## 2 Background
[15] proposed a new simple network architecture, the \"Transformer\", for translating one language to another and achieved outstanding results. Two important terms related to the mother tongue are \"language loss\" and \"language policy\". Language loss refers to the gradual or rapid decline in the proficiency or use of an individual's mother tongue, often due to language change or death [16]. A study looking for the effect of the use of computers and Internet use on language loss would be noteworthy. Language policy refers to decisions and practices related to language use in various sectors, such as education, government, media, and commerce, which can significantly impact the status, use, and development of the mother tongue and other languages [17]. Regarding the detection of the mother tongue, [PERSON] et al. [18], utilising a gated recurrent unit (GRU) network, introduced a deep learning model that can accurately identify the mother tongue of Arabic language learners, an essential aspect of language education. The primary objective of this study was to tackle the challenge of recognising the mother tongue of Arabic language learners to customise personalised language learning strategies for each learner. The learners' written work is presented as input to the proposed model to generate writing samples. The pre-trained word embedding layer transforms the input text into a sequence of vectors, then passed to the GRU network to capture and model the input data's long-term dependencies, given its ability to model sequential data. The model is trained on a dataset of writing samples from Arabic language learners with different mother tongues and is evaluated on a separate test set. The results show that the proposed model outperforms several baseline models and achieves high accuracy in identifying the mother tongue of Arabic language learners. In addition, [PERSON] et al. [19] investigated the use of pronunciation information for speaker and language recognition. They tested their models on conversational speech datasets in multiple languages and found that pronunciation information improves the accuracy of mother tongue recognition. The papers presented in this discussion demonstrate the diverse range of approaches and methods that researchers have used to identify mother tongues. In addition to the tools used in the works mentioned above, many others can also be used to find a user's mother tongue. Some of them are attention models [20; 21], transformer models [22; 23], and graph models [24; 25]. By developing more accurate and efficient methods for mother tongue recognition, researchers can potentially improve language-related applications such as speech recognition, language teaching, and natural language processing. One of the earliest studies on keystroke dynamics (KD) was conducted by [PERSON] et al. in 1980 [26], where they investigated the variation in typing patterns between individuals and found that individuals had unique typing patterns that could be used for identification purposes. Since then, several studies have focused on using KD in authentication systems. For example, [PERSON] et al. [27] proposed a keyboard-based authentication system that used neural networks to identify users based on their typing patterns, achieving high accuracy rates. [PERSON] et al. [28] also conducted a study on using KD for biometric authentication, developing a model based on KD and evaluating its effectiveness through experiments. Other studies have explored this using KD to detect impostors and anomalies in typing behaviours. [PERSON] and [PERSON] [29] used a dataset from users typing a fixed text at regular intervals over several weeks to train and test anomaly detection algorithms, such as Principal Component Analysis, Mahalanobis distance, and Support Vector Machines. They found that the performance of the algorithms varied depending on the specific keystroke features being analysed.