tasks on author profil-ing and on plagiarism detection have been organised in the framework of the WIQ-EIIRSES project (Grant No. 269180) within the EC FP 7 Marie Curie People. The research work described in the paper was carried out in the framework of the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction inIntelligent Systems.Rosso, P. (2015). Author Profiling and Plagiarism Detection. En Information Retrieval. Springer. 229-250. https://doi.org/10.1007/978-3-319-25485-2_6S229250Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. TEXT 23, 321–346 (2003)Association of Teachers and Lecturers. School work plagued by plagiarism - ATL survey. Technical report, Association of Teachers and Lecturers, London, UK (2008). (Press release)Barrón-Cedeño, A.: On the mono- and cross-language detection of text re-use and plagiarism. Ph.D. thesis, Universitat Politènica de València (2012)Barrón-Cedeño, A., Rosso, P., Pinto, D., Juan, A.: On cross-lingual plagiarism analysis using a statistical model. In: Proceedings of the ECAI 2008 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, PAN 2008 (2008)Barrón-Cedeño, A., Gupta, P., Rosso, P.: Methods for cross-language plagiarism detection. Knowl. Based Syst. 50, 11–17 (2013)Barrón-Cedeño, A., Vila, M., Martí, M., Rosso, P.: Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Comput. Linguist. 39(4), 917–947 (2013)Bogdanova, D., Rosso, P., Solorio, T.: Exploring high-level features for detecting cyberpedophilia. Comput. Speech Lang. 28(1), 108–120 (2014)Braschler, M., Harman, D.: Notebook papers of CLEF 2010 LABs and workshops. Padua, Italy (2010)Cappellato, L., Ferro, N., Halvey, M., Kraaij, W.: CLEF 2014 labs and workshops, notebook papers. In: CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613–0073 (2014). http://ceur-ws.org/Vol-1180/Comas, R., Sureda, J., Nava, C., Serrano, L.: Academic cyberplagiarism: a descriptive and comparative analysis of the prevalence amongst the undergraduate students at Tecmilenio University (Mexico) and Balearic Islands University (Spain). In: Proceedings of the International Conference on Education and New Learning Technologies (EDULEARN 2010), Barcelona (2010)Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221–233 (1948)Flores, E., Barrón-Cedeño, A., Rosso, P., Moreno, L.: Desocore: detecting source code re-use across programming languages. In: Proceedings of 12th International Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-2012, pp. 1–4, Montreal, Canada (2012)Flores, E., Barrón-Cedeño, A., Moreno, L., Rosso, P.: Uncovering source code re-use in large-scale programming environments. In: Computer Applications in Engineering and Education, Accepted (2014). doi: 10.1002/cae.21608Forner, P., Navigli, R., Tufis, D.: CLEF 2013 evaluation labs and workshop - working notes papers, 23–26 September. Valencia, Spain (2013)Franco-Salvador, M., Gupta, P., Rosso, P.: Cross-Language plagiarism detection using a multilingual semantic network. In: Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E., Serdyukov, P. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 710–713. Springer, Heidelberg (2013)Franco-Salvador, M., Gupta, P., Rosso, P.: Knowledge graphs as context models: improving the detection of cross-language plagiarism with paraphrasing. In: Ferro, N. (ed.) PROMISE Winter School 2013. LNCS, vol. 8173, pp. 227–236. Springer, Heidelberg (2014)Gollub, T., Stein, B., Burrows, S.: Ousting Ivory tower research: towards a web framework for providing experiments as a service. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M., (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), pp. 1125–1126. ACM, August 2012. ISBN 978-1-4503-1472-5. doi: 10.1145/2348283.2348501Gollub, T., Hagen, M., Michel, M., Stein, B.: From keywords to keyqueries: content descriptors for the web. In: Gurrin, C., Jones, G., Kelly, D., Kruschwitz, U., de Rijke, M., Sakai, T., Sheridan, P., (eds.) 36th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2013), pp. 981–984. ACM (2013)Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers’ age and gender. In: Adar, E., Hurst, M., Finin, T., Glance, N.S., Nicolov, N., Tseng, B.L., (eds.) ICWSM. The AAAI Press (2009)Gressel, G., Hrudya, P., Surendran, K., Thara, S., Aravind, A., Prabaharan, P.: Ensemble Learning Approach for Author Profiling-Notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Grozea, C., Popescu, M.: ENCOPLOT - performance in the Second International Plagiarism Detection Challenge lab report