Reviewerly/exHarmony huggingface dataset:
An End-to-End Uyghur Speech Recognition Based on Multi-task Learning

With the emergence of deep learning, speech recognition research for languages with a wide speaker base, such as English and Chinese, has become reasonably mature. However, Uyghur speech recognition research has only developed slowly, because the modeling is subpar since Uighur is a low-resource language. To solve the aforementioned problem, we propose an end-to-end Uyghur speech recognition model based on multi-task learning with Branchformer. Branchformer can capture both global and local contexts, allowing it to learn richer features from low volumes of data to improve model performance in resource-constrained situations. The multi-task learning model can fully utilize the data through multiple related tasks, enhancing the model’s generalizability under low resources. In this paper, the multi-task learning model is trained and decoded using modeling units including phoneme, sub-word, and word. The performance of the model is then evaluated using various datasets. The results show that the proposed model outperforms the mainstream models in terms of recognition, in the self-built database and open-source corpus Thuyg-20, respectively, the word accuracy of the proposed model reaches 94.48% and 91.16%, which is a significant improvement compared with each benchmark model.
Knowledge Concept Recommendation Model for MOOCs with Local Sub-graph Embedding

Massive open online courses (MOOCs) play a crucial role in modern education by providing users with largescale open online learning platforms. Substantial research has been conducted to reduce user learning blindness and improve user experience, particularly in personalized course recommendations based on graph neural networks. However, these efforts have focused primarily on fixed or homogeneous graphs, are vulnerable to data sparsity problems, and are difficult to scale. This study proposes to overcome this limitation using graph convolution on local subgraphs combined with an extended matrix factorization model. First, the proposed method decomposes the heterogeneous graph into multiple meta-path-based subgraphs and combines random wandering sampling methods to capture complex semantic relationships between entities while sampling nodes' influence-rich neighborhoods. Next, the attention mechanism adaptively fuses the contextual information of different subgraphs for a more comprehensive construction of user preferences. Experiments on publicly available MOOCs datasets reveal that the proposed model outperforms other benchmark models and is highly scalable while alleviating the data sparsity problem.
Underwater Image Enhancement Method Based on Dark Channel Prior and Guided Filtering

This paper presents a comprehensive enhancement method based upon Underwater Dark Channel Prior (UDCP) and Guided Filtering for standard RGB underwater images without depth information. Firstly, color compensation and Gray World Algorithm are used to correct the color of images obtained underwater. After that, the restored image is dehazed by using the optimized dehazing algorithm created on UDCP. The dehazing algorithm proposed in this study is obtained by reconstructing the ambient light transmittance expression in UDCP. It effectively avoids the “excessive dehazing” caused by traditional dehazing algorithms, and it can also optimize the depth of field of dehazed images. At the same time, due to the complexity of underwater dark channel image dehazing, the dehazed image will still produce fuzzy white areas in zones with large pixel color difference changes (such as the boundary of objects). Therefore, our method adds an image fusion approach built upon guided filtering to optimize the dehazed image to eliminate the white areas to enhance the image clarity further. At last, this paper compares the image enhancement effect of our method with that of other four methods such as Unified Generative Adversarial Networks (UGAN) by using five objective image evaluation indexes such as Underwater Color Image Quality Evaluation (UCIQE).
Lightweight Conv-Swin Transformer for Wildlife Detection

Wildlife detection is of great significance for wildlife monitoring and protection. Among existing object detection methods, Faster RCNN is a typical two-stage object detection method. In despite of its effectiveness, it suffers from the less satisfactory detection accuracy. This is mainly limited by the insufficient global representation of both objects and scenes. To this end, this paper proposes a lightweight Conv-Swin Transformer method for wildlife detection involving a lightweight combination of both convolution and Swin Transformer. In this study, Lightweight improvements are made in two main ways. The first one is done by reducing the number of Blocks in the third stage of the Swin Transformer; the second one is done by optimizing the down-sampling of different stages in the Swin Transformer network through the convolutional structure, which can speed up the detection of the model and improve the detection efficiency. The Faster RCNN model was chosen for experiments on a self-constructed wildlife dataset, using three different CNNs as well as the Swin Transformer as the backbone network for comparison. Experimental results show that the improved Conv-Swin Transformer, which combines the advantages of the attention mechanism and the convolutional structure, improves detection speed by 17.5% with a slight reduction in detection accuracy.
Autonomous Navigation of UAV in Dynamic Unstructured Environments via Hierarchical Reinforcement Learning

