Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California

Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (\u3e 1.6 m/year) to extremely slow (\u3c 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

On the use of word embedding for cross language plagiarism detection

[EN] Cross language plagiarism is the unacknowledged reuse of text across language pairs. It occurs if a passage of text is translated from source language to target language and no proper citation is provided. Although various methods have been developed for detection of cross language plagiarism, less attention has been paid to measure and compare their performance, especially when tackling with different types of paraphrasing through translation. In this paper, we investigate various approaches to cross language plagiarism detection. Moreover, we present a novel approach to cross language plagiarism detection using word embedding methods and explore its performance against other state-of-the-art plagiarism detection algorithms. In order to evaluate the methods, we have constructed an English-Persian bilingual plagiarism detection corpus (referred to as HAMTA-CL) comprised of seven types of obfuscation. The results show that the word embedding approach outperforms the other approaches with respect to recall when encountering heavily paraphrased passages. On the other hand, translation based approach performs well when the precision is the main consideration of the cross language plagiarism detection system.Asghari, H.; Fatemi, O.; Mohtaj, S.; Faili, H.; Rosso, P. (2019). On the use of word embedding for cross language plagiarism detection. Intelligent Data Analysis. 23(3):661-680. https://doi.org/10.3233/IDA-183985S661680233H. Asghari, K. Khoshnava, O. Fatemi and H. Faili, Developing bilingual plagiarism detection corpus using sentence aligned parallel corpus: Notebook for {PAN} at {CLEF} 2015, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.A. Barrón-Cede no, M. Potthast, P. Rosso and B. Stein, Corpus and evaluation measures for automatic plagiarism detection, In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner and D. Tapias, editors, Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2010, 17–23 May 2010, Valletta, Malta. European Language Resources Association, 2010.A. Barrón-Cede no, P. Rosso, D. Pinto and A. Juan, On cross-lingual plagiarism analysis using a statistical model, In B. Stein, E. Stamatatos and M. Koppel, editors, Proceedings of the ECAI’08 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, Patras, Greece, July 22, 2008, volume 377 of {CEUR} Workshop Proceedings. CEUR-WS.org, 2008.Farghaly, A., & Shaalan, K. (2009). Arabic Natural Language Processing. ACM Transactions on Asian Language Information Processing, 8(4), 1-22. doi:10.1145/1644879.1644881J. Ferrero, F. Agnès, L. Besacier and D. Schwab, A multilingual, multi-style and multi-granularity dataset for cross-language textual similarity detection, In N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk and S. Piperidis, editors, Proceedings of the Tenth International Conference on Language Resources and Evaluation {LREC} 2016, Portorož, Slovenia, May 23–28, 2016, European Language Resources Association {(ELRA)}, 2016.Franco-Salvador, M., Gupta, P., Rosso, P., & Banchs, R. E. (2016). Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language. Knowledge-Based Systems, 111, 87-99. doi:10.1016/j.knosys.2016.08.004Franco-Salvador, M., Rosso, P., & Montes-y-Gómez, M. (2016). A systematic study of knowledge graph analysis for cross-language plagiarism detection. Information Processing & Management, 52(4), 550-570. doi:10.1016/j.ipm.2015.12.004C.K. Kent and N. Salim, Web based cross language plagiarism detection, CoRR, abs/0912.3, 2009.McNamee, P., & Mayfield, J. (2004). Character N-Gram Tokenization for European Language Text Retrieval. Information Retrieval, 7(1/2), 73-97. doi:10.1023/b:inrt.0000009441.78971.beT. Mikolov, K. Chen, G. Corrado and J. Dean, Efficient estimation of word representations in vector space, CoRR, abs/1301.3, 2013.S. Mohtaj, B. Roshanfekr, A. Zafarian and H. Asghari, Parsivar: A language processing toolkit for persian, In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis and T. Tokunaga, editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7–12, 2018, European Language Resources Association ELRA, 2018.R.M.A. Nawab, M. Stevenson and P.D. Clough, University of Sheffield – Lab Report for {PAN} at {CLEF} 2010, In M. Braschler, D. Harman and E. Pianta, editors, {CLEF} 2010 LABs and Workshops, Notebook Papers, 22–23 September 2010, Padua, Italy, volume 1176 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2010.G. Oberreuter, G. L’Huillier, S.A. Rios and J.D. Velásquez, Approaches for intrinsic and external plagiarism detection – Notebook for {PAN} at {CLEF} 2011, In V. Petras, P. Forner and P.D. Clough, editors, {CLEF} 2011 Labs and Workshop, Notebook Papers, 19–22 September 2011, Amsterdam, The Netherlands, volume 1177 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2011.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to crosslingual natural language tasks. Journal of Algorithms, 64(1), 51-60. doi:10.1016/j.jalgor.2009.02.005M. Potthast, A. Barrón-Cede no, A. Eiselt, B. Stein and P. Rosso, Overview of the 2nd international competition on plagiarism detection, In M. Braschler, D. Harman and E. Pianta, editors, {CLEF} 2010 LABs and Workshops, Notebook Papers, 22–23 September 2010, Padua, Italy, volume 1176 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2010.Potthast, M., Barrón-Cedeño, A., Stein, B., & Rosso, P. (2010). Cross-language plagiarism detection. Language Resources and Evaluation, 45(1), 45-62. doi:10.1007/s10579-009-9114-zM. Potthast, A. Eiselt, A. Barrón-Cede no, B. Stein and P. Rosso, Overview of the 3rd international competition on plagiarism detection, In V. Petras, P. Forner and P.D. Clough, editors, {CLEF} 2011 Labs and Workshop, Notebook Papers, 19–22 September 2011, Amsterdam, The Netherlands, volume 1177 of {CEUR} Workshop Proceedings. CEUR-WS.org, 2011.M. Potthast, S. Goering, P. Rosso and B. Stein, Towards data submissions for shared tasks: First experiences for the task of text alignment, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.Potthast, M., Stein, B., & Anderka, M. (s. f.). A Wikipedia-Based Multilingual Retrieval Model. Advances in Information Retrieval, 522-530. doi:10.1007/978-3-540-78646-7_51B. Pouliquen, R. Steinberger and C. Ignat, Automatic identification of document translations in large multilingual document collections, CoRR, abs/cs/060, 2006.B. Stein, E. Stamatatos and M. Koppel, Proceedings of the ECAI’08 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, Patras, Greece, July 22, 2008, volume 377 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2008.J. Wieting, M. Bansal, K. Gimpel and K. Livescu, Towards universal paraphrastic sentence embeddings, CoRR, abs/1511.0, 2015.V. Zarrabi, J. Rafiei, K. Khoshnava, H. Asghari and S. Mohtaj, Evaluation of text reuse corpora for text alignment task of plagiarism detection, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.Barrón-Cedeño, A., Gupta, P., & Rosso, P. (2013). Methods for cross-language plagiarism detection. Knowledge-Based Systems, 50, 211-217. doi:10.1016/j.knosys.2013.06.01

