Reviewerly/exHarmony huggingface dataset:
Global-Local Generation Adversarial Learning Based Low-Light Image Enhancement

In the field of low-light image enhancement, the existing unsupervised methods still have some problems, such as the lack of authenticity and the unclear effect of image enhancement in extremely dark conditions. To address this issue, we propose a highly-effective unsupervised approach to directly recover normal light image from low-light image by designing an effective cyclic generation adversarial network, Firstly, in order to solve the problem of insufficient memory caused by large-size input, a global and local generator has been designed. Secondly, we develop an adaptive two-stage discriminator. Among it, by discriminating the whole image, the local region that needs to be discriminated again is obtained adaptively. Finally, the loss in frequency domain is employed to avoid image distortion. We employ the focal frequency loss which allows the model to adaptively focus on frequency components that are hard to synthesize. The PI index of the method in this paper reaches on NPE, LIME, MEF, DICM and VV datasets respectively 2.81, 2.78, 2.40, 3.15, 3.69. On LOL datasets, PSNR and SSIM index reached 19.89 dB, 0.782 3, with good robustness.
2D Medical Image Segmentation Combining Multi-Scale Channel Attention and Boundary Enhancement

2D medical image segmentation plays an important role in disease diagnosis and computer-assisted treatment. Aiming at the problem that 2D medical image is difficult to accurately segment due to target size, shape and blurred boundaries, a method combining multi-scale channel attention and boundary enhancement is proposed. First, 2D medical image is received as input, and the encoder and boundary enhancement module are used to extract the high-level feature maps and boundary segmentation result respectively. Then the multi-scale channel attention module extracts context information of different scales from the high-level feature map, enhances the useful features and suppresses the useless feature response. Finally, the context information obtained in the previous step is passed into the decoder to obtain the region segmentation result, and integrated with the boundary segmentation result to obtain the final segmentation result. In order to resolve the problem of data imbalance in medical images, a custom loss function is proposed. Experimental results on the four datasets of tooth panoramas containing residual roots, tooth panoramas containing caries, retinal vessels, and skin lesions show that the segmentation precision of proposed method has reached 85.63%, 70.15%, 75.86% and 85.92%, respectively. Compared with other medical image segmentation methods, proposed method performs better.
FP-Net: Frontal Pose Estimation Based on Single Human Body Image withArbitrary View

Accurate extraction of the frontal pose of the human body can assist behavior recognition, image generation, and virtually try-on work. However, side images and back images have problems, such as self-occlusion of the human body and invisible key points, which make the extraction of the frontal pose very difficult. Therefore, a frontal pose estimation network FP-Net (frontal pose network) based on single human body image at arbitrary view is designed and implemented. First, a multi-view human body image data set is produced to provide data support for model design. Second, in order to improve the accuracy of model prediction results, a regression module based on Anchor pose and a feature fusion module based on 3D pose are designed. Finally, FP-Net realizes the frontal pose extraction of human body images from arbitrary view. The PCK evaluation index is used for ablation experiments on the BJUT Taichi and CMU Panoptic datasets. The results show that proposed method effectively improves the accuracy of the frontal pose estimation of the human body.
Knowledge Distillation Anomaly Detection with Multi-Scale Feature Fusion

To enhance the generalization of anomaly detection, this paper proposes a multi-scale detection method based on knowledge distillation. During training, the well-pretrained teacher network is used to teach the student network to learn the feature of normal samples. During testing, the teacher network can still represent anomaly well due to its strong generalization, while the student network cannot. The difference between them makes the detection task available. Furthermore, a mid-level feature pyramid structure is adopted to enhance the ability for handling the anomaly with different size, and a feature reconstruction modular is also employed to enlarge the difference between teacher and student network for an anomaly. The method achieves 97.8% and 97.7% AUC score on pixel and image level respectively, evaluated on the public benchmark-MVTecAD.
Nasal-Skull Base Fast Path Extraction and Automatic Navigation