Autonomous navigation of unmanned aerial vehicle (UAV) is one of the fundamental yet completely solved problems in automatic control. In this paper, an option-based hierarchical reinforcement learning approach is proposed for UAV autonomous navigation. Specifically, the proposed method consists of a high-level and two low-level model, where the high level behavior selection model learns a stable and reliable behavior selection strategy automatically, while the low-level obstacle avoidance model and target-driven control model implement two behavior strategies, obstacle avoidance and target approach, respectively, thus avoiding the dependence on manually designed control rules. Furthermore, the proposed model is pre-trained on large public dataset, allowing the model to converge quickly in various complex unstructured flight environments. Extensive experiments show that the proposed method indicates an overall advantage in various evaluation metrics, which indicating that the proposed method has a strong generalization capability in autonomous navigation task of UAV.
YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded Badminton Robots

Employing robots in badminton training contributes to a more accurate analysis of an athlete's movements and helps avoid injuries. Shuttlecock detection during the flying stage is a critical component of the badminton robot design. However, previous shuttlecock localization methods were unable to detect shuttlecock quickly and accurately in embedded device-based badminton robots, given scale variations, few extractable features, occlusion, and device limitation. In this paper, a deep learning-based shuttlecock localization method is proposed. First, an indoor shuttlecock dataset including 9548 shuttlecock images of various angles and scenes was constructed. Then a shuttlecock detection method YOLO-BTM is proposed, which is based on YOLOv4-Tiny. We proposed a new convolution block to replace the cross-stage partially block in the backbone, to improve the detection speed. To improve the network's ability to detect small objects, the efficient channel attention block is introduced in feature fusion. Finally, a comparative experiment on the accuracy of the method and the detection speed was conducted. The results show that the proposed YOLO-BTM has better performance in detection speed and accuracy compared to the existing state-of-the-art object detection methods on our own shuttlecock dataset. Our method enables real-time, accurate localization of shuttlecock and has the potential to be used in other embedded device based sports robots.
Natural Language Understanding for Chinese-based Service Robots

Service robots have been used in a large number of practical applications, and in recent years there has been an increasing amount of research work on human-robot interaction. In the field of human-robot interaction, the problem of natural language understanding for robots is crucial, and there is very little research on natural language understanding for robots in Chinese. In this paper, we propose a framework for understanding the Chinese language-based robot navigation commands and verify their effectiveness.
UAV Reconnaissance Task Allocation with Reinforcement Learning and Genetic Algorithm

Unmanned air vehicle (UAV) reconnaissance task allocation is important in a total military combat system. The typical Genetic Algorithm (GA) is a common effective means to deal with the UAV task allocation problem. But when face with a large number of targets, the initial population has a huge influence on the performance of GA algorithms, which leads to instability on the solution accuracy. To overcome this limitation of heuristics algorithms, we propose a new algorithm combing reinforcement learning (RL) and the GA algorithms, named GA-RL. The RL is used to fast provide an initial population for GA, and then the GA algorithms further optimize this initial population to get the solution. Finally, the numerical simulation tests show that this algorithm can hugely improve the solving accuracy, especially in large tasks allocation problems.
Modified Unscented Kalman Filter for Relative Navition of Space Target Acquisition, Tracking and Control

At present, the trend of improving the mobility of fast acquisition, long-term tracking and high-precision control has been formed in China. The continuous observation ability of non-cooperative targets and good imaging performance in motion are important trends in the development of optical remote sensing technology. Aiming at the high-precision observation and real-time tracking requirements of high orbit space satellites, this paper studies the high-precision relative navigation of space non cooperative targets. In order to solve the problem that the estimation accuracy of Unscented Kalman filter (UKF) decreases when the system visibility is low, a modified Unscented Kalman filter (MUKF) based on the visibility is proposed. This algorithm defines a system visibility characterization method based on the error gain matrix of the filtering process, and proposes the visibility scaling parameter based on this method, The filter gain covariance matrix is adjusted online, so that the algorithm can adjust the weight of state prediction and system observation online according to the observability of the current time. Numerical simulation shows that compared with UKF, the estimation accuracy of MUKF is improved by about 4 times, and MUKF stabilizes faster and has higher accuracy.
There and Back Again: 3D Sign Language Generation from Text Using Back-Translation

