Translation Architecture Research Articles

DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image Sequences

Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study proposes a novel disease progression prediction (DPP) method for Ginkgo leaf blight with a multi-level feature translation architecture and enhanced spatiotemporal attention module (eSTA). The proposed DPP method is capable of capturing key spatiotemporal dependencies of disease symptoms at various feature levels. Experiments demonstrated that the DPP method achieves state-of-the-art prediction performance in disease progression prediction. Compared to the top-performing spatiotemporal predictive learning method (SimVP + TAU), our method significantly reduced the mean absolute error (MAE) by 19.95% and the mean square error (MSE) by 25.35%. Moreover, it achieved a higher structure similarity index measure (SSIM) of 0.970 and superior peak signal-to-noise ratio (PSNR) of 37.746 dB. The proposed method can accurately forecast the progression of Ginkgo leaf blight to a large extent, which is expected to provide valuable insights for precision and intelligent disease management. Additionally, this study presents a novel perspective for the extensive research on plant disease prediction.

An image caption model based on attention mechanism and deep reinforcement learning.

Image caption technology aims to convert visual features of images, extracted by computers, into meaningful semantic information. Therefore, the computers can generate text descriptions that resemble human perception, enabling tasks such as image classification, retrieval, and analysis. In recent years, the performance of image caption has been significantly enhanced with the introduction of encoder-decoder architecture in machine translation and the utilization of deep neural networks. However, several challenges still persist in this domain. Therefore, this paper proposes a novel method to address the issue of visual information loss and non-dynamic adjustment of input images during decoding. We introduce a guided decoding network that establishes a connection between the encoding and decoding parts. Through this connection, encoding information can provide guidance to the decoding process, facilitating automatic adjustment of the decoding information. In addition, Dense Convolutional Network (DenseNet) and Multiple Instance Learning (MIL) are adopted in the image encoder, and Nested Long Short-Term Memory (NLSTM) is utilized as the decoder to enhance the extraction and parsing capability of image information during the encoding and decoding process. In order to further improve the performance of our image caption model, this study incorporates an attention mechanism to focus details and constructs a double-layer decoding structure, which facilitates the enhancement of the model in terms of providing more detailed descriptions and enriched semantic information. Furthermore, the Deep Reinforcement Learning (DRL) method is employed to train the model by directly optimizing the identical set of evaluation indexes, which solves the problem of inconsistent training and evaluation standards. Finally, the model is trained and tested on MS COCO and Flickr 30 k datasets, and the results show that the model has improved compared with commonly used models in the evaluation indicators such as BLEU, METEOR and CIDEr.

Unsupervised underwater image enhancement via content-style representation disentanglement

The absorption and scattering properties of the water medium cause various types of distortion in underwater images, which seriously affects the accuracy and effectiveness of subsequent processing. The application of supervised learning algorithms in underwater image enhancement is limited by the difficulty of obtaining a large number of underwater paired images in practical applications. As a solution, we propose an unsupervised representation disentanglement based underwater image enhancement method (URD-UIE). URD-UIE disentangles content information (e.g., texture, semantics) and style information (e.g., chromatic aberration, blur, noise, and clarity) from underwater images and then employs the disentangled information to generate the target distortion-free image. Our proposed method URD-UIE adopts an unsupervised cycle-consistent adversarial translation architecture and combines multiple loss functions to impose specific constraints on the output results of each module to ensure the structural consistency of underwater images before and after enhancement. The experimental results demonstrate that the URD-UIE technique effectively enhances the quality of underwater images when training with unpaired data, resulting in a significant improvement in the performance of the standard model for underwater object detection and semantic segmentation.

CycleGAN-Based Image to Image Translation for Realistic Surgical Training Phantoms.

Training in surgery is essential for surgeons to develop skill and dexterity. Physical training phantoms provide excellent haptic feedback and tissue properties for stitching and operating with authentic instruments and are easily available. However, they lack realistic traits and fail to reflect the complex environment of a surgical scene. Generative Adversarial Networks can be used for image-to-image translation, addressing the lack of realism in physical phantoms, by mapping patterns from the intraoperative domain onto the video stream captured during training with these surgical simulators. This work aims to achieve a successful I2I translation, from intra-operatory mitral valve surgery images onto a surgical simulator, using the CycleGAN model. Different experiments are performed - comparing the Mean Square Error Loss with the Binary Cross Entropy Loss; validating the Fréchet Inception Distance as a training and image quality metric; and studying the impact of input variability on the model performance. Differences between MSE and BCE are modest, with MSE being marginally more robust. The FID score proves to be very useful in identifying the best training epochs for the CycleGAN I2I translation architecture. Carefully selecting the input images can have a great impact in the end results. Using less style variability and input images with good feature details and clearly defined characteristics enables the network to achieve better results.Clinical Relevance- This work further contributes for the domain of realistic surgical training, successfully generating fake intra operatory images from a surgical simulator of the cardiac mitral valve.

