Effect of Attachment Training on Paternal-fetal Attachment

Background & aim: Paternal-fetal attachment develops an emotional relationship between father and his infant which can affect their future interactions. Therefore, the present study aimed to determine the effect of attachment training on paternal-fetal attachment. Methods: This clinical trial was conducted in Karmandan and 22 Bahman health centers in Mashhad in 2015. The participants of the study consisted of 60 randomly-selected fathers whose wives `gestational age was 28 to 32 weeks. The intervention group received three 120-min sessions of attachment training once a week in the forms of group discussion, lectures, question and answer, film screenings, and educational booklet. Data collection was performed by means of two questionnaires, named personal and fertility characteristics questionnaire and Weaver Cranley paternal-fetalattachment questionnaire. Two groups were assessed before, immediately after, and 3 weeks after intervention (follow-up) by paternal-fetal attachment questionnaire. Data analysis was performed in SPSS (version 22) using the Chi-square, independent t-test, Fisher, Mann-Whitney U test, and repeated measure tests. P Results: The results of repeated measures showed that mean scores of paternal-fetal attachment was not significantly different between the control and intervention groups before training (P=0.527) However, paternal-fetal attachment significantly increased at post-test (P=0.069) and follow-up (P=0.006) in the experimental group. Conclusion: Attachment training increases paternal-fetal attachment; therefore, pregnancy care programs should include training sessions for fathers

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

Plant leaf classification using GIST texture features

The leaves of plants have rich information in recognition of plants. In general, agriculture experts accomplish information extraction from the leaves. Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features. Then, the principal and suitable features are selected by principal component analysis (PCA) algorithm. In the classification step, three different approaches such as Patternnet neural network, support vector machine, and K‐nearest neighbour (KNN) algorithms were applied to the extracted features. For evaluation of the authors’ approach, they applied their proposed algorithm on three famous datasets. In comparison to some widely used features, the results show that their approach outperforms the other methods in the case of the time and the accuracy. The best results were achieved by applying PCA algorithm to GIST feature vector and using the Cosine KNN classifier

The Effect of Paternal-Fetal Attachment Training on Marital Satisfaction during Pregnancy

Background & aim: Marital satisfaction during pregnancy is one of the factors affecting marital affectional bond. This study was performed to evaluate the effect of paternal-fetal attachment training on marital satisfaction during pregnancy. Methods: This clinical trial was conducted on 60 couples referring to two health centers of Mashhad, Iran, in 2015. The couples were randomly divided into intervention and control groups (n=30 couples in each group). The fathers in the intervention group participated in three 120-minute sessions of paternal-fetal attachment training and the mothers in both groups (intervention and control) received the routine prenatal care. Both groups were evaluated using Marital Satisfaction questionnaire of Nathan H Azarin before and three weeks after the intervention. To analyze the data, descriptive statistics, t-test, Chi-square test, Wilcoxon, Mann-Whitney U test, and analysis of covariance were performed using SPSS, version 22. Results: The mean score of marital satisfaction in men was significantly higher in the intervention group than the control group (P=0.003). The mean score of women's marital satisfaction in the intervention group increased after training from 62.63±2.58 to 66.50±2.43. However, there was no significant difference between two groups in terms of women’s mean score of marital satisfaction (P=0.083). Conclusion: Paternal-fetal attachment training promoted marital satisfaction in men during pregnancy, so it is suggested to hold training programs for couples during pregnancy to enhance their marital satisfaction