We introduce the first method to automatically generate 3D mesh sequences from text, inspired by the challenging problem of Sign Language Production (SLP). The approach only requires simple 2D annotations for training, which can be automatically extracted from video. Rather than incorporating high-definition or motion capture data, we propose back-translation as a powerful paradigm for supervision: By first addressing the arguably simpler problem of translating 2D pose sequences to text, we can leverage this to drive a transformer-based architecture to translate text to 2D poses. These are then used to drive a 3D mesh generator. Our mesh generator Pose2Mesh uses temporal information, to enforce temporal coherence and significantly reduce processing time. The approach is evaluated by generating 2D pose, and 3D mesh sequences in DGS (German Sign Language) from German language sentences. An extensive analysis of the approach and its sub-networks is conducted, reporting BLEU and ROUGE scores, as well as Mean 2D Joint Distance. Our proposed Text2Pose model outperforms the current state-of-the-art in SLP, and we establish the first benchmark for the complex task of text-to-3D-mesh-sequence generation with our Text2Mesh model.
Remolding and Thinking of Network Security System in Intelligent Hospital

Objective: With the era of Internet + continually evolving, in the network security system that stacks traditional equipment, the reason why it is unable to guard against the escalating security threats is that remedy measures are always taken after the event, and network security is immethodical. Methods: through the reconstruction of the network security system, a framework system consisting of six components, namely, the network security capability system, the planning methodology and tool system, the capability building model, the construction and implementation project library, the network security deployment reference architecture, and the network security operation reference architecture, is constructed, which is a process of “establish something new and in place of the old”. Results: a new generation of network security framework system based on endogenous security is formed, which made network security move from the traditional “dispersedly and partly putting out a fire inside the walls” to “deep integration and endogenous systematization”. Conclusion: It is important to establish ubiquitous network security immunity in the intelligent hospitals system, and truly realize endogenous network security.
A Text Detection and Recognition Method Based on PSENet and CRNN

Text detection refers to detecting, for a given image, the coordinates of the text regions within it, and text recognition refers to identifying specific characters from the detected text boxes and optimizing the recognition results. In this paper, a text detection and recognition method based on PSENet and CRNN is designed for book name recognition in conjunction with the application scenario of automatic book return in self-service libraries. The PSENet text detection algorithm is used to obtain the text box of the book cover, and the CRNN+CTC text recognition algorithm is used to perform text recognition on the name of the book cover, achieving a better recognition effect.
Research on Camouflage Object Detection Based on Improved YOLOv5s

The camouflaged objects have a high internal similarity with the surrounding environment, which is more challenging than the traditional object detection task. There are problems such as low detection accuracy, high missed detection rate and slow detection speed. Aiming at the above problems, the data augmentation processing of the public dataset COD10k is carried out. In YOLOv5s network structure, attention mechanism is added. Through the combination of data augmentation and improved YOLOv5s algorithm, the final mean Average Precision value reached 85.65 %, Recall value reached 87.63 %. The experimental results show that the proposed improvement has higher accuracy and robustness in the Camouflage Object Detection.
Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading

We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene. Rendering objects using an illumination model of the scene doesn't work, because doing so requires a geometric and material model of the object, which is hard to recover from a single image. In this paper, we introduce a method that corrects shading inconsistencies of the inserted object without requiring a geometric and physical model or an environment map. Our method uses a deep image prior (DIP), trained to produce reshaded renderings of inserted objects via consistent image decomposition inferential losses. The resulting image from DIP aims to have (a) an albedo similar to the cut-and-paste albedo, (b) a similar shading field to that of the target scene, and (c) a shading that is consistent with the cut-and-paste surface normals. The result is a simple procedure that produces convincing shading of the inserted object. We show the efficacy of our method both qualitatively and quantitatively for several objects with complex surface properties and also on a dataset of spherical lampshades for quantitative evaluation. Our method significantly outperforms an Image Harmonization (IH) baseline for all these objects. They also outperform the cut-and-paste and IH baselines in a user study with over 100 users.
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning

We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction. Unlike previous time-contrastive methods for hand pose estimation, our framework considers temporal consistency in its augmentation scheme, and accounts for the differences of hand poses along the temporal direction. Our data-driven method leverages unlabelled videos and a standard CNN, without relying on synthetic data, pseudo-labels, or specialized architectures. Our approach improves the performance of fully-supervised hand reconstruction methods by 15.9% and 7.6% in PA-V2V on the HO-3D and FreiHAND datasets respectively, thus establishing new state-of-the-art performance. Finally, we demonstrate that our approach produces smoother hand re-constructions through time, and is more robust to heavy occlusions compared to the previous state-of-the-art which we show quantitatively and qualitatively. Our code and models will be available at https://eth-ait.github.io/tempclr.
Distortion-aware Depth Estimation with Gradient Priors from Panoramas of Indoor Scenes

