Reviewerly/exHarmony huggingface dataset:
DCU-NET: Self-supervised monocular depth estimation based on densely connected U-shaped convolutional neural networks

Depth estimation is crucial for scene understanding and downstream tasks, especially the self-supervised training methods showing great potential. The overall structure and local details of the scene are essential for improving the quality of depth estimation. The proposal of Monodepth2 has led to significant progress in self-supervised monocular depth estimation. However, Monodepth2 uses the most basic encoder–decoder architecture. The limited data flow information of the network leads to a large semantic gap between the encoder and the decoder, which reduces the accuracy of the network for fine-grained feature recognition. Monodepth2 adopts Resnet18 pre-trained on the Imagenet dataset as the encoder. This traditional convolutional pooling structure results in a loss of pixel information in the network at every scale. In order to solve this problem, this paper proposes an improved DepthNet. The network adopts Hrnet in semantic segmentation as the base encoder, which adopts an advanced multi-scale fusion method in the whole process, thus avoiding the loss of pixel information. An additional densely connected U-Net is employed at the decoder side to provide more information flow. Furthermore, the semantic gap between the encoder and decoder is reduced by adding different numbers of residual connections and channel attention on each layer. The network structure can be regarded as a collection of fully convolutional networks. Since the deep features of the network have a higher correlation with the vertical position, we add a spatial location attention module to the deep-level network to reduce this semantic gap. The approach performs significantly well on the KITTI dataset benchmark, with several performance criteria comparable to supervised monocular depth inference methods.
IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s

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Adaptive inverse perspective mapping transformation method for ballasted railway based on differential edge detection and improved perspective mapping model

Inverse perspective mapping (IPM) can eliminate the perspective effect of the image, which is mainly used for rail-lane detection and foreign body intrusion detection in rail transportation. Most of the IPM algorithms set the IPM parameters in advance according to the intrinsic and extrinsic parameters of the camera system, which has the problem of poor generality. In order to solve the problem of existing algorithms, an adaptive IPM method suitable for ballasted rail is proposed. This method has two parts. The first part is to obtain a trapezoidal calibration region surrounded by rails and sleepers; The second part is through the improved perspective mapping model, the trapezoidal calibration region is solved to perform the adaptive IPM. Finally, we conducted two experiments. In calibration experiments, the error range of adaptive IPM is between 0.1% and 5.3%. In outdoor field experiments, the error of the camera's horizontal field of view, vertical field of view, and installation height obtained by the adaptive IPM is about 10%. Experiments show that adaptive IPM is better than other methods when only the camera pitch angle parameter is needed, and it has generality on different cameras and adaptability in different scenarios. This research has a certain reference value for future railway rail inspection and foreign body intrusion detection.
Hardware optimization for photonic time-delay reservoir computer dynamics

Abstract Reservoir computing (RC) is one kind of neuromorphic computing mainly applied to process sequential data such as time-dependent signals. In this paper, the bifurcation diagram of a photonic time-delay RC system is thoroughly studied, and a method of bifurcation dynamics guided hardware hyperparameter optimization is presented. The time-evolution equation expressed by the photonic hardware parameters is established while the intrinsic dynamics of the photonic RC system is quantitively studied. Bifurcation dynamics based hyperparameter optimization offers a simple yet effective approach in hardware setting optimization that aims to reduce the complexity and time in hardware adjustment. Three benchmark tasks, nonlinear channel equalization (NCE), nonlinear auto regressive moving average with 10th order time lag (NARMA10) and Santa Fe laser time-series prediction tasks are implemented on the photonic delay-line RC using bifurcation dynamics guided hardware optimization. The experimental results of these benchmark tasks achieved overall good agreement with the simulated bifurcation dynamics modeling results.
A Lexicon and Depth-Wise Separable Convolution Based Handwritten Text Recognition System

Cursive handwritten text recognition is a challenging research problem in pattern recognition. The current state-of-the-art approaches include models based on convolutional recurrent neural networks and multi-dimensional long short-term memory recurrent neural network techniques. These methods are highly computationally extensive as well model is complex at the design level. In recent studies, a combination of convolutional neural networks and gated convolutional neural networks based models demonstrated less number of parameters in comparison to convolutional recurrent neural networks based models. In the direction to reduced the total number of parameters to be trained, in this work, we have used depthwise separable convolution in place of standard convolutions with a combination of gated-convolutional neural network and bidirectional gated recurrent unit to reduce the total number of parameters to be trained. Additionally, we have also included a lexicon-based word beam search decoder at the testing step. It also helps in improving the overall accuracy of the model. We have obtained 3.84% character error rate and 9.40% word error rate on IAM dataset, 3.15% character error rate and 11.8% word error rate on RIMES dataset and 4.88% character error rate and 14.56% word error rate in George Washington dataset respectively.
DCR-ReID: Deep Component Reconstruction for Cloth-Changing Person Re-Identification