In the nasal-skull base virtual endoscope, the existing navigation path extraction algorithms have low operation efficiency, and automatic navigation algorithm is easy to cause violent rotation of image. Therefore, a center path extraction algorithm based on navigation area pre-planning and an automatic navigation algorithm based on adaptive speed and rotation are proposed. The former improves the distance transformation method. The navigation area is pre-planned based on the fast expanding random tree and calculate the boundary distance field map, and construct the maximum cost tree to extract the center path. The latter improves the DoTween method. The camera moving speed is adjusted according to the length of the shortest line segment in the path, and the predicted steering distance is adjusted according to the cavity diameter at the vertex in the path. Experiments are conducted with nasal models from DICOM. The results show that the path extraction speed of the center path extraction algorithm based on navigation area pre-planning method is about 49 times faster than the distance transformation method, and the automatic navigation algorithm of adaptive speed and rotation is more natural at the vertices comparing with methods such as DoTween.
MDataEE: Analysis and Visualization of Multifactor Time Series Data

The visualization of multifactor time series data and abnormal data is of great significance in improving the decision-making efficiency and other problems. Since different types of data have different characteristics, traditional visualization methods will face the challenges of complex graphics and low user observation efficiency when drawing such data. Therefore, an efficient visualization method MDataEE for exploring multifactor time series data and abnormal data is proposed. Firstly, the view of multiple kinds of data is simplified by visual mapping. Secondly, the rendering of coordinate axes is optimized according to the density and importance of data and visual perception. Finally, we added some interactive operations, such as displaying and hiding graphics and generating contrast views, so that users could explore different aspects of data freely according to their needs. The real-world PM<sub>2.5</sub> data set is used for the experimental tests in this paper. The results show that the proposed method can generate a concise visual view, which has advantages in analyzing the trend and causes of abnormal data, and can improve the efficiency in understanding and analyzing abnormal multifactor time series data.
Deep Neural Network Regression of MANO from a Single Image

In order to improve the precision and efficiency of estimating the pose and shape of the 3D human hand from a monocular RGB image and accelerate 3D reconstruction, combining the efficiency of deep neural networks and accuracy of traditional optimization, DeepMANO is proposed. First, a DNN model is used to extract sparse features from image, then using these features to regress MANO model parameters. The regressed parameters are then used to initialize the iterative optimization routine which fits MANO to 3D joints. Finally, the parameters obtained by the routine are employed to supervise the network. Two large hand motion datasets, FreiHand and ObMan, are utilized to train the network. The approach assumes a weak perspective model. Experimental results show that the hand pose error is reduced by 52% and mesh error is reduced by 59% compared with state-of-the-art (SOTA) methods. The running time is about three times faster than SOTA methods.
Multi-Conditional Generative Adversarial Network for Text-to-Video Synthesis

Aiming at the problem that the current mainstream models have strong randomness in the text-to-video process and lack the ability to synthesize complex scenes and diverse motion videos, a text-to-video method based on multi-condition generative adversarial networks is proposed, including text processing modules, pose modeling and transition module, video frame generation and optimization module. The text processing module combines traditional generation methods (retrieval and supervised learning methods) with generative models to establish an action retrieval database to improve the controllability of the generation process, the pose modeling and transition module realizes the extraction of pose information and 3D modeling, the video frame generation and optimization module uses multi-condition generative adversarial networks to synthesize and optimize video frames. The model is verified by public datasets such as iPER and DeepFashion, and evaluated by IS, SSIM, PSNR and other indicators. Compared with the existing models, the semantic consistency and video quality of the video synthesis by the proposed model have advantages. Compared with the current mainstream posture transformation model such as MonkeyNet, the SSIM on the iPER dataset has increased by 16.8%, the IS has increased by 22.7%, and the PSNR value has increased by 27.1%. In terms of evaluating posture transition, in the baseline dataset DeepFashion, the FreID value increased by 26.7%.
On the Exponential Diophantine Equation 2x+15y=z2

