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Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvements in accuracy. Additionally, most existing models are built upon Transformer architecture, which tends to be complex and unfriendly to small datasets. Hence, we propose a Nonlinear Regularization Decoding Method for Speech Recognition. Firstly, we introduce the nonlinear Transformer decoder, breaking away from traditional left-to-right or right-to-left decoding orders and enabling associations between any characters, mitigating the limitations of Transformer architectures on small datasets. Secondly, we propose a novel regularization attention module to optimize the attention score matrix, reducing the impact of early errors on later outputs. Finally, we introduce the tiny model to address the challenge of overly large model parameters. The experimental results indicate that our model demonstrates good performance. Compared to the baseline, our model achieves recognition improvements of 0.12%, 0.54%, 0.51%, and 1.2% on the Aishell1, Primewords, Free ST Chinese Corpus, and Common Voice 16.1 datasets of Uyghur, respectively. [ABSTRACT FROM AUTHOR]

Islam is widely spread throughout every corner of China, with the Hui people, the largest Muslim ethnic group in China, numbering over 10 million people, serving as its main carrier. Their culture types and local features exhibit great diversity across different provinces. The ceremony of Prophet's Day or Mawlid al-Nabi in China, as one of the three fundamental festivals of the Hui people alongside Eid al-Fitur and Eid al-Qurban, appears to be more comprehensive, open, and localized. Drawing from fieldwork in three Hui communities—Xi'an in Shaanxi province, Najiaying in Yunnan province, and Hezhou in Gansu province—this paper approaches the topic from the perspective of inculturation and cultural innovation. It aims to describe the ritual processes observed in these three different Hui communities and discuss how the Hui people integrate Islam with traditional Chinese culture in their local contexts, with the intention of forming and preserving their own cultural characteristics. [ABSTRACT FROM AUTHOR]

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset. [ABSTRACT FROM AUTHOR]

In this study, based on a morpheme segmentation framework, we researched a text keyword extraction method for Uyghur, Kazakh and Kirghiz languages, which have similar grammatical and lexical structures. In these languages, affixes and a stem are joined together to form a word. A stem is a word particle with a notional meaning, while the affixes perform grammatical functions. Because of these derivative properties, the vocabularies used for these languages are huge. Therefore, pre-processing is a necessary step in NLP tasks for Uyghur, Kazakh and Kirghiz. Morpheme segmentation enabled us to remove the suffixes as the auxiliary unit while retaining the meaningful stem and it reduced the dimension of the feature space present in the keyword extraction task for Uyghur, Kazakh and Kirghiz texts. We transformed the morpheme segmentation task into the problem of labeling the morpheme sequences, and we used the Bi-LSTM network to bidirectionally obtain the position feature information of character sequences. We applied CRF to effectively learn the information of the preceding and following label sequences to build a highly accurate Bi-LSTM_CRF morpheme segmentation model, and we prepared morpheme-based experimental text sets by using this model. Subsequently, we used the stem vectors' similarity to modify the TextRank algorithm, subsequent to the training of the stem embedding vector using the Doc2vec algorithm, and then we performed a text keyword extraction experiment. In this experiment, the highest F1 scores of 43.8%, 44% and 43.9% were obtained for three datasets. The experimental results show that the morpheme-based approach provides much better results than the word-based approach, which shows the stem vector similarity weighting is an efficient method for the text keyword extraction task, thus proving the efficiency of morpheme sequence for morphologically derivative languages. [ABSTRACT FROM AUTHOR]

Qixiang is a unique sacrificial culture created by the Han army eight banners in northeast China. This culture not only has elements such as shamanism and Han people burning incense, but also has military sacrificial elements. This paper argues that Qixiang is the evolution and legacy of Maji, a military sacrificial ritual in ancient China. The Han military banner people in the Qing Dynasty took Maji as the main body, combined the Manchu Shamanism with the Han incense burning, and created a cultural symbol representing their own ethnic group. At present, the study of Qixiang not only helps to understand the complexity of the development and evolution of Manchu shamanism, but also helps to reveal the ethnic identity of Han bannermen under the Eight Banners system of the Qing Dynasty. [ABSTRACT FROM AUTHOR]

This essay explores how Paul negotiates and constructs social identity for the Christos-followers in Corinth from a post-supersessionist perspective by using the Social Identity Theory. Focusing on a close reading of two controversial passages taken from 1 Corinthians (1 Cor 7:17–24 and 9:19–23), this essay argues that there is continuation of existing social identities of both the Jewish and gentile Christos-followers. In the Christos-movement, Jews were not expected to give up their place of belonging in order to become Christos-followers. Likewise, gentiles were not expected to leave behind their previous identity and embrace Jewish practices in order to become the people of God who worship the God of Israel. While the existing social identities continue, Paul also creatively transforms them. Reflection on the implication of this reading for contemporary ethnic Chinese Christos-followers in Muslim-majority Malaysia is also offered. [ABSTRACT FROM AUTHOR]

Handwritten signatures have traditionally been used as a common form of recognition and authentication in tasks such as financial transactions and document authentication. However, there are few studies on minority languages such as Uyghur and Kazakh used in Xinjiang, China, and no available public dataset for these scripts, which are widely used in banking and other fields. Therefore, this paper addresses this problem by constructing a dataset containing Uyghur, Kazakh, and Han languages and presents an automatic handwritten signature recognition approach based on Uyghur, Kazakh, Han, and public datasets. In the paper, a handwritten signature recognition method that combines local maximum occurrence features (LOMO) and histogram of orientated gradients (HOG) features was proposed. LOMO features use a sliding window to represent the local features of the signature image. The high-dimensional features formed by the combination of these methods are dimensionally reduced by principal component analysis (PCA). The classification is performed using k-nearest neighbors (k-NN), and it is compared with the random forest method. The proposed method achieved a recognition rate of 98.4% using a diverse signature database compared with existing methods. It shows that the method was effective and can be applied to large datasets of mixed, multilingual signatures. [ABSTRACT FROM AUTHOR]

