فهرست مطالب ali asghar hosseinzadeh

Semantic Textual Similarity (STS) across languages is a pivotal challenge in natural language processing, with applications ranging from plagiarism detection to machine translation. Despite significant strides in STS, it remains a formidable task in languages with distinct syntactic structures and limited digital resources. Linguistic diversity, especially in word order variation, poses unique challenges, exemplified by languages adhering to Subject-Object-Verb (SOV) or Subject-Verb-Object (SVO) patterns, compounded by complexities like pronoun-dropping. This paper addresses the intricate task of measuring STS in Persian, characterized by SOV word order and distinctive linguistic features.

We propose a novel joint learning approach, harnessing an enhanced self-attention model, to tackle the STS challenge in both SOV and SVO language structures. Our methodology involves establishing a comprehensive multilingual corpus with parallel data for SOV and SVO languages, ensuring a diverse representation of linguistic structures. An improved self-attention model is introduced, featuring weighted relative positional encoding and enriched context representations infused with co-occurrence information through pointwise mutual information (PMI) factors. A joint learning framework leverages shared representations across languages, facilitating effective knowledge transfer and bridging the linguistic gap between SOV and SVO languages.

Our model, trained on Persian-English and Persian-Persian language pairs simultaneously, successfully extracts informative features, explicitly considering differences in word order and pronoun-dropping. During the training, the batch is sampled from STS benchmark with English and Translated Persian Pair texts and fed into customized encoder to get attention matrix and output embeddings. Then, the similarity module predicts the STS score. We use the STS score to compute the Mean Square Error (MSE) loss. Evaluation on Persian-English and Persian-Persian STS-Benchmarks demonstrates impressive performance, achieving Pearson correlation coefficients of 89.51% and 92.47%, respectively. Comparative experiments reveal superior performance against existing models, emphasizing the effectiveness of our proposed approach.

The ablation study further substantiates the robustness of our system, showcasing faster convergence and reduced susceptibility to overfitting. The results underscore the significance of our enhanced model in addressing the complexities of measuring semantic similarity in languages with diverse linguistic structures and limited digital resources. The approach not only advances cross-lingual STS capabilities but also provides insights into handling syntactic variations, such as SOV and SVO word orders, and pronoun-dropping. This research opens avenues for future investigations into enhancing STS in languages with unique structural characteristics.

Semantic Textual Similarity (STS) is considered one of the subfields of natural language processing that has gained extensive research attention in recent years. Measuring the semantic similarity between words, phrases, paragraphs, and documents plays a significant role in natural language processing and computational linguistics. Semantic Textual Similarity finds applications in plagiarism detection, machine translation, information retrieval, and similar areas. STS aims to develop computational methods that can capture the nuanced degrees of resemblance in meaning between words, phrases, sentences, paragraphs, or even entire documents which is a challenging task for languages with low digital resources. This task becomes intricate in languages with pronoun-dropping and Subject-Object-Verb (SOV) word order specifications, such as Persian, due to their distinctive syntactic structures. One of the most important aspects of linguistic diversity lies in word order variation within languages. Some languages adhere to Subject-Object-Verb (SOV) word order, while others follow Subject-Verb-Object (SVO) patterns. These structural disparities, compounded by factors like pronoun-dropping, render the task of measuring cross-lingual STS in such languages exceptionally intricate. In the context of low-resource languages like Persian, this study proposes a customized model based on linguistic properties. Leveraging pronoun-dropping and SOV word order specifications of Persian, we introduce an innovative enhancement: a novel weighted relative positional encoding integrated into the self-attention mechanism. Moreover, we enrich context representations by infusing co-occurrence information through pointwise mutual information (PMI) factors. This paper introduces a cross-lingual model for semantic similarity analysis between Persian and English texts, utilizing parallel corpora. The experiments show that our proposed model achieves better performance than other models. Ablation study also shows that our system can converge faster and is less prone to overfitting. The proposed model is evaluated on Persian-English and Persian-Persian STS-Benchmarks and achieved 88.29% and 91.65% Pearson correlation coefficients on monolingual and cross-lingual STS-B, respectively.

In recent years, increasing carbon emissions and relatively unfavorable climate change have led to paying attention to the concepts of sustainability as well as the imposition of strict government regulations on manufacturers and service providers. This has caused all parts of society, containing consumers, governments, and companies, to pay greater attention to low-carbon manufacturing in the supply chain (SC). To this end, this paper has designed a sustainable, multi-echelon, multi-product, multi-period, and multi-objective closed-loop supply chain (CLSC) network, with different distribution and collection channels and Radio Frequency Identification (RFID) technology, in addition, in order to produce low-carbon products, low-carbon products and subsidizing policies, and also deals with pricing strategy. This model, at the same time, maximizes profits and the social responsibility of the SC network, while it minimizes the overall delay in delivery time and environmental pollution. To cope with the parameters' uncertainty, a Robust scenario-based Stochastic programming (RSSP) approach has been used, and to solve and validate, the small-size model the Augmented Epsilon Constraint (AEC) method, and to solve large-sized ones, the third edition of the Non-dominated Sorting Genetic Algorithm (NSGA-III) and Multi-Objective Grey Wolf Optimizer Algorithm (MOGWO) are used. According to the computational results, the suggested model can provide efficient decisions and the MOGWO algorithm yields 14.5% improvement in execution time compared to the NSGA-III algorithm. Also suggested model can be a great tool for managers and professionals with a wide range of strategic applications.