for PAN at CLEF 2010. In: Braschler and Harman [8]Grozea, C., Gehl, C., Popescu, M.: ENCOPLOT: pairwise sequence matching in linear time applied to plagiarism detection. In: Stein et al., (ed.) Overview of the 1st International Competition on Plagiarism Detection, pp. 10–18 (2009)Gunning, R.: The Technique of Clear Writing. McGraw-Hill Int. Book Co, New York (1952)Gupta, P., Barrón-Cedeño, A., Rosso, P.: Cross-language high similarity search using a conceptual thesaurus. In: Catarci, T., Peñas, A., Santucci, G., Forner, P., Hiemstra, D. (eds.) CLEF 2012. LNCS, vol. 7488, pp. 67–75. Springer, Heidelberg (2012)Honore, A.: Some simple measures of richness of vocabulary. Assoc. Lit. Linguist. Comput. Bull. 7(2), 172–177 (1979)IEEE. A Plagiarism FAQ. http://www.ieee.org/publications_standards/publications/rights/plagiarism_FAQ.html (2008). Published: 2008; Last Accessed 25 November 2012Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Lit. Linguist. Comput. 17(4), 401–412 (2002)Liau, Y., Vrizlynn, L.: Submission to the author profiling competition at pan-2014. In: Proceedings Recent Advances in Natural Language Processing III (2014). http://www.webis.de/research/events/pan-14Lopez-Monroy, A.P., Montes-Y-Gomez, M., Escalante, H.J., Villaseñor-Pineda, L., Villatoro-Tello, E.: INAOE’s participation at PAN 2013: author profiling task–notebook for PAN at CLEF 2013. In: Forner, et al. [14]Pastor López-Monroy, A., Montes y Gómez, M., Escalante, H.J., Villaseñor-Pineda, L.: Using Intra-profile information for author profiling-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Maharjan, S., Shrestha, P., Solorio, T.: A simple approach to author profiling in MapReduce–notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Marquardt, J., Fanardi, G., Vasudevan, G., Moens, M.F., Davalos, S., Teredesai, A., De Cock, M.: Age and gender identification in social media-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Martin, B.: Plagiarism: policy against cheating or policy for learning? Nexus (Newsl. Aust. Sociol. Assoc.) 16(2), 15–16 (2004)Mcnamee, P., Mayfield, J.: Character n-gram tokenization for european language text retrieval. Inf. Retr. 7(1), 73–97 (2004)Meina, M., Brodzinska, K., Celmer, B., Czokow, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based classification for author profiling using various features-notebook for PAN at CLEF 2013. In: Forner, et al. [14]Eissen, S.M., Stein, B.: Intrinsic plagiarism detection. In: Tombros, A., Yavlinsky, A., Rüger, S.M., Tsikrika, T., Lalmas, M., MacFarlane, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 565–569. Springer, Heidelberg (2006)Montes y Gómez, M., Gelbukh, A.F., López-López, A., Baeza-Yates, R.A.: Flexible comparison of conceptual graphs. In: Proceedings DEXA, pp. 102–111 (2001)Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)Nawab, R.M.A., Stevenson, M., Clough, P.: University of sheffield lab report for pan at clef 2010. In: Braschler and Harman [8]Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”; a study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)Oberreuter, G., Eiselt, A.: Submission to the 6th international competition on plagiarism detection, From Innovand.io, Chile (2014). http://www.webis.de/research/events/pan-14Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29(1), 19–51 (2003)Palkovskii, Y., Belov, A.: Developing high-resolution universal multi-type N-Gram plagiarism detector-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological aspects of natural language use: our words, our selves. Ann. Rev. Psychol. 54(1), 547–577 (2003)Potthast, M., Stein, B., Barrón-Cedeño, A., Rosso, P.: An evaluation framework for plagiarism detection. In: COLING 2010: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 997–1005 (2010)Potthast, M., Stein, B., Anderka, M.: A wikipedia-based multilingual retrieval model. In: Plachouras, V., Macdonald, C., Ounis, I., White, R.W., Ruthven, I. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 522–530. Springer, Heidelberg (2008)Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.:. Overview of the 1st international competition on plagiarism detection. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E., (eds.) Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 1–9, 2009. CEUR-WS.org (September 2009). http://ceur-ws.org/Vol-502Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd International Competition on Plagiarism Detection. In: Braschler and Harman [8]Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd international competition on plagiarism detection. In: Braschler, M., Harman, D., Pianta, E., (eds.) Working Notes Papers of the CLEF 2010 Evaluation Labs (September 2010) 2010. http://www.clef-initiative.eu/publication/working-notesPotthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Lang. Resour. Eval. 45(1), 45–62 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Petras, V., Forner, P., Clough, P., (eds.) Working Notes Papers of the CLEF 2011 Evaluation Labs (September 2011) (2011). http://www.clef-initiative.eu/publication/working-notesPotthast, M., Gollub, T., Hagen, M., Grabegger, J., Kiesel, J., Michel, M., Oberlander, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th international competition on plagiarism detection. In: Forner, P., Karlgren, J., Womser-Hacker, C., (eds.) Working Notes Papers of the CLEF 2012 Evaluation Labs (September 2012) (2012). http://www.clef-initiative.eu/publication/working-notesPotthast, M., Hagen, M., Stein, B., Grabegger, J., Michel, M., Tippmann, M., Welsch, C.: Chatnoir: a search engine for the clueweb09 corpus. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M., (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), p. 1004 (2012)Potthast, M., Gollub, T., Hagen, M., Tippmann, M., Kiesel, J., Rosso, P., Stamatatos, E., Stein, B.: Overview of the 5th international competition on plagiarism detection. In: Forner, et al. [14]Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th International Competition on Plagiarism Detection. In: Cappellato, et al. [9]Pouliquen, B., Steinberger, R., Ignat, C.: Automatic linking of similar texts across languages. In: Proceedings of Recent Advances in Natural Language Processing III, RANLP 2003, pp. 307–316 (2003)Prakash, A., Saha, S.: Experiments on document chunking and query formation for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013–notebook for PAN at CLEF 2013. In: Forner, et al. [14]Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkman, M., Stein, B., Verhoeven, B., Daelemans, W.: Overview of the 2nd author profiling task at PAN 2014–notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Sanchez-Perez, M., Sidorov, G., Gelbukh, A.: A winning approach to text alignment for text reuse detection at PAN 2014-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 199–205. AAAI (2006)Stamatatos, E.: Intrinsic plagiarism detection using character n-gram profiles. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E., (eds.) Proceedings of the SEPLN09 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 38–46, 2009. CEUR-WS.org, September 2009. http://ceur-ws.org/Vol-502Stein, B., Meyer zu Eissen, S., Potthast, M.: Strategies for retrieving plagiarized documents. In: Clarke, C., Fuhr, N., Kando, N., Kraaij, W., de Vries, A., (eds.) 30th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 825–826. ACM (2007)Stein, B., Potthast, M., Rosso, P., Barrón-Cedeño, A., Stamatatos, E., Koppel, M.: Fourth international workshop on uncovering plagiarism, authorship, and social software misuse. ACM SIGIR Forum 45, 45–48 (2011)Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., Varga, D.: The jrc-acquis: a multilingual aligned parallel corpus with +20 languages. In: Proceedings of 5th International Conference on language resources and evaluation LREC 2006 (2006)Suchomel, S., Brandejs, M.: Heterogeneous queries for synoptic and phrasal search-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Villena-Román, J., González-Cristóbal, J.C.: DAEDALUS at PAN 2014: Guessing Tweet Author’s Gender and Age-Notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Vossen, P.: Eurowordnet: a multilingual database of autonomous and language-specific wordnets connected via an inter-lingual index. Int. J. Lexicography 17, 161–173 (2004)Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792 (2010)Weren, E.R.D., Moreira, V.P., de Oliveira, J.P.M.:. Exploring information retrieval features for author profiling-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Williams, K., Chen, H.H., Giles, C.: Supervised ranking for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Yule, G.: The Statistical Study of Literary Vocabulary. Cambridge University press, Cambridge (1944)Zubarev, D., Sochenkov, I.: Using sentence similarity measure for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, L., et al. [9
Author Profiling Tracks at FIRE