In the past two decades, studies of Y chromosomal single nucleotide polymorphisms (Y-SNPs) and short tandem repeats (Y-STRs) have shed light on the demographic history of Central Asia, the heartland of Eurasia. However, complex patterns of migration and admixture have complicated population genetic studies in Central Asia. Here, we sequenced and analyzed the Y-chromosomes of 187 male individuals from Kazakh, Kyrgyz, Uzbek, Karakalpak, Hazara, Karluk, Tajik, Uyghur, Dungan, and Turkmen populations. High diversity and admixture from peripheral areas of Eurasia were observed among the paternal gene pool of these populations. This general pattern can be largely attributed to the activities of ancient people in four periods, including the Neolithic farmers, Indo-Europeans, Turks, and Mongols. Most importantly, we detected the consistent expansion of many minor lineages over the past thousand years, which may correspond directly to the formation of modern populations in these regions. The newly discovered sub-lineages and variants provide a basis for further studies of the contributions of minor lineages to the formation of modern populations in Central Asia. [ABSTRACT FROM AUTHOR]

Uyghur text detection and recognition in images with simple backgrounds is still a challenging task for Uyghur image content analysis. In this paper, we propose a new effective Uyghur text detection method based on channel-enhanced MSERs and the CNN classification model. In order to extract more complete text components, a new text candidate region extraction algorithm is put forward, which is based on the channel-enhanced MSERs according to the characteristics of Uyghur text. In order to effectively prune the non-text regions, we design a CNN classification network according to the LeNet-5, which gains the description characteristics automatically and avoids the tedious and low efficiency artificial characteristic extraction work. For Uyghur text recognition in images, we improved the traditional CRNN network, and to verify its effectiveness, the networks trained on a synthetic dataset and evaluated on the text recognition datasets. The experimental results indicated that the Uyghur text detection method in this paper is robust and applicable, and the recognition result by improvedCRNN was better than the original CRNN network. [ABSTRACT FROM AUTHOR]

Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar texture and text morphology in the complex background noise of natural scene images are prone to false recall and difficult to detect multi-scale texts, a multi-directional scene Uyghur text detection model based on fine-grained feature representation and spatial feature fusion is proposed, and feature extraction and feature fusion are improved to enhance the network's ability to represent multi-scale features. In this method, the multiple groups of 3 × 3 convolutional feature groups that are connected like the hierarchical residual to build a residual network for feature extraction, which captures the feature details and increases the receptive field of the network to adapt to multi-scale text and long glued dimensional font detection and suppress false positives of text-like objects. Secondly, an adaptive multi-level feature map fusion strategy is adopted to overcome the inconsistency of information in multi-scale feature map fusion. The proposed model achieves 93.94% and 84.92% F-measure on the self-built Uyghur dataset and the ICDAR2015 dataset, respectively, which improves the accuracy of Uyghur text detection and suppresses false positives. [ABSTRACT FROM AUTHOR]

Bilingual lexicon extraction is useful, especially for low-resource languages that can leverage from high-resource languages. The Uyghur language is a derivative language, and its language resources are scarce and noisy. Moreover, it is difficult to find a bilingual resource to utilize the linguistic knowledge of other large resource languages, such as Chinese or English. There is little related research on unsupervised extraction for the Chinese-Uyghur languages, and the existing methods mainly focus on term extraction methods based on translated parallel corpora. Accordingly, unsupervised knowledge extraction methods are effective, especially for the low-resource languages. This paper proposes a method to extract a Chinese-Uyghur bilingual dictionary by combining the inter-word relationship matrix mapped by the neural network cross-language word embedding vector. A seed dictionary is used as a weak supervision signal. A small Chinese-Uyghur parallel data resource is used to map the multilingual word vectors into a unified vector space. As the word-particles of these two languages are not well-coordinated, stems are used as the main linguistic particles. The strong inter-word semantic relationship of word vectors is used to associate Chinese-Uyghur semantic information. Two retrieval indicators, such as nearest neighbor retrieval and cross-domain similarity local scaling, are used to calculate similarity to extract bilingual dictionaries. The experimental results show that the accuracy of the Chinese-Uyghur bilingual dictionary extraction method proposed in this paper is improved to 65.06%. This method helps to improve Chinese-Uyghur machine translation, automatic knowledge extraction, and multilingual translations. [ABSTRACT FROM AUTHOR]

Suosuo grape (Vitis vinifer L) is traditionally used as a therapeutic agent for measles and hepatitis by the ethnic Uighurs. This work aimed to investigate the anti-HBV effect of total triterpene (VTT), total flavonoids (VTF) and total polysaccharides (VTP) from Suosuo grape, and their synergistic effects were also tested. The viral antigens of cellular secretion, HBsAg and HBeAg, were determined by enzyme linked immunosorbent assay (ELISA).The quantity of HBV-DNA released in the supernatant was assayed by realtime PCR. It was found that it effectively suppressed the secretion of HBsAg and HBeAg from HepG2.2.15 cells in a dose-dependent manner, as well as the HBV DNA. The results of orthogonal design experiment showed that the combination of VTT 20 μg/mL, VTF 50 μg/mL and VTP 50 μg/mL had the best optimistic inhibitory effects on HBeAg secretion. [ABSTRACT FROM AUTHOR]