List of Articles mutual information

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  1 - Holographic entanglement entropy in the fourth-order scale-invariant gravity
  Tahereh Hamedi M. Reza Tanhayi
  10.57647/j.jtap.2023.1701.02
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  2 - Higher-curvature corrections to holographic mutual information
  H. Bagheri M. Reza Tanhayi
  10.1007/s40094-020-00367-4
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  3 - Joint Learning Approach with Attention-based Model for Semantic Textual Similarity
  Ebrahim Ganjalipour Amir Hossein Refahi Sheikhani Sohrab Kordrostami Ali asghar Hosseinzadeh
  Introduction: 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 langua More

  Introduction: 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. Method: 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. Results: 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. Discussion: 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. Manuscript profile
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  4 - An Online group feature selection algorithm using mutual information
  maryam rahmaninia sondos bahadori
  Introduction: In the area of big data, the dimension of data in many fields are increasing dramatically. To deal with the high dimensions of training data, online feature selection algorithms are considered as very important issue in data mining. Recently, online featur More

  Introduction: In the area of big data, the dimension of data in many fields are increasing dramatically. To deal with the high dimensions of training data, online feature selection algorithms are considered as very important issue in data mining. Recently, online feature selection methods have attracted a lot of attention from researchers. These algorithms deal with the process of selecting important and efficient features and removing redundant features without any pre-knowledge of the set of features. Despite all the progress in this field, there are still many challenges related to these algorithms. Among these challenges, we can mention scalability, minimum size of selected features, sufficient accuracy and execution time. On the other hand, in many real-world applications, features are entered into the dataset in groups and sequentially. Although many online feature selection algorithms have been presented so far, but none of them have been able to find trade of between these criteria. Method: In this paper, we propose a group online feature selection method with feature stream using two new measures of redundancy and relevancy using mutual information theory. Mutual information can compute linear and non-linear dependency between the variables. With the proposed method, we try to create a better tradeoff between all the challenges. Results: In order to show the effectiveness of the proposed online group feature selection method, a number of experiments have been conducted on six large multi-label training data sets named ALLAML, colon, SMK-CAN-187, credit-g, sonar and breast-cancer in different applications and 3 online group feature selection algorithms named FNE_OGSFS، Group-SAOLA and OGSFS which are presented recently. Also, 3 evaluation criteria including average accuracy using KNN (k - nearest neighborhood (, SVM (Support Vector Machine) and NB (Naïve Bayesian) classifiers, number of selected features and executing time were used as criteria for comparing the proposed method. According to the obtained results, the proposed algorithm has obtained better results in almost of cases compared to other algorithms which it shows the efficiency of the proposed method. Discussion: In this paper, we will show that proposed online group feature selection method will achieve better performance by considering label group dependency between the new arrival features. Manuscript profile
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  5 - Enhanced Self-Attention Model for Cross-Lingual Semantic Textual Similarity in SOV and SVO Languages: Persian and English Case Study
  Ebrahim Ganjalipour Amir Hossein Refahi Sheikhani Sohrab Kordrostami Ali Asghar Hosseinzadeh
  10.22094/jcr.2023.1996326.1315
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  6 - A Supervised Method for Constructing Sentiment Lexicon in Persian Language
  Faranak Ebrahimi Rashed Neda Abdolvand
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  7 - FFS: A F-DBSCAN Clustering- Based Feature Selection For Classification Data
  Nasim Eshaghi Ali Aghagolzadeh
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  8 - Improving Short-Term Wind Power Prediction with Neural Network and ICA Algorithm and Input Feature Selection
  Elham Imaie Abdolreza Sheikholeslami Roya Ahmadi Ahangar
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  9 - The role of mutual information content of profit from market indices in the synchronicity of returns and portfolio performance with the mutual entropy approach
  Hassan Zarei Majid Davodi Nasr Majid Zanjirdar
  10.30495/afi.2023.1979811.1196
  Objective: The purpose of this research was to examine the role of the mutual information content of earnings from market indices in the synchrony of returns and portfolio performance. Research Methodology: This study was applied, descriptive, and analytical. The stati More

  Objective: The purpose of this research was to examine the role of the mutual information content of earnings from market indices in the synchrony of returns and portfolio performance. Research Methodology: This study was applied, descriptive, and analytical. The statistical population consisted of all companies listed on the Tehran Stock Exchange during the years 2012 to 2021. After screening, 118 companies were selected as the statistical sample. Joint entropy was used to measure the mutual information content of the variables. The Rahavard Novin software and the website of the Securities and Exchange Organization were used for data collection. Student's t-tests, mean comparison, paired tests, Levene's test for equality of variances, and bootstrap in hypothesis testing were employed, along with the R software for analysis. Findings: The results indicated that while the mutual information of company earnings from market indices leads to greater synchrony in returns, it is not statistically significant. Additionally, the results showed that portfolios with a coefficient of mutual information from earnings perform differently than traditional portfolios. The findings of the study suggest that portfolios formed from the top 20% coefficient of mutual information, overall and in holding periods of 6, 9, and 12 months, performed better than traditional and other portfolios. Originality / Value: The contribution of this research includes the use of joint entropy as a tool to measure the mutual information content of earnings from the market and examining its effect on synchrony, as well as presenting a new model for portfolio formation. Manuscript profile