No full text[EN] Benchmarking activities are vital for fostering research and addressing new challenging problems. During the last 10 years of the FIRE initiative we have been involved in the organization of more than ten tracks, with the aim of the creation of new resources in several languages that were made available to the research community. This allowed to compare the new several approaches on the same datasets. In this chapter we will focus on the description of three author profiling tracks, on their data creation as well as the results analysis.The work on the author profiling data in Arabic was made possible by NPRP Grant #9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authorsRosso, P.; Rangel Pardo, FM. (2020). Author Profiling Tracks at FIRE. SN Computer Science. 1:1-11. https://doi.org/10.1007/s42979-020-0073-1S1111Al Sukhni E, Alequr Q. Investigating the use of machine learning algorithms in detecting gender of the Arabic tweet author. Int J Adv Comput Sci Appl. 2016;1(7):319–28.Alsmearat K, Al-Ayyoub M, Al-Shalabi R. An extensive study of the bag-of-words approach for gender identification of Arabic articles. In: 2014 IEEE/ACS 11th international conference on computer systems and applications (AICCSA). 2014. pp 601–608. IEEE.Alsmearat K, Shehab M, Al-Ayyoub M, Al-Shalabi R, Kanaan G. Emotion analysis of Arabic articles and its impact on identifying the authors gender. In: 12th international conference on computer systems and applications (AICCSA), 2015 IEEE/ACS; 2015.Álvarez-Carmona MA, López-Monroy AP, Montes-Y-Gómez M, Villaseñor-Pineda L, Jair-Escalante H. Inaoe’s participation at pan’15: author profiling task—notebook for pan at clef 2015; 2015.Argamon S, Koppel M, Fine J, Shimoni AR. Gender, genre, and writing style in formal written texts. TEXT. 2003;23:321–46.Argamon S, Dhawle S, Koppel M, Pennebaker JW. Lexical predictors of personality type. In: Proceedings of the joint annual meeting of the interface and the classification society of North America; 2005.Asghari H, Mohtaj S, Fatemi O, Faili H, Rosso P, Potthast M. Algorithms and corpora for Persian plagiarism detection: overview of pan at fire 2016. In: Notebook Papers of FIRE 2016, FIRE-2016, Kolkata, India, December 7–10, CEUR Workshop Proceedings. CEUR-WS.org, vol 1737; 2016. pp 135–144.Bachrach Y, Kosinski M, Graepel T, Kohli P, Stillwell D. Personality and patterns of Facebook usage. In: Proceedings of the ACM web science conference. ACM New York, NY, USA; 2012. pp 36–44.Banerjee S, Chakma K, Naskar DA Sudip, Rosso P, Bandyopadhyay S, Choudhury M. Overview of the mixed script information retrieval (MSIR) at fire-2016. In: Notebook papers of FIRE 2016, FIRE-2016, Kolkata, India, December 7–10, CEUR workshop proceedings. CEUR-WS.org, vol 1737; 2016. pp 94–99.Barrón-Cedeño A, Rosso P, Lalitha-Devi S, Clough P, Stevenson M. Pan@fire: Overview of the cross-language !ndian text re-use detection competition. In: 2nd and 3th international workshops FIRE 2010 and 2011, multilingual information access in south Asian Languages, Springer, LNCS(7536); 2013. pp 59–70.Bensalem I, Boukhalfa I, Rosso P, Abouenour L, Darwish K, Chikhi S. Overview of the araplagdet pan@ fire2015 shared task on Arabic plagiarism detection. In: Notebook papers of FIRE 2015, FIRE-2015, Gandhinagar, India, December 4–6, CEUR Workshop Proceedings. CEUR-WS.org, vol 1587; 2015. pp 111–122.Bishop-Clark C. Cognitive style, personality, and computer programming. Computers in human behavior, vol. 11–2. New York: Elsevier; 1995. p. 241–60.Castro D, Souza E, de Oliveira AL. Discriminating between brazilian and european portuguese national varieties on twitter texts. In: 5th Brazilian conference on intelligent systems (BRACIS); 2016. pp 265–270.Celli F, Polonio L. Relationships between personality and interactions in Facebook. Social networking: recent trends, emerging issues and future outlook. New York: Nova Science Publishers Inc; 2013. p. 41–54.Celli F, Lepri B, Biel JI, Gatica-Perez D, Riccardi G, Pianesi F. The workshop on computational personality recognition 2014. In: Proceedings of the ACM international conference on multimedia, ACM; 2014. pp 1245–1246.Costa PT, McCrae RR. The revised neo personality inventory (neo-pi-r). The SAGE handbook of personality theory and assessment, vol. 2. Thousand Oaks: Sage Publications Inc.; 2008. p. 179–98.Elfardy H, Diab MT. Sentence level dialect identification in Arabic. In: Association for computational linguistics (ACL); 2013. pp 456–461.Estival D, Gaustad T, Hutchinson B, Bao-Pham S, Radford W. Author profiling for English and