In this article, we solve the exponential Diophantine equation 2 2 15 x y + = z where x y, and z are non-negative integers. The basic theorems in Number theory are given and applied to find all solutions. The result reveals that there are only three solutions to the equation.
Evaluation of a Digital Image Analyzer UIMD PBIA for Determining White Blood Cell Differential Count

slide review for the validation of the CBC test results is inevitable.The Clinical and Laboratory Standards Institute (CLSI) guideline requires a manual differential count of 200 cells performed by two experienced diagnostic hematologists [1].This job is laborious and quality control is dif cult to apply.Automated digital image analyzers have been developed to address these dif culties [2].Since the development of the rst image analysis system in 1966, remarkable progress has been made regarding its processing speed and convenience [2, 3].The automated image analyzer was produced by Cellavision (CellaVision AB, Lund, Sweden) in the early 2000s.This system is integrated into the DI-60 platform (Sysmex, Kobe, Japan), which is equipped with a hematology analyzer and slide-making devices.The performance of the Cellavision and DI-60 has been thoroughly evaluated in recent studies [3-10].Prior studies revealed suboptimal accuracy in the identication of abnormal cells, especially blasts and immature granulo-
Beyond Benchmarks: Spotting Key Topical Sentences While Improving Automated Essay Scoring Performance with Topic-Aware BERT

Automated Essay Scoring (AES) automatically allocates scores to essays at scale and may help teachers reduce the heavy burden during grading activities. Recently, researchers have deployed neural-based AES approaches to improve upon the state-of-the-art AES performance. These neural-based AES methods mainly take student essays as the sole input and focus on learning the relationship between student essays and essay scores through deep neural networks. However, their only product, the predicted holistic score, is far from providing adequate pedagogical information, such as automated writing evaluation (AWE). In this work, we propose Topic-aware BERT, a new method of learning relations among scores, student essays, as well as topical information in essay instructions. Beyond improving the AES benchmark performance, Topic-aware BERT can automatically retrieve key topical sentences in student essays by probing self-attention maps in intermediate layers. We evaluate the performance of Topic-aware BERT of different variants to (i) perform AES and (ii) retrieve key topical sentences using the open dataset Automated Student Assessment Prize and a manually annotated dataset. Our experiments show that Topic-aware BERT achieves a strong AES performance compared with the previous best neural-based AES methods and demonstrates effectiveness in identifying key topical sentences in argumentative essays.
Correlations Between Positive or Negative Utterances and Basic Acoustic Features of Voice: a Preliminary Analysis

The major aim of this paper is to establish possible correlations between continuous sentiment scores and four basic acoustic characteristics of voice. In order to achieve this objective, the text of “A Christmas Carol” by Charles Dickens was tokenized at the sentence level. Next, each of the resulting text units was assessed in terms of sentiment polarity and aligned with the corresponding fragment in an audiobook. The results indicate weak but statistically significant correlations between sentiment scores and three acoustic features: the mean F0, the standard deviation of F0 and the mean intensity. These findings may be useful in selecting optimal acoustic features for model training in multimodal sentiment analysis. Also, they are essential from a linguistic point of view and could be applied in studies on such language phenomena as irony.
Use of L2 Pronunciation Techniques in and Outside Classes: Students’ Preferences