The aim of this study was to predict the attitude of single young people to marriage based on personality traits and perfectionism. The present research was descriptive-correlational in terms of applied purpose and descriptive research design. The statistical population included all single students of Isfahan universities in the academic year 2020-2021. From this community, 170 students were selected by available methods and all paticipants responded to Bratton and Rosen (1998) Attitudes Towards Marriage, the 5 Big Factors of Short Form Personality (NEO-FFI), and the Terry - Short et al. (1995) Perfectionism Questionnaires. Pearson correlation method and hierarchical regression analysis and SPSS software were used to analyze the data. The results showed that among personality traits, neuroticism negatively and extroversion positively and significantly predicts the attitude to marriage. Also, negative perfectionism negatively and positive perfectionism positively and significantly predict the attitudes towards marriage. Based on the research findings, it can be concluded that personality traits and perfectionism are among the factors that play a role in young people's attitudes toward marriage.

Data Envelopment Analysis (DEA) is an extremely flexible and useful methodology, which provides a relative efficiency score for each decision-making unit.Classic DEA models only evaluate the performance efficiency of units and they cannot provide any information about the progress and regress of units in two periods. Also, they suppose that all inputs and outputs are positive real numbers. But in the real world, due to the existence of uncertainty, this assumption may not always be true. So, the DEA models with fuzzy data (FDEA models) can more realistically represent real-world problems than the conventional DEA models. In this paper, we assume all inputs, outputs and efficiency measures are triangular fuzzy numbers. So we present a method for obtaining the fuzzy Malmquist productivity index on fuzzy data, and finally, we can determine progress and regression for units with fuzzy data.

Nafas al-Mahmūm, is written about the martyrdom of Husayn bin ʿAli  and  has met with the favor and attention of the Shiite community more than other similar writings. In composing this book, Sheikh Abbas Qumi (d. 1941) used the method of hadith scholars, emphasizing on isnad, and tried to write his work based on sources that he considered authentic. Al-Qumi sought to put aside the last layers from the martyrdom of al-Husayn literature, which were often added during the Qājārid era (1789-1925). However, this process does not seem to produce a single, coherent narration of the ‘ashura event. The main shortcomings of the book are:  relying on some historians and hadith scholars and referring to their sources frequently without criticizing their narrations, not identifying the primary sources of the tradition or ignoring it, lack of reports verification, lack of checking the accuracy of the reports, ignoring conflict between reports, lack of historical approach and finally, the lack of coherence in the conclusion and selection of the preferred report. It can be said that although this book keeps the reader away from the distortions of the late period, the number of different reports on each issue does not allow the reader to find out Qumi's opinion.

The present study examined a theoretical model of indirect relationship between maternal bonding and symptoms of vulnerability related to eating through two mediational variables: eating disorder beliefs and alexithymia. Five hundred and twenty five students (215 females, 210 males) were selected through the cluster random sampling. The sample completed the Eating Disorder Inventory (Garner & et al., 1987), the Alexithymia Scale (Bagby & et al., 1994), the Eating Disorder Belief Questionnaire (Cooper & et al., 1997) and the Parental Bonding Instrument (Parker & et al., 1979). The results of structural equation modeling indicated that the maternal bonding had a significant indirect relation to symptoms of vulnerability to eating through alexithymia. However, relation of maternal bonding to symptoms of vulnerability to eating through eating disorder beliefs wasn’t significant. The findings suggested that strengthening maternal bonding can play an important role in preventing eating disorders in adolescents. maternal bonding, alexithymia, eating disorder beliefs, vulnerability related to eating

The aim of the present study was to investigate relationship between social support, students’ self-esteem, and identity styles. 224 university students (119 girls &105 boys) in Mahmood Abad city were selected through multi stage sampling. These participants completed Social Support Questionnaire (voux, 1986), Self-Esteem Scale (Rosenberg, 1965) and Identity Styles Inventory (ISI) (Brzonsky, 1997). Data was analyzed by applying regression and correlation coefficient. Results indicate that there is a significant positive correlation between self-esteem with normative and informational identity styles. However, the results show a significant negative correlation between self-esteem and confused/avoidance identity style. There was also a significant positive correlation between social support and informational and normative identity styles, while correlation between confused /avoidance style with social support was not significant. findings of the study is useful in predicting positive relation between self-esteem and social¬ support with informational and normative identity styles and negative relation between self-esteem and social support with confused /avoidance styles.