Semantically preserving adversarial unsupervised domain adaptation network for improving disease recognition from chest x-rays.

Supervised deep learning methods have been successfully applied in medical imaging. However, training deep learning systems often requires ample annotated data. Due to cost and time restrictions, not all collected medical images, e.g., chest x-rays (CXRs), can be labeled in practice. To classify these unlabeled images, a solution may involve adopting a model trained with sufficient labeled data in relevant domains (with both source and target being CXRs). However, domain shift may cause the trained model not able to generalize well on unlabeled target datasets. This work aims to develop a novel unsupervised domain adaptation (UDA) framework to improve recognition performance on unlabeled target data. We present a semantically preserving adversarial UDA network, i.e., SPA-UDA net, with the potential to bridge the domain gap, by reconstructing the images in the target domain via an adversarial encode-and-reconstruct translation architecture. To preserve the class-specific semantic information (i.e., with or without disease) of the original images when translating, a semantically consistent framework is embedded. This framework is designed to guarantee that fine-grained disease-related information on the original images can be safely transferred. Furthermore, the proposed SPA-UDA net does not require paired images from source and target domains when training, which reduces the cost of arranging data significantly and is ideal for UDA. We evaluate the proposed SPA-UDA net on two public CXR datasets for lung disease recognition. The experimental results show that the proposed framework achieves significant performance improvements compared to other state-of-the-art UDA methods.

Practical Disruption of Image Translation Deepfake Networks

By harnessing the latest advances in deep learning, image-to-image translation architectures have recently achieved impressive capabilities. Unfortunately, the growing representational power of these architectures has prominent unethical uses. Among these, the threats of (1) face manipulation ("DeepFakes") used for misinformation or pornographic use (2) "DeepNude" manipulations of body images to remove clothes from individuals, etc. Several works tackle the task of disrupting such image translation networks by inserting imperceptible adversarial attacks into the input image. Nevertheless, these works have limitations that may result in disruptions that are not practical in the real world. Specifically, most works generate disruptions in a white-box scenario, assuming perfect knowledge about the image translation network. The few remaining works that assume a black-box scenario require a large number of queries to successfully disrupt the adversary's image translation network. In this work we propose Leaking Transferable Perturbations (LTP), an algorithm that significantly reduces the number of queries needed to disrupt an image translation network by dynamically re-purposing previous disruptions into new query efficient disruptions.

Semi-Supervised Model for Aspect Sentiment Detection

Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model.

Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons.

While Generative Adversarial Networks (GANs) can now reliably produce realistic images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic textures present in many large 3D fluorescent microscopy (FM) images acquired in biological research. This is especially problematic in neuroscience where the lack of ground truth data impedes the development of automated image analysis algorithms for neurons and neural populations. We therefore propose an unpaired mesh-to-image translation methodology for generating volumetric FM images of neurons from paired ground truths. We start by learning unique FM styles efficiently through a Gramian-based discriminator. Then, we stylize 3D voxelized meshes of previously reconstructed neurons by successively generating slices. As a result, we effectively create a synthetic microscope and can acquire realistic FM images of neurons with control over the image content and imaging configurations. We demonstrate the feasibility of our architecture and its superior performance compared to state-of-the-art image translation architectures through a variety of texture-based metrics, unsupervised segmentation accuracy, and an expert opinion test. In this study, we use 2 synthetic FM datasets and 2 newly acquired FM datasets of retinal neurons.

Монгол-Aнгли орчуулга хийдэг нейрон сүлжээнд суурилсан машины орчуулгын гурван шатлалт загвар боловсруулах

The widespread use of neural machine translation has the advantage of allowing users to translate terms and translate untrained data to a certain extent, but in some cases often results in distorted sentence structure. This study aims to address issues such as neural machine translation control, high-probability translation of unrecognized data, correct sentence structure, beginning and ending recognition, and the establishment of an independent, machine translator in one's home country. We have made improvements to the neural network model, such as adjusting neural machine translation to unidentified words in subunits, and defining sentence boundaries and scope. The design is based on the usual PMT and SMT templates used to compare words in a system that takes into account word and sentence structure. However, the model we developed is based on the latest neural machine translation (NMT) architecture, which can make more complex relationships. In this sense, this work can be seen as an attempt to use a combination of statistical machine translation and neural machine translation. We sought and tested in practice a step-by-step approach to combining complex deep neural network models that included longer contexts in a system that considered only short contexts in terms of word and sentence structures.