Person re-identification (Re-ID) plays an important role in many areas such as robotics, multimedia and forensics. However, it becomes difficult when considering long-term scenarios, due to changing clothes irregularly for people. Therefore, cloth-changing person re-identification (CC-ReID) has attracted more attention recently. CC-ReID aims to identify the same person but with different clothes. Its main challenge is how to disentangle clothes-irrelevant features, such as face, shape, body, etc. Most existing methods force the model to learn clothes-irrelevant features by changing the colour of clothes or reconstructing people dressed in different colours. However, due to the lack of the ground truth for supervision, these methods inevitably introduce noises which spoil the discriminativeness of features and lead to uncontrollable disentanglement. In this paper, we propose a novel disentanglement framework, called Deep Component Reconstruction Re-ID (DCR-ReID), which can disentangle the clothes-irrelevant features and the clothes-relevant features in a controllable manner. Specifically, we propose a Component Reconstruction Disentanglement (CRD) module to disentangle the clothes-irrelevant features and the clothes-relevant features based on the reconstruction of human component regions. In addition, we propose a Deep Assembled Disentanglement (DAD) module, which further improves the discriminativeness of these disentangled features. Extensive experiments on three real-world benchmark CC-ReID datasets, LTCC, PRCC, and CCVID, are conducted to demonstrate the effectiveness of the proposed DCR-ReID. Empirical studies show that our DCR-ReID achieves the state-of-the-art performance against the other CC-ReID methods. The source code of this paper is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PKU-ICST-MIPL/DCR-ReID_TCSVT2023</uri> .
Automatic SignWriting Recognition: Combining Machine Learning and Expert Knowledge to Solve a Novel Problem

Sign languages are viso-gestual languages, using space and movement to convey meaning. To be able to transcribe them, SignWriting uses an iconic system of symbols meaningfully arranged in the page. This two-dimensional system, however, is very different to traditional writing systems, so its automatic processing poses a novel challenge for computational linguistics. In this article, we present a novel problem for the state of the art in artificial intelligence: automatic SignWriting recognition. We examine the problem, model the underlying data domain, and present a first solution in the form of an expert system that exploits the domain knowledge encoded in the data modelization. This system uses an adaptable pipeline of neural networks and deterministic processing, overcoming the challenges posed by the novelty and originality of the problem. Thanks to our data modelization, it improves the accuracy compared to a straight-forward deep learning approach by 17%. All of our data and code are publicly available, and our approach may be useful not only for SignWriting processing but also for other similar graphical data.
AFD-Former: A Hybrid Transformer With Asymmetric Flow Division for Synthesized View Quality Enhancement

Recently, CNN-based post-processing has shown great potential in Synthesized View Quality Enhancement (SVQE). However, due to the limited receptive field of convolution, it is ineffective in explicitly modeling long-range dependencies, which are critical to eliminate the distortion induced by Depth Image Based Rendering (DIBR) in synthesized views. Although transformers exhibit tremendous success at learning global contextual information, it is weak at extracting local texture information. To take full advantages of the CNN and transformer, we present a novel U-shaped hybrid transformer with asymmetric flow division to collaboratively capture global-local information for SVQE, termed as AFD-former. Specifically, the AFD-former utilizes the Transformer-CNN Block (TCB) as encoder and decoder, in which several Dynamic Hybrid Attention Blocks (DHABs) are designed to simultaneously model long-range interactions and retain texture details. Then, considering that the deeper layers of the U-shaped network play more roles in capturing global information while shallow layers more in extracting local information, an Asymmetric Flow Division Unit (AFDU) is embedded into each DHAB to assign different contributions of global-local contextual information to the transformer and CNN branches across different layers. Finally, a dynamic learnable modulator is incorporated into two branches to help model effectively feature representation learning. That can be viewed as the dynamic process of adjusting the weight for each channel of the input feature based on contextual cues. Extensive experiments demonstrate that the proposed AFD-former can significantly enhance perceptual quality of synthesized views with similar SVQE speed compared with the related state-of-the-art SVQE methods. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/House-yuyu/AFD-former</uri> .
A robust areal residual-restrained variational mode decomposition for filtering on surface texture analysis