A Scalable Byzantine Grid

Modern networks assemble an ever growing number of nodes. However, it remains difficult to increase the number of channels per node, thus the maximal degree of the network may be bounded. This is typically the case in grid topology networks, where each node has at most four neighbors. In this paper, we address the following issue: if each node is likely to fail in an unpredictable manner, how can we preserve some global reliability guarantees when the number of nodes keeps increasing unboundedly ? To be more specific, we consider the problem or reliably broadcasting information on an asynchronous grid in the presence of Byzantine failures -- that is, some nodes may have an arbitrary and potentially malicious behavior. Our requirement is that a constant fraction of correct nodes remain able to achieve reliable communication. Existing solutions can only tolerate a fixed number of Byzantine failures if they adopt a worst-case placement scheme. Besides, if we assume a constant Byzantine ratio (each node has the same probability to be Byzantine), the probability to have a fatal placement approaches 1 when the number of nodes increases, and reliability guarantees collapse. In this paper, we propose the first broadcast protocol that overcomes these difficulties. First, the number of Byzantine failures that can be tolerated (if they adopt the worst-case placement) now increases with the number of nodes. Second, we are able to tolerate a constant Byzantine ratio, however large the grid may be. In other words, the grid becomes scalable. This result has important security applications in ultra-large networks, where each node has a given probability to misbehave.Comment: 17 page