The present study describes the level of effectiveness of both traditional and computer-assisted second language pronunciation techniques from the students’ perspectives. By traditional techniques we mean those activities which make use of phonetic alphabet, including transcription practice, detailed description of the articulatory systems, drills (e.g. minimal pair drills), reading aloud, tongue twisters, rhymes, etc. (Hismanoglu and Hismanoglu 2010: 985). On the other hand, computer-assisted techniques include activities based on listening and imitating tasks, which use technology, such as self-imitation practice, recordings of L2 learner’s, visual aids, and automatic speech recognition tools. The main aim of this study does not aim to classify L2 pronunciation methods by allocating them to previously mentioned categories but rather attempts to examine the intricate relationship between students’ knowledge, perceptions, attitudes and their most preferable practices which, in their opinion, result in improvement of their L2 pronunciation. 118 study subjects were asked to complete four main questions, within which tasks based on the Likert-scale items gathered data about the students’ most preferable L2 pronunciation teaching and learning techniques. The students were asked to create their own list, starting from the most useful to the least beneficial techniques. The last task was an open-ended question about other techniques than mentioned in the questionnaire. The analysis of the obtained data involved a two-stage process: a) data segmentation; and b) techniques categorisation. The first step was to select pronunciation learning techniques in terms of their frequency and use and to adjust them to the research group. The second stage, techniques categorisation, was based on a careful analysis of the answers given by the students in the questionnaire. Following that, five categories were distinguished: (1) traditional and used only in the classroom, (2) traditional but also used in distance learning, (3) computer-assisted but used only in the classroom, (4) computer-assisted and also used in distance learning, (5) innovative: combining students’ needs and available online.Highlighting the prominence of pronunciation in acquiring communicative competence, the authors propose their own, innovative suggestions for the future creation of teaching materials.
Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections.To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique, an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance, this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected, the proposed method builds and adds a new classifier to the ensemble.To add a new classifier, the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end, a weighting mechanism is used to calculate the weight of each classifier, which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the results were evaluated on the false positive rate, miss detection rate, and accuracy measures.The proposed method is also compared with the state-of-the-art methods, which include DDM, EDDM, and PageHinkley with support vector machine (SVM) and Naïve Bayes classifiers that are frequently used in concept drift detection studies.In all cases, the results show the efficiency of our proposed method.6002
MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images.On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not.Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information.With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built.Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images.The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models.The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios.The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads.Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect.The experiments and the evaluations clearly proved this attempt.
3D Face Reconstruction from a Single Image Using a Combined PCA-LPP燤ethod

In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance.Principal component analysis (PCA) is commonly used to compress images and extract features.One disadvantage of PCA is local feature loss.To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP).However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering.In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques.By contrast, in this study, complex and detailed parts, such as wrinkles around the eyes and mouth, can be reconstructed by composing the topology of the 3D face model as an adjacency graph and extracting local features from the connection relationship between the 3D model vertices.Experiments verified the effectiveness of the proposed method.When the proposed method was applied to the 3D face reconstruction evaluation set, a performance improvement of 10% to 20% was observed compared with the existing PCA-based method.
Image-Based Discount and Barcode Recognition System

Current Point-of-Sale processing is complex and time consuming. In this paper, we propose an image-based discount sticker and barcode “scan” system for automation. Recognition of discount stickers and barcodes is quite a big challenge, as different shooting conditions can result in different appearances. We design a deep learning classifier of various discount rates and barcode basing on YOLACT detection network. We also propose a data augmentation to generate various data that are close to real scene to improve the classification performance of deep learning model. Evaluation with our original data set shows that the proposed approach achieves high performance and is applicable to real-world scenario.
Patch-Based Image Coding with End-To-End Learned Codec using Overlapping

End-to-end learned image and video codecs, based on auto-encoder architecture, adapt naturally to image resolution, thanks to their convolutional aspect. However, while coding high resolution images, these codecs face hardware problems such as memory saturation. This paper proposes a patch-based image coding solution based on an end-to-end learned model, which aims to remedy to the hardware limitation while maintaining the same quality as full resolution image coding. Our methodconsists in coding overlapping patches of the image and reconstruct them into a decoded image using a weighting function. This approach manages to be on par with the performance of full resolution image coding using an end-to-end learned model, and even slightly outperform it, while being adaptable to different memory size. It is also compatible with any learned codec based on a conv/deconvolutional autoencoderarchitecture without having to retrain the model.