Abstract This study proposes a novel filter, namely areal RrVMD based on Variational Mode Decomposition (VMD), for decomposing surface areal texture into the form, waviness and roughness. VMD is one of the latest signal decomposition techniques and has been introduced into the field of surface metrology recently. The paper develops a residual-restrained method to further improved the VMD algorithm. It consists of three processing steps: firstly, calculating the robust weight function; secondly, decomposing the surface into the corresponding k modes and a residual by using the devised areal residual-restrained VMD; thirdly, identifying different surface topography features by different wavelengths of modes. This study also proposes a robust algorithm to handle outliers and defects on the measured surface. The experimental results demonstrate that the robust areal residual-restrained VMD can precisely separate form, waviness and roughness and eliminate outliers efficiently.
Cross-Scale KNN Image Transformer for Image Restoration

Numerous image restoration approaches have been proposed based on attention mechanism, achieving superior performance to convolutional neural networks (CNNs) based counterparts.However, they do not leverage the attention model in a form fully suited to the image restoration tasks.In this paper, we propose an image restoration network with a novel attention mechanism, called cross-scale k-NN image Transformer (CS-KiT), that effectively considers several factors such as locality, non-locality, and cross-scale aggregation, which are essential to the image restoration.To achieve locality and nonlocality, the CS-KiT builds k-nearest neighbor relation of local patches and aggregates similar patches through a local attention.To induce cross-scale aggregation, we ensure that each local patch embraces different scale information with scale-aware patch embedding (SPE) which predicts an input patch scale through a combination of multi-scale convolution branches.We show the effectiveness of the CS-KiT with experimental results, outperforming state-of-the-art restoration approaches on image denoising, deblurring, and deraining benchmarks.
Semantic-Aware Face Deblurring With Pixel-Wise Projection Discriminator

Most recent face deblurring methods have leveraged the distribution modeling ability of generative adversarial networks (GANs) to impose a constraint that the deblurred image should follow the distribution of sharp ground-truth images. However, generating sharp face images with high fidelity and realistic properties from a blurry face image remains challenging under the GAN framework. To this end, we focus on modeling the joint distribution of sharp face images and segmentation label maps for face image deblurring in a GAN framework. We propose a semantic-aware pixel-wise projection (SAPP) discriminator that models pixel-label matching with semantic label map information and generates a pixel-wise probability map of realness for the input image as well as a per-image probability. Moreover, we introduce a prediction-weighted (PW) loss to focus on erroneous pixels in the output of the decoder, using per-pixel real/fake probability map to re-weight the contribution of each pixel in the decoder. Furthermore, we present a coarse-to-fine training technique for the generator, which encourages the generator to focus on global consistency in the early training stages and local details in the later stages. Extensive experimental results show that our method outperforms existing methods both quantitatively and qualitatively in terms of perceptual image quality.
Empirical Study: How Issue Classification Influences Software Defect Prediction

Software defect prediction aims to identify potentially defective software modules to better allocate limited quality assurance resources. Practitioners often do this by utilizing supervised models trained using historical data. This data is gathered by mining version control and issue tracking systems. Version control commits are linked to issues they address. If the linked issue is classified as a bug report, the change is considered as bug fixing. The problem arises from the fact that issues are often incorrectly classified within issue tracking systems. This introduces noise into the gathered datasets. In this paper, we investigate the influence issue classification has on software defect prediction dataset quality and resulting model performance. To do this, we mine data from 7 popular open-source repositories, create issue classification and software defect prediction datasets for each of them. We investigate issue classification using four different methods; a simple keyword heuristic, an improved keyword heuristic, the FastText model and the RoBERTa model. Our results show that using the RoBERTa model for issue classification produces the best software defect prediction datasets, containing on average 14.3641% of mislabeled instances. SDP models trained on such datasets achieve superior performance, to those trained on SDP datasets created using other issue classification methods, in 65 out of 84 experiments, with 55 of them being statistically relevant. Furthermore, in 17 out of 28 experiments we could not show a statistically relevant performance difference between SDP models trained on RoBERTa derived software defect prediction datasets and those created using manually labeled issues.
A short review on the application of blockchain technology in petroleum industry