Arabic emails; 2008.Flores E, Rosso P, Moreno L, Villatoro-Tello E. Pan@fire: Overview of SOCO track on the detection of source code re-use. In: Notebook papers of FIRE, FIRE-2014. India: Bangalore; 2014.Flores E, Rosso P, Moreno L, Villatoro-Tello E. Pan@ fire 2015: Overview of cl-soco track on the detection of cross-language source code re-use. In: Proceedings of the seventh forum for information retrieval evaluation (FIRE 2015), Gandhinagar, India; 2015. pp 4–6.Franco-Salvador M, Rangel F, Rosso P, Taule M, Marti M. Language variety identification using distributed representations of words and documents. Experimental IR meets multilinguality, multimodality, and interaction. Berlin: Springer; 2015. p. 28–40.Golbeck J, Robles C, Turner K. Predicting personality with social media. In: CHI’11 extended abstracts on human factors in computing systems, ACM; 2011. pp 253–262.Gupta P, Clough P, Rosso P, Stevenson M. Pan@fire: Overview of the cross-language Indian news story search (CLINSS) track. In: Notebook papers of FIRE 2012, FIRE-2012, Kolkata, India, December 17–19; 2012.Gupta P, Clough P, Rosso P, Stevenson M, Banchs R. Pan@fire: Overview of the cross-language Indian news story search (CLINSS) track. In: Notebook Papers of FIRE 2013, FIRE-2013, Delhi, India, December 4–6; 2013.Holmes J, Meyerhoff M. The handbook of language and gender. Blackwell handbooks in linguistics. New York: Wiley; 2003.Huang C, Lee L. Contrastive approach towards text source classification based on top-bag-of-word similarity. In: In PACLIC; 2008. pp 404–410.Karimi Z, Baraani-Dastjerdi A, Ghasem-Aghaee N, Wagner S. Links between the personalities, styles and performance in computer programming. J Syst Softw. 2016;111:228–41.Koppel M, Argamon S, Shimoni AR. Automatically categorizing written texts by author gender. Lit Linguist Comput. 2002;17:4.Kosinski M, Bachrach Y, Kohli P, Stillwell D, Graepel T. Manifestations of user personality in website choice and behaviour on online social networks. New York: Springer; 2013. p. 1–24.Litvinova T, Litvinlova O, Zagorovskaya O, Seredin P, Sboev A, Romanchenko O. “ruspersonality”: a Russian corpus for authorship profiling and deception detection. In: Intelligence, social media and web (ISMW FRUCT), 2016 international FRUCT conference on, IEEE; 2016. pp 1–7.Litvinova T, Seredin P, Litvinova O, Zagorovskaya O, Sboev A, Gudovskih D, Moloshnikov I, Rybka R. Gender prediction for authors of Russian texts using regression and classification techniques. In: CDUD@ CLA; 2016. pp 44–53.Litvinova T, Gudovskikh D, Sboev A, Seredin P, Litvinova O, Pisarevskaya D, Rosso P. Author gender prediction in Russian social media texts. In: Conference on analysis of images, social networks, and texts, AIST-2017, IEEE; 2017. pp 1101–1106.Litvinova T, Rangel F, Rosso P, Seredin P, Litvinova O. Overview of the rusprofiling pan at fire track on cross-genre gender identification in Russian. In: Notebook papers of FIRE 2017, FIRE-2017, Bangalore, India, December 8–11, CEUR Workshop Proceedings. CEUR-WS.org, vol 2036; 2017. pp 1–7.Lui M, Cook P. Classifying English documents by national dialect. In: Proceedings of the Australasian Language Technology Association Workshop; 2013. pp 5–15.Maharjan S, Shrestha P, Solorio T, Hasan R. A straightforward author profiling approach in mapreduce. In: Advances in artificial intelligence. Iberamia; 2014. pp 95–107.Maier W, Gomez-Rodriguez C. Language variety identification in Spanish tweets. In: LT4CloseLang 2014; 2014.Mairesse F, Walker MA, Mehl MR, Moore RK. Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Intell Res. 2007;30–1:457–500.Malmasi S, Zampieri M, Ljubešić N, Nakov P, Ali A, Tiedemann J. Discriminating between similar languages and Arabic dialect identification: a report on the third DSL shared task. In: Proceedings of the third workshop on NLP for similar languages, varieties and dialects (VarDial3); 2016. pp 1–14.Maulana Siagian AHA, Aritsugi M. Dbms-ku approach for author profiling and deception detection in Arabic. In: Metha P, Rosso P, Majumder P, Mitra M (Eds) Working notes of the forum for information retrieval evaluation (FIRE 2019). CEUR workshop proceedings. CEUR-WS.org, Kolkata, India, December 12–15; 2019.Neuman Y, Cohen Y. A vectorial semantics approach to personality assessment. Sci Rep. 2014;4:4761.Oberlander J, Nowson S. Whose thumb is it anyway?: classifying author personality from weblog text. In: Proceedings of the COLING/ACL on main conference poster sessions, Association for Computational Linguistics; 2006. pp 627–634.Paruma-Pabón OH, González FA, Aponte J, Camargo JE, Restrepo-Calle F. Finding relationships between socio-technical aspects and personality traits by mining developer e-mails. In: Proceedings of the 9th international workshop on cooperative and human aspects of software engineering, ACM; 2016. pp 8–14.Pennebaker JW, Mehl MR, Niederhoffer KG. Psychological aspects of natural language use: our words, our selves. Annu Rev Psychol. 