The neural machine translation models for the low-resource Kazakh-English language pair.

The development of the machine translation field was driven by people's need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh-English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh-English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.

Aplikasi Penerjemah Bahasa Bangka Ke Bahasa Indonesia Menggunakan Neural Machine Translation Berbasis Website

At this time, machine translation is one of the options for translating languages, especially for people who want to learn a language. Bangka language is a language that is often used by the Bangka people for everyday life. There have been many developments for machine translation, but no one has yet developed a translation machine for the Bangka language. The development of a machine translation using Neural Machine Translation (NMT) and its implementation using the Flask microframework is the first step for the development of a machine translation for the Bangka language. This study aims to make and find out the translation results of the Bangka language translator application into Indonesian using the RNN translator model. The development starts from creating a parallel corpus of Bangka language to Indonesian and machine translation architecture with the RNN model. The research method starts from dataset collection, data preprocessing, modeling and training, system evaluation and implementation. From the results of the BLEU Scores evaluation, a value of 55.3% was obtained for Bangka to Indonesian. Furthermore, the model is implemented in the form of a website by utilizing the Flask framework, so that users can easily translate.

A Voyage on Neural Machine Translation for Indic Languages

With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades. Slowly, NMT paved its path into Indian MT research and witnessed many works for various language pairs in this regard. Numerous NMT architectures are floating across the international and national research pool; many claims to be state-of-the-art architectures. Though NMT for Indic languages (ILNMT) is giving better results for majority speaking language pairs, the translation quality is low due to a lack of significant resources. Automated machine translation models are unavailable for some less spoken Indic languages like Kashmiri and Dogri. Hence, there is increasing demand in the research to address the challenges of developing applicable MT models even when minuscule training data is available. Based on the corpus availability, the languages are categorized into High Resource Languages (HRLs), Low Resource Languages (LRLs), and Zero Resource Languages (ZRLs). Many Indic languages are classified into HRLs, LRLs, and ZRLs based on corpus availability. The vision behind this literature survey paper is to make this paper a collective source for all information regarding the predominant ILNMT architectures, the toolkits available for building NMT models, and various pre-trained language models needed by researchers who contribute to the ILNMT research community. In this survey paper, ILNMT architectures for different Indic languages are covered, e.g., Hindi, Tamil (HRLs), Kannada, Marathi (LRLs), Sinhala, and Nepali (ZRLs). There are a few language-specific survey papers on ILNMT, and this is one of the first kinds of survey papers where all the information is gathered under one canopy.

Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture

Transformers have emerged as a pivotal tool in machine translation. Nonetheless, their effectiveness typically hinges on extensive training with millions of bilingual parallel corpora. This paper presents a novel architecture, termed as Attention Link (AL), which is specifically designed to bolster the performance of transformer models in situations where training resources are scarce. We furnish theoretical substantiation that underscores the superiority of the AL architecture in such resource-limited settings. Experimental outcomes lend further credence to the enhancements brought about by our methodology, marking it as a stride towards enhancing the performance of transformer models.

Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification

In daily life, when taking photos of scenes containing glass, the images of the dominant transmission layer and the weak reflection layer are often blended, which are difficult to be uncoupled. Meanwhile, because the reflection layer contains sufficient important information about the surrounding scene and the photographer, the problem of recovering the weak reflection layer from the mixture image is of importance in surveillance investigations. However, most of the current studies mainly focus on extracting the transmission layer while often ignoring the merit of the reflection layer. To fill that gap, in this paper, we propose a network framework that aims to accomplish two tasks: (1) for general scenes, we attempt to recover reflection layer images that are as close as possible to the ground truth ones, and (2) for scenes containing portraits, we recover the basic contour information of the reflection layer while improving the defects of dim portraits in the reflection layer. Through analyzing the performance exhibited by different levels of feature maps, we present the first transmission removal network based on an image-to-image translation architecture incorporating residual structures. The quality of generated reflection layer images is improved via tailored content and style constraints. We also use the patch generative adversarial network to increase the discriminator’s ability to perceive the reflection components in the generated images. Meanwhile, the related information such as edge and color distribution of transmission layer in the mixture image is used to assist the overall reflection layer recovery. In the large-scale experiments, our proposed model outperforms reflection removal-based SOTAs by more than 5.356 dB in PSNR, 0.116 in SSIM, and 0.057 in LPIPS.