The blockchain technology has been in the limelight with the emergence of bitcoin and since then it is attracting huge interest and investment across industries. The blockchain technology is a decentralized and a distributed-leger technology in which all the information or transactions are digitally stored in a secure environment across different locations and people. The petroleum industry is not far behind in adopting the blockchain technology due to its cost benefits, security, and transparency it brings to the whole system. Various projects successfully implemented in the petroleum industry based on the blockchain technology are trading platform, smart contract management, royalty transactions, etc. The implementation of blockchain technology in petroleum industry is still at a nascent stage but it has the potential to completely transform and disrupt the petroleum industry. This paper review the characteristic features, merits and demerits of different types of blockchain, the design principle of any blockchain technology, potential application and various use cases of blockchain technology in petroleum industry.
Binocular camera calibration and stereo correction based on Halcon calibration method

With the proportion of artificial intelligence becoming more important, binocular vision technology is more widely used in the field of machine vision. An important step that must be experienced is binocular camera calibration. The accuracy of binocular calibration will also directly affect the accuracy of subsequent work. In view of the disadvantages of low accuracy and poor robustness, Halcon calibration uses binocular camera calibration and stereo correction. This method fully considers the influence of camera distortion on calibration, and determines the conversion relationship between each coordinate system. The internal and external parameters of the camera were obtained and validated by image-stereo correction experiments. The results show that the calibration method is highly accurate and can be widely used in the binocular vision field.
Detection and classification of galaxy morphology based on YOLOv5

Detection and morphological classification of galaxies are important steps to study the formation, structure and evolution of galaxies. However, with the development of large-scale sky surveys such as dark Energy Spectral Survey (DESI), the image data of galaxies are increasing rapidly, and the problem of low classification efficiency caused by excessive image data is difficult to be solved by traditional classification methods. Machine learning algorithms can help astronomers efficiently and automatically detect and classify different galaxies on large astronomical datasets. Therefore, combining with the current popular YOLO visual detection model, this paper proposes a galaxy shape detection model based on YOLOv5.In this paper, affine transformation is firstly carried out on the image, then bilateral filtering and sharpening are used to enhance the data, and for small target detection, the method of reducing the convolution step is adopted. Finally, the YOLOv5 model was used to detect and classify spiral galaxies, elliptical galaxies, lensed galaxies (bar galaxies) and stars in the galaxy images. The mAP@0.5 of the model reached 87.63%, which could accurately detect the positions of different galaxies and classify them.
Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach

Low resolution object detection could be challenging. In this paper, we proposed a GAN-based real-time data augmentation algorithm for the transfer learning task of UAV vehicle detection from ImageNet, with improvements including using FocalLoss to replace the cross-entropy loss commonly used in the industry, as well as redesigning the target detection Head combination to improve the model's detection accuracy by 4% over original YOLOv5 model. We make it feasible for deployments on UAV-carried ARM systems.
Solving evolutionary problems using recurrent neural networks

A technique for flexible control of induction baking of electrically non-conductive layers (paints, varnishes, resins, etc.) is presented, based on the temperature prediction. As the numerical solution of the full model of the process takes a long time, it is necessary to approximate it with a suitable equivalent model. In this case, recurrent neural networks (RNNs) prove to be a powerful tool for solving the task practically online and providing the input data to control the field current fast enough. The methodology was first tested to predict the current based on the knowledge of the voltage, which can be determined from the analytical solution of the ordinary differential equation that describes the feeding circuit. Subsequently, the methodology was implemented on a system for baking non-conductive layers.
Immersive role-playing with avatars leads to adoption of others’ personalities

In modern society, where nations and even individuals are divided, building bridges between people of different personalities and backgrounds is essential to achieve harmonious coexistence. In recent years, research has been conducted on the use of technologies to bridge this gap. In this study, the effectiveness of using immersive virtual reality (IVR) technology to play the role of a stranger in helping people learn about and empathize with others was investigated. Specifically, participants were asked to role-play a first-time stranger in an IVR environment after being given prior information about the stranger via a preparatory video. The effects of role-playing between acting as a target stranger through his or her avatar in an immersive environment, acting through a different avatar in an immersive environment, and acting through his or her avatars in a non-immersive interface were compared. The results showed that using IVR technology with an exact avatar to play the target person was found to have the greatest effect on the participants’ personalities and thoughts as well as increasing their empathy for that individual. This finding suggests that IVR technology may be an effective tool for bridging the gap between people from different backgrounds.