2003;54(1):547–77.Quercia D, Lambiotte R, Stillwell D, Kosinski M, Crowcroft J. The personality of popular Facebook users. In: Proceedings of the ACM 2012 conference on computer supported cooperative Work, ACM; 2012. pp 955–964.Rangel F, Rosso P. On the multilingual and genre robustness of emographs for author profiling in social media. In: 6th international conference of CLEF on experimental IR meets multilinguality, multimodality, and interaction, Springer-Verlag, LNCS(9283); 2015. pp 274–280.Rangel F, Rosso P. On the impact of emotions on author profiling. Inf Process Manag. 2016;52(1):73–92.Rangel F, Rosso P. On the implications of the general data protection regulation on the organisation of evaluation tasks. Lang Law. 2019;5:95–117.Rangel F, Rosso P. Overview of the 7th author profiling task at pan 2019: Bots and gender profiling. In: Cappellato L, Ferro N, MÃller H, Losada D (Eds) CLEF 2019 labs and workshops, notebook papers. CEUR Workshop Proceedings. CEUR-WS.org; 2019.Rangel F, Rosso P, Potthast M, Stein B, Daelemans W. Overview of the 3rd author profiling task at pan 2015. In: Cappellato L, Ferro N, Jones G, San Juan E (Eds) CLEF 2015 labs and workshops, notebook papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1391; 2015.Rangel F, González F, Restrepo-Calle F, Montes M, Rosso P. Pan at fire: Overview of the PR-SOCO track on personality recognition in source code. In: Notebook papers of FIRE 2016, FIRE-2016, Kolkata, India, December 7–10, CEUR workshop proceedings. CEUR-WS.org, vol 1737; 2016. pp 1–5.Rangel F, Rosso P, Franco-Salvador M. A low dimensionality representation for language variety identification. In: 17th international conference on intelligent text processing and computational linguistics, CICLing. Springer; 2016. LNCS. arXiv:1705.10754Rangel F, Rosso P, Potthast M, Stein B. Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Working notes papers of the CLEF 2017 evaluation labs, CLEF and CEUR-WS.org, CEUR workshop proceedings; 2017.Rangel F, Rosso P, Charfi A, Zaghouani W, Ghanem B, Sánchez-Junquera J. Overview of the track on author profiling and deception detection in Arabic. In: Metha P, Rosso P, Majumder P, Mitra M (Eds) Working notes of the forum for information retrieval evaluation (FIRE 2019). CEUR workshop proceedings. CEUR-WS.org, Kolkata, India, December 12–15; 2019.Rangel F, Paolo R, Zaghouani W, Charfi A. Fine-grained analysis of language varieties and demographics. Nat Lang Eng; 2020. (In Press).Rosso P, Rangel F, Hernández-Farías I, Cagnina L, Zaghouani W, Charfi A. A survey on author profiling, deception, and irony detection for the Arabic language. Lang Ling Compass. 2018;12:4.Sadat F, Kazemi F, Farzindar A. Automatic identification of Arabic language varieties and dialects in social media. In: Proceedings of SocialNLP; 2014. p 22.Schler J, Koppel M, Argamon S, Pennebaker JW. Effects of age and gender on blogging. In: AAAI spring symposium: computational approaches to analyzing weblogs, AAAI; 2006. pp 199–205.Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, Agrawal M, Shah A, Kosinski M, Stillwell D, Seligman ME, et al. Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS One. 2013;8–9:773–91.Sequiera R, Choudhury M, Gupta P, Rosso P, Kumar S, Banerjee S, Kumar-Naskar S, Bandyopadhyay S, Chittaranjan G, Das A, Chakma K. Overview of fire-2015 shared task on mixed script information retrieval. In: Notebook papers of FIRE 2015, FIRE-2015, Gandhinagar, India, December 4–6, CEUR workshop proceedings. CEUR-WS.org, vol 1587; 2015. pp 19–25.Sun Y, Ning H, Chen K, Kong L, Yang Y, Wang J, Qi H. Author profiling in arabic tweets:an approach based on multi-classification with word and character features. In: Metha P, Rosso P, Majumder P, Mitra M (eds) Working notes of the forum for information retrieval evaluation (FIRE 2019). CEUR workshop proceedings. CEUR-WS.org, Kolkata, India, December 12–15; 2019.Weren E, Kauer A, Mizusaki L, Moreira V, de Oliveira P, Wives L. Examining multiple features for author profiling. J Inf Data Manag. 