Compared to 2D perspective images, panoramic images capture a larger field-of-view (FOV). Depth estimation from panoramas is an important task for 3D scene understanding and has made significant progress with the development of CNNs. However, existing CNN-based methods still suffer from the Equirectangular Projection (ERP) problem to deal with panoramic distortions (e.g. same receptive fields near the equator and the two poles) and have difficulty generating accurate depth boundaries. In contrast to existing CNN-based methods, in this paper, a novel Transformer-based method is proposed which is able to cope with panoramic distortions and to generate accurate depth boundaries. A Distortion-aware Transformer is designed using a yaw-invariant cycle shift and a distortion-guided partitioning. The aim is to alleviate the distortion effect by enlarging the receptive fields in both horizontal and vertical directions. Then, a Gradient Transformer is proposed to enhance the features around the boundaries. Gradient information is adopted as a boundary prior. Large-scale experimental results show an improvement compared to state-of-the-art methods. Our method also shows strong generalization capabilities. Finally, our method is extended to panorama semantic segmentation.
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction

Continual learning has been extensively studied for classification tasks with methods developed to primarily avoid catastrophic forgetting, a phenomenon where earlier learned concepts are forgotten at the expense of more recent samples. In this work, we present a set of continual 3D object shape reconstruction tasks, including complete 3D shape reconstruction from different input modalities, as well as visible surface (2.5D) reconstruction which, surprisingly demonstrate positive knowledge (backward and forward) transfer when training with solely standard SGD and without additional heuristics. We provide evidence that continuously updated representation learning of single-view 3D shape reconstruction improves the performance on learned and novel categories over time. We provide a novel analysis of knowledge transfer ability by looking at the output distribution shift across sequential learning tasks. Finally, we show that the robustness of these tasks leads to the potential of having a proxy representation learning task for continual classification. The codebase, dataset and pretrained models released with this article can be found at https://github.com/rehg-lab/CLRec
An Image Encryption Scheme Based on 4D Chaotic System and Permutation-diffusion Operations

The security of information transmission is a hot issue in the information industry. Chaotic dynamical systems have been widely applied in the field of image encryption because of its excellent characteristics. A novel image encryption scheme using improved chaotic sequences yielded by one 4D chaotic dynamical system is proposed. Firstly, the discrete sequences generated are improved to make them have better chaotic natures. Secondly, the improved chaotic sequences are used to control the permutation operation of plain-image, which disrupts the pixel positions and reduces the correlation of adjacent pixels. Finally, the permuted image is diffused twice by bitwise XOR operation with the key streams derived from the improved quantized chaotic sequences to get the cipher-image. The detailed performance analysis is carried out, including histogram analysis, information entropy analysis, adjacent pixel correlation analysis, plaintext sensitivity analysis, and so on. Experimental results show that the proposed image encryption scheme has high security and can resist various classical attacks. It is suitable for many application fields, such as confidential video conference, pay TV, military image database, enterprise secret storage, online private album and so on.
Freedom Sentence Paraphrase Method

A good method of natural language generation is not letting the model generate whatever you want, but teaching the model not to generate the things you don't want. For paraphrase generation task, there should be some different words between source sentence and paraphrase sentence. In this paper, we introduce the Freedom Sentence Paraphrase Method (FSPM) that teaching the paraphrase model generate sentence without the prompt word which shouldn't appear in paraphrase sentence. This method train model by suggesting words that in the source sentence but not in the reference paraphrase sentence. Then, we can get diversified paraphrase sentences for other sentences by enumerating different word in the source sentence as the sensitive word. And there must be a best paraphrase sentence in all the results. What's more, this method don't need other conditions or complex model design. Not only the method are very brief, but also it significantly improve quality and diversity for paraphrase generation task on base models.
A Novel Approach for Image Dehazing via Spatial and Channel Feature Fusion

The quality of images collected outdoors is compromised owing to hazy weather, resulting in contrast reduction and degradation. To solve this problem, a new dehazing algorithm that combines spatial and channel feature maps was proposed. The algorithm comprises of two parts: image dehazing and image enhancement. The first part uses the anti-symmetric nature of the Involution operator and the convolution operator to extract the spatial and channel feature maps of the hazy image completely. This part can restore the dehazed image by combining it with a deformed atmospheric scattering model. In the second part, a contrast-limited adaptive histogram equalization algorithm is used to improve the dehazed image. This process can improve the contrast and brightness of an image and enhance its detailed information. The experiment used the structural similarity index measure(SSIM), peak signal-to-noise ratio(PSNR), and other parameters to compare the algorithm with three other common dehazing algorithms to verify the dehazing effect and robustness of the algorithm. The experimental results show that the proposed algorithm improved the SSIM and PSNR by 4.72% and 15.96% respectively.