PISDGAN: Perceive image structure and details for laryngeal image enhancement

The introduction of optical techniques such as narrow-band imaging (NBI) during laryngeal endoscopic diagnosis can emphasize the superficial mucosal veins and increase the identification rate of lesions. To eliminate the limitation of endoscopic devices, in this paper, an image enhancement method that can perceive the information and details of the image structure (PISDGAN) is proposed. The method is based on the classical cycle consistency image translation architecture, and its generator is adjusted to a U-shaped residual structure. At the same time, a multiscale cross-layer adaptation module (CLA) is designed, which integrates features of different depths through 'feature transfer' to obtain abundant image information. A non-local feature denoising module (FD) is also introduced to enhance the quality of the translated image. In addition, the structure difference loss is proposed to constrain the image structure to ensure the integrity of the translated image. Based on the self-built white light and narrow band laryngeal image datasets, the experimental comparison of PISDGAN and other similar image translation methods shows that the proposed method not only effectively preserves the image structure, but also maintains good image quality while highlighting the details.

English-Chinese Machine Translation Based on Transfer Learning and Chinese-English Corpus.

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.

Workload and cognitive architecture in translation

Abstract This article endeavours to sketch out a consistent merger of models of language production and bilingualism that are currently used in the fields of Translation and Interpreting and of psycholinguistics. The workload resource of Cognitive Verbal Processing and the processing components that it serves are found to be of particular interest in this interdisciplinary perspective. Known psycholinguistic effects that involve workload (i.e., a function of activation energy) are outlined with particular emphasis on their possible relevance in translation processing. This suggests a number of potential lines of research for both fields.

Early Changes in Transcriptomic Profiles in Synaptodendrosomes Reveal Aberrant Synaptic Functions in Alzheimer's Disease.

Alzheimer’s disease (AD) is one of the most prevalent neurodegenerative disorders characterized by the progressive decline of cognitive functions, and is closely associated with the dysfunction of synapses, which comprise the basic structure that mediates the communication between neurons. Although the protein architecture and machinery for protein translation at synapses are extensively studied, the impact that local changes in the mRNA reservoir have on AD progression is largely unknown. Here, we investigated the changes in transcriptomic profiles in the synaptodendrosomes purified from the cortices of AD mice at ages 3 and 6 months, a stage when early signatures of synaptic dysfunction are revealed. The transcriptomic profiles of synaptodendrosomes showed a greater number of localized differentially expressed genes (DEGs) in 6-month-old AD mice compared with mice 3 months of age. Gene Ontology (GO) analysis showed that these DEGs are majorly enriched in mitochondrial biogenesis and metabolic activity. More specifically, we further identified three representative DEGs in mitochondrial and metabolic pathways—Prnp, Cst3, and Cox6c—that regulate the dendritic spine density and morphology in neurons. Taken together, this study provides insights into the transcriptomic changes in synaptodendrosomes during AD progression, which may facilitate the development of intervention strategies targeting local translation to ameliorate the pathological progression of AD.

Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks.

Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2× in BP estimation.

Semantic Analysis Technology of English Translation Based on Deep Neural Network.

English translation plays an important role in the development of science and technology and cultural exchanges. With the increase in translation volume, intelligent translation has become inevitable, but there is no effective solution for semantic translation in English translation. To provide an effective translation improvement scheme in English translation, this paper studies and analyzes the application of deep neural network in English translation semantic analysis. Based on a brief analysis of the research progress of English translation analysis and the current situation of neural network, a neural network translation architecture is established, and a deep neural network model for English translation analysis is proposed. Aiming at the problem of gradient disappearance in RNN model, the ability of Gru neural network to deal with long-distance translation is enhanced, and the computational complexity of Gru neural network is reduced. At the same time, a bi-directional Gru model is designed to translate according to the context. For some nonlinear translation, a deep neural network model based on part of speech sequence information is proposed to realize semantic analysis, and experiments are designed to test the translation effect of the neural network model. The simulation results show that the English translation based on deep neural network can improve the translation effect, reduce errors and improve the accuracy of semantic analysis of English translation, which has certain reference significance for improving the level of English translation.