2014;20:266–79.Xu F, Wang M, Li M. Sentence-level dialects identification in the greater china region. Int J Nat Lang Comput. 2016;5:6.Zaghouani W, Charfi A. Arapâ tweet: a large multiâ dialect twitter corpus for gender, age and language variety identification. In: Proceedings of the 11th international conference on language resources and evaluation (LREC), Miyazaki, Japan; 2018.Zaghouani W, Charfi A. Guidelines and annotation framework for Arabic author profiling. In: Proceedings of the 3rd workshop on open-source Arabic corpora and processing tools, 11th international conference on language resources and evaluation (LREC), Miyazaki, Japan; 2018.Zaidan OF, Callison-Burch C. Arabic dialect identification. Comput Ling. 2014;40(1):171–202.Zampieri M, Gebre B. Automatic identification of language varieties: the case of Portuguese. In: The 11th conference on natural language processing (KONVENS). Osterreichischen Gesellschaft fur Artificial Intelligende (OGAI); 2012. pp 233–237.Zampieri M, Malmasi S, Ljubešić N, Nakov P, Ali A, Tiedemann J, Scherrer Y, Aepli N. Findings of the vardial evaluation campaign 2017. In: Proceedings of the fourth workshop on NLP for similar languages, varieties and dialects; 2017. pp 1–15
Porto Rosso / Schrift u. Gerippe v. Assissenten Jersche ; Terrainschraffirung v. Oberlt. Karl Roman Feigel
No full textPORTO ROSSO / SCHRIFT U. GERIPPE V. ASSISSENTEN JERSCHE ; TERRAINSCHRAFFIRUNG V. OBERLT. KARL ROMAN FEIGEL
Specialkarte der K. u. K. Österreichisch-Ungarischen Monarchie (-)
Porto Rosso / Schrift u. Gerippe v. Assissenten Jersche ; Terrainschraffirung v. Oberlt. Karl Roman Feigel (6958) ( -
Violenza contro le donne: linee politico-criminali
No full textLe linee politico-criminali degli interventi normativi in materia di violenza contro le donne: dal codice Rocco al 'codice rosso', fino al recentissimo "DDL Roccella"
“Un Sistema di Riferimento per la Temperatura Superficiale: una Collaborazione tra INRIM e Industria”
No full textExcluded-volume potential for rigid molecules endowed with symmetry
No full textThe excluded volume of a pair of molecules is proportional to the second virial coefficient in hard-core models that represent, for instance, the reference model in perturbation approaches to statistical theories of fluids [see, e.g. Chap. 5 of Kalikmanov, V. (2001), Statistical Physics of Fluids. Texts and Monograph in Physics, Springer, Berlin]. In three space dimensions, there exist exact results for convex molecules and in fact lack of convexity has been a major obstacle in applying the mathematical techniques employed in the convex case. In this paper, we illustrate how a mixed—analytical and numerical—method can be used to obtain exact expressions of the excluded volume for a pair of non-convex molecules conceived as aggregates of hard spheres; these can model van der Waals regions associated to the atoms forming each molecule. To compute the excluded volume for molecules endowed with C2v symmetry, modelled as chains of tangent hard spheres, we adapt a numerical code available to the scientific community. Because the result is a rather cumbersome expression in term of the relative orientation of the interacting molecules, we expand it over a suitable set of symmetry adapted Wigner functions to build up approximate, but faithful expressions, and we also prove analytical results announced elsewhere [Bisi, F., Durand, G., Rosso, R. & Virga, E. (2008), Polar steric interactions for v-shaped molecules. Phys. Rev. E, 78, 011705]
Il "Convento Rosso" e il suo territorio: alle origini di un sito monastico
No full textNel quadro di una collaborazione culturale tra l'Università di Roma Tre e la South Valley University (Sohag, Egitto), il contributo analizza il sito geografico e le strutture insediative del "Convento Rosso", monumento dell'arte copta risalente al V sec. Sono i primi risultati di una ricerca volta ad esplorare le logiche ambientali e i processi storico-territoriali che con la diffusione del cristianesimo hanno determinato il proliferare di siti monastici lungo la valle del Nilo. Il "Convento Rosso", che in una complessità di contesto geografico, di stratificazioni storico-culturali e di funzioni socio-economiche ha saputo associare le forme della vita eremitica presenti sulla vicina falesia con il modello cenobitico radicatosi nel fondovalle, si era venuto qualificando anche come centro di organizzazione e di sviluppo delle campagne circostanti
Biomimicking Extracellular Vesicles with Fully Artificial Ones: A Rational Design of EV-BIOMIMETICS toward Effective Theranostic Tools in Nanomedicine
No full textExtracellular Vesicles (EVs) are the protagonists in cell communication and membrane trafficking, being responsible for the delivery of innumerable biomolecules and signaling moieties. At the moment, they are of paramount interest to researchers, as they naturally show incredibly high efficiency and specificity in delivering their cargo. For these reasons, EVs are employed or inspire the development of nanosized therapeutic delivery systems. In this Perspective, we propose an innovative strategy for the rational design of EV-mimicking vesicles (EV-biomimetics) for theranostic scopes. We first report on the current state-of-the-art use of EVs and their byproducts, such as surface-engineered EVs and EV-hybrids, having an artificial cargo (drug molecule, genetic content, nanoparticles, or dye incorporated in their lumen). Thereafter, we report on the new emerging field of EV-mimicking vesicles for theranostic scopes. We introduce an approach to prepare new, fully artificial EV-biomimetics, with particular attention to maintaining the natural reference lipidic composition. We overview those studies investigating natural EV membranes and the possible strategies to identify key proteins involved in site-selective natural homing, typical of EVs, and their cargo transfer to recipient cells. We propose the use also of molecular simulations, in particular of machine learning models, to approach the problem of lipid organization and self-assembly in natural EVs. We also discuss the beneficial feedback that could emerge combining the experimental tests with atomistic and molecular simulations when designing an EV-biomimetics lipid bilayer. The expectations from both research and industrial fields on fully artificial EV-biomimetics, having the same key functions of natural ones plus new diagnostic or therapeutic functions, could be enormous, as they can greatly expand the nanomedicine applications and guarantee on-demand and scalable production, off-the-shelf storage, high reproducibility of morphological and functional properties, and compliance with regulatory standards
Dosimetry PET (DOPET)
No full textDOPET arises from a collaboration among different groups within INFN (Istituto Nazionale di Fisica Nucleare): Pisa and Bologna researchers will cooperate with the CATANA group at LNS (Catania).
DOPET aims to perform a feasibility study for verifying the applicability of an in-beam-PET for indirect extrapolation of radiation range in tissues, in the field of proton therapy. With the DoPET project we want to perform an evaluation of detection efficiency and spatial resolution that can be achieved with a dedicated PET system, in order to propel further developments in the field of relative dosimetry. The project provides the realization of a prototype for a compact two-head PET, allowing the maximum angular cover within the volume available in the therapy room. The prototype will be used for the comparison of activity profiles obtained by measurements on phantoms and GEANT4-based simulations.
Due to the limited angular acceptance of the prototype, the exact reconstruction of the image in all the three dimension cannot be achieved. However, to the purpose of verifying the utility of an in-beam PET, the most important result is to reach resolution of the order of the millimeter for the proton range. The depth profile is in fact the most critical issue of proton therapy, while the lateral beam spread is similar for carbon ions and protons.
Each PET head will be made of a position-sensitive photomultiplier Hamamatsu H8500 coupled with a matrix of high light yield crystal pixels. The dimensions of the matrix will match those of the active area of the PMT, i.e. 49x49 mm2, each pixel being 2x2 mm2 large and 1.5 interaction lengths deep. In order to achieve a satisfying energetic and spatial resolution, crystal choice will be oriented towards LYSO.
The collaboration with institutes provided of medical proton beams, such as CATANA (LNS, Italy), will allow us to carry out measurements on phantoms by using the prototype built. The PET prototype, for irradiations of the order of 15 Gy, will provides counting rates lower than 20 Hz. By acquiring positron annihilation events for ten minutes, the resulting statistic will be about 104 events, about one hundred times lower than